[
    {
        "text": "Stock Mechanics: a classical approach: New theoretical approaches about forecasting stock markets are proposed. A\nmathematization of the stock market in terms of arithmetical relations is\ngiven, where some simple (non-differential, non-fractal) expressions are also\nsuggested as general stock price formuli in closed forms which are able to\ngenerate a variety of possible price movements in time. A kind of mechanics is\nsubmitted to cover the price movements in terms of classical concepts. Where\nutilizing stock mechanics to grow the portfolios in real markets is also\nproven.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A Fast Algorithm for the Discrete Core/Periphery Bipartitioning Problem: Various methods have been proposed in the literature to determine an optimal\npartitioning of the set of actors in a network into core and periphery subsets.\nHowever, these methods either work only for relatively small input sizes, or do\nnot guarantee an optimal answer. In this paper, we propose a new algorithm to\nsolve this problem. This algorithm is efficient and exact, allowing the optimal\npartitioning for networks of several thousand actors to be computed in under a\nsecond. We also show that the optimal core can be characterized as a set\ncontaining the actors with the highest degrees in the original network.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Fundamentals of spreading processes in single and multilayer complex\n  networks: Spreading processes have been largely studied in the literature, both\nanalytically and by means of large-scale numerical simulations. These processes\nmainly include the propagation of diseases, rumors and information on top of a\ngiven population. In the last two decades, with the advent of modern network\nscience, we have witnessed significant advances in this field of research. Here\nwe review the main theoretical and numerical methods developed for the study of\nspreading processes on complex networked systems. Specifically, we formally\ndefine epidemic processes on single and multilayer networks and discuss in\ndetail the main methods used to perform numerical simulations. Throughout the\nreview, we classify spreading processes (disease and rumor models) into two\nclasses according to the nature of time: (i) continuous-time and (ii) cellular\nautomata approach, where the second one can be further divided into synchronous\nand asynchronous updating schemes. Our revision includes the heterogeneous\nmean-field, the quenched-mean field, and the pair quenched mean field\napproaches, as well as their respective simulation techniques, emphasizing\nsimilarities and differences among the different techniques. The content\npresented here offers a whole suite of methods to study epidemic-like processes\nin complex networks, both for researchers without previous experience in the\nsubject and for experts.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Niche to normality -- an interdisciplinary review of Vehicle-to-Grid: Vehicle-to-Grid (V2G) capabilities, which enable electric vehicles to\ndischarge power from their batteries for external uses, epitomise the coupling\nof the electricity and transport sectors. To thrive at the nexus of these large\nand well-established sectors V2G services must deliver technical, economic and\nsocial values to many stakeholders. In this Review we present a holistic and\ninterdisciplinary examination of V2G services, highlighting the wide range of\npotential benefits as well as the challenges slowing the technology's evolution\nfrom niche trials to mainstream adoption. We find that benefits tend to be\nsiloed by value proposition and stakeholder while the challenges tend to stem\nfrom stacking multiple values and connecting multiple stakeholders.\nConsequently, we identify key areas for future research, industry and policy\nactivities that will accelerate and smoothen the realisation of V2G's potential\nas an essential pillar of clean transport-electricity systems.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Kinetic models for epidemic dynamics in the presence of opinion\n  polarization: Understanding the impact of collective social phenomena in epidemic dynamics\nis a crucial task to effectively contain the disease spread. In this work we\nbuild a mathematical description for assessing the interplay between opinion\npolarization and the evolution of a disease. The proposed kinetic approach\ndescribes the evolution of aggregate quantities characterizing the agents\nbelonging to epidemiologically relevant states, and will show that the spread\nof the disease is closely related to consensus dynamics distribution in which\nopinion polarization may emerge. In the present modelling framework,\nmicroscopic consensus formation dynamics can be linked to macroscopic epidemic\ntrends to trigger the collective adherence to protective measures. We conduct\nnumerical investigations which confirm the ability of the model to describe\ndifferent phenomena related to the spread of an epidemic.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Scaling and Universality in City Space Syntax: between Zipf and Matthew: We report about universality of rank-integration distributions of open spaces\nin city space syntax similar to the famous rank-size distributions of cities\n(Zipf's law). We also demonstrate that the degree of choice an open space\nrepresents for other spaces directly linked to it in a city follows a power law\nstatistic. Universal statistical behavior of space syntax measures uncovers the\nuniversality of the city creation mechanism. We suggest that the observed\nuniversality may help to establish the international definition of a city as a\nspecific land use pattern.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Innovation diffusion equations on correlated scale-free networks: We introduce a heterogeneous network structure into the Bass diffusion model,\nin order to study the diffusion times of innovation or information in networks\nwith a scale-free structure, typical of regions where diffusion is sensitive to\ngeographic and logistic influences (like for instance Alpine regions). We\nconsider both the diffusion peak times of the total population and of the link\nclasses. In the familiar trickle-down processes the adoption curve of the hubs\nis found to anticipate the total adoption in a predictable way. In a major\ndeparture from the standard model, we model a trickle-up process by introducing\nheterogeneous publicity coefficients (which can also be negative for the hubs,\nthus turning them into stiflers) and a stochastic term which represents the\nerratic generation of innovation at the periphery of the network. The results\nconfirm the robustness of the Bass model and expand considerably its range of\napplicability.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Continuous opinion model in small world directed networks: In the compromise model of continuous opinions proposed by Deffuant et al,\nthe states of two agents in a network can start to converge if they are\nneighbors and if their opinions are sufficiently close to each other, below a\ngiven threshold of tolerance $\\epsilon$. In directed networks, if agent i is a\nneighbor of agent j, j need not be a neighbor of i. In Watts-Strogatz networks\nwe performed simulations to find the averaged number of final opinions $<F>$\nand their distribution as a function of $\\epsilon$ and of the network\nstructural disorder. In directed networks $<F>$ exhibits a rich structure,\nbeing larger than in undirected networks for higher values of $\\epsilon$, and\nsmaller for lower values of $\\epsilon$.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The effect of pack formation at the 2005 world orienteering\n  championships: At the 2005 world championships there was considerable discussion and a\nformal protest in the long distance race arising from a perceived advantage\nobtained by some athletes running together. It is shown that a statistical\nmodel presented in previous work [1] is applicable to this event, giving\npredictions of the final times to with 2-3 minutes. Using the model, we show\nthat pack formation was inevitable in this format. The statistical benefit\ngained at the elite level from running with other competitors appears to derive\nboth from increase speed through the terrain, and the elimination of large\nnavigational errors.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Flexible model of network embedding: There has lately been increased interest in describing complex systems not\nmerely as single networks but rather as collections of networks that are\ncoupled to one another. We introduce an analytically tractable model that\nenables one to connect two layers in a multilayer network by controlling the\nlocality of coupling. In particular we introduce a tractable model for\nembedding one network (A) into another (B), focusing on the case where network\nA has many more nodes than network B. In our model, nodes in network A are\nassigned, or embedded, to the nodes in network B using an assignment rule where\nthe extent of node localization is controlled by a single parameter. We start\nby mapping an unassigned `source' node in network A to a randomly chosen\n`target' node in network B. We then assign the neighbors of the source node to\nthe neighborhood of the target node using a random walk starting at the target\nnode and with a per-step stopping probability $q$. By varying the parameter\n$q$, we are able to produce a range of embeddings from local ($q = 1$) to\nglobal ($q \\to 0$). The simplicity of the model allows us to calculate key\nquantities, making it a useful starting point for more realistic models.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Dynamics of Norm Change in the Cultural Evolution of Language: What happens when a new social convention replaces an old one? While the\npossible forces favoring norm change - such as institutions or committed\nactivists - have been identified since a long time, little is known about how a\npopulation adopts a new convention, due to the difficulties of finding\nrepresentative data. Here we address this issue by looking at changes occurred\nto 2,541 orthographic and lexical norms in English and Spanish through the\nanalysis of a large corpora of books published between the years 1800 and 2008.\nWe detect three markedly distinct patterns in the data, depending on whether\nthe behavioral change results from the action of a formal institution, an\ninformal authority or a spontaneous process of unregulated evolution. We\npropose a simple evolutionary model able to capture all the observed behaviors\nand we show that it reproduces quantitatively the empirical data. This work\nidentifies general mechanisms of norm change and we anticipate that it will be\nof interest to researchers investigating the cultural evolution of language\nand, more broadly, human collective behavior.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Finding multiple core-periphery pairs in networks: With a core-periphery structure of networks, core nodes are densely\ninterconnected, peripheral nodes are connected to core nodes to different\nextents, and peripheral nodes are sparsely interconnected. Core-periphery\nstructure composed of a single core and periphery has been identified for\nvarious networks. However, analogous to the observation that many empirical\nnetworks are composed of densely interconnected groups of nodes, i.e.,\ncommunities, a network may be better regarded as a collection of multiple cores\nand peripheries. We propose a scalable algorithm to detect multiple\nnon-overlapping groups of core-periphery structure in a network. We illustrate\nour algorithm using synthesised and empirical networks. For example, we find\ndistinct core-periphery pairs with different political leanings in a network of\npolitical blogs and separation between international and domestic subnetworks\nof airports in some single countries in a world-wide airport network.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Untangling urban data signatures: unsupervised machine learning methods\n  for the detection of urban archetypes at the pedestrian scale: Urban morphological measures applied at a high-resolution of spatial analysis\ncan yield a wealth of data describing characteristics of the urban environment\nin a substantial degree of detail; however, such forms of high-dimensional\nnumeric datasets are not immediately relatable to broader constructs rooted in\nconventional conceptions of urbanism. Data science and machine learning (ML)\nmethods provide an opportunity to explore such forms of complex datasets by\napplying unsupervised ML methods to reduce the dimensionality of the data while\nrecovering latent themes and characteristic patterns which may resonate with\nurbanist discourse more generally.\n  Dimensionality reduction and clustering methods, including Principal\nComponent Analysis (PCA), Variational Autoencoders, and an Autoencoder based\nGaussian Mixture Model, are discussed and demonstrated for purposes of\n`untangling' urban datasets, revealing themes bridging quantitative and\nqualitative descriptions of urbanism. The methods are applied to a dataset for\nGreater London consisting of network centralities, land-use accessibilities,\nmixed-use measures, and density measures. The measures are computed at\npedestrian walking tolerances at a $20m$ network resolution utilising a local\nwindowing-methodology with distances computed directly over the network and\nwith aggregations performed dynamically and with respect to the direction of\napproach, thus preserving the relationships between the variables and retaining\ncontextual precision.\n  Whereas the demonstrated methods hold tremendous potential, their power is\ndifficult to convey or fully exploit using conventional lower-dimensional\nvisualisation methods, thus underscoring a need for subsequent research into\nhow such methods may be coupled to interactive visualisation tools to further\nelucidate the richness of the data and its potential implications.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Fitting In and Breaking Up: A Nonlinear Version of Coevolving Voter\n  Models: We investigate a nonlinear version of coevolving voter models, in which node\nstates and network structure update as a coupled stochastic dynamical process.\nMost prior work on coevolving voter models has focused on linear update rules\nwith fixed and homogeneous rewiring and adopting probabilities. By contrast, in\nour nonlinear version, the probability that a node rewires or adopts is a\nfunction of how well it \"fits in\" within its neighborhood. To explore this\nidea, we incorporate a parameter $\\sigma$ that represents the fraction of\nneighbors of an updating node that share its opinion state. In an update, with\nprobability $\\sigma^q$ (for some nonlinearity parameter $q$), the updating node\nrewires; with complementary probability $1-\\sigma^q$, the updating node adopts\na new opinion state. We study this mechanism using three rewiring schemes:\nafter an updating node deletes a discordant edge, it then either (1)\n\"rewires-to-random\" by choosing a new neighbor in a random process; (2)\n\"rewires-to-same\" by choosing a new neighbor in a random process from nodes\nthat share its state; or (3) \"rewires-to-none\" by not rewiring at all (akin to\n\"unfriending\" on social media). We compare our nonlinear coevolving model to\nseveral existing linear models, and we find in our model that initial network\ntopology plays a larger role in the dynamics and the choice of rewiring\nmechanism plays a smaller role. A particularly interesting feature of our model\nis that, under certain conditions, the opinion state that is held initially by\na minority of the nodes can effectively spread to almost every node in a\nnetwork if the minority nodes view themselves as the majority. In light of this\nobservation, we relate our results to recent work on the majority illusion in\nsocial networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Groupwise information sharing promotes ingroup favoritism in indirect\n  reciprocity: Indirect reciprocity is a mechanism for cooperation in social dilemma\nsituations, in which an individual is motivated to help another to acquire a\ngood reputation and receive help from others afterwards. Ingroup favoritism is\nanother aspect of human cooperation, whereby individuals help members in their\nown group more often than those in other groups. Ingroup favoritism is a puzzle\nfor the theory of cooperation because it is not easily evolutionarily stable.\nIn the context of indirect reciprocity, ingroup favoritism has been shown to be\na consequence of employing a double standard when assigning reputations to\ningroup and outgroup members; e.g., helping an ingroup member is regarded as\ngood, whereas the same action toward an outgroup member is regarded as bad. We\nanalyze a model of indirect reciprocity in which information sharing is\nconducted groupwise. In our model, individuals play social dilemma games within\nand across groups, and the information about their reputations is shared within\neach group. We show that evolutionarily stable ingroup favoritism emerges even\nif all the players use the same reputation assignment rule regardless of group\n(i.e., a single standard). Two reputation assignment rules called simple\nstanding and stern judging yield ingroup favoritism. Stern judging induces much\nstronger ingroup favoritism than does simple standing. Simple standing and\nstern judging are evolutionarily stable against each other when groups\nemploying different assignment rules compete and the number of groups is\nsufficiently large. In addition, we analytically show as a limiting case that\nhomogeneous populations of reciprocators that use reputations are unstable when\nindividuals independently infer reputations of individuals, which is consistent\nwith previously reported numerical results.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Epidemic spread in interconnected directed networks: In the real world, many complex systems interact with other systems. In\naddition, the intra- or inter-systems for the spread of information about\ninfectious diseases and the transmission of infectious diseases are often not\nrandom, but with direction. Hence, in this paper, we build epidemic model based\non an interconnected directed network, which can be considered as the\ngeneralization of undirected networks and bipartite networks. By using the\nmean-field approach, we establish the Susceptible-Infectious-Susceptible model\non this network. We theoretically analyze the model, and obtain the basic\nreproduction number, which is also the generalization of the critical number\ncorresponding to undirected or bipartite networks. And we prove the global\nstability of disease-free and endemic equilibria via the basic reproduction\nnumber as a forward bifurcation parameter. We also give a condition for\nepidemic prevalence only on a single subnetwork. Furthermore, we carry out\nnumerical simulations, and find that the independence between each node's in-\nand out-degrees greatly reduce the impact of the network's topological\nstructure on disease spread.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Correlation Analysis of Nodes Identifies Real Communities in Networks: A significant problem in analysis of complex network is to reveal community\nstructure, in which network nodes are tightly connected in the same\ncommunities, between which there are sparse connections. Previous algorithms\nfor community detection in real-world networks have the shortcomings of high\ncomplexity or requiring for prior information such as the number or sizes of\ncommunities or are unable to obtain the same resulting partition in multiple\nruns. In this paper, we proposed a simple and effective algorithm that uses the\ncorrelation of nodes alone, which requires neither optimization of predefined\nobjective function nor information about the number or sizes of communities. We\ntest our algorithm on real-world and synthetic graphs whose community structure\nis already known and observe that the proposed algorithm detects this known\nstructure with high applicability and reliability. We also apply the algorithm\nto some networks whose community structure is unknown and find that it detects\ndeterministic and informative community partitions in these cases.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Self-organized network design by link survivals and shortcuts: One of the challenges for future infrastructures is how to design a network\nwith high efficiency and strong connectivity at low cost. We propose\nself-organized geographical networks beyond the vulnerable scale-free structure\nfound in many real systems. The networks with spatially concentrated nodes\nemerge through link survival and path reinforcement on routing flows in a\nwireless environment with a constant transmission range of a node. In\nparticular, we show that adding some shortcuts induces both the small-world\neffect and a significant improvement of the robustness to the same level as in\nthe optimal bimodal networks. Such a simple universal mechanism will open\nprospective ways for several applications in wide-area ad hoc networks, smart\ngrids, and urban planning.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Totally asymmetric simple exclusion process with a time-dependent\n  boundary: interaction between vehicles and pedestrians at intersections: Interaction between vehicles and pedestrians is seen in many areas such as\ncrosswalks and intersections. In this paper, we study a totally asymmetric\nsimple exclusion process with a bottleneck at a boundary caused by an\ninteraction. Due to the time-dependent effect originating from the speed of\npedestrians, the flow of the model varies even if the average hopping\nprobability at the last site is the same. We analyze the phenomenon by using\ntwo types of approximations: (2+1)-cluster approximation and isolated\nrarefaction wave approximation. The approximate results capture intriguing\nfeatures of the model. Moreover, we discuss the situation where vehicles turn\nright at the intersection by adding a traffic light at the boundary condition.\nThe result suggests that pedestrian scrambles are valid to eliminate traffic\ncongestion in the right turn lane.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A Two-Phase Dynamic Contagion Model for COVID-19: In this paper, we propose a continuous-time stochastic intensity model,\nnamely, two-phase dynamic contagion process(2P-DCP), for modelling the epidemic\ncontagion of COVID-19 and investigating the lockdown effect based on the\ndynamic contagion model introduced by Dassios and Zhao (2011). It allows\nrandomness to the infectivity of individuals rather than a constant\nreproduction number as assumed by standard models. Key epidemiological\nquantities, such as the distribution of final epidemic size and expected\nepidemic duration, are derived and estimated based on real data for various\nregions and countries. The associated time lag of the effect of intervention in\neach country or region is estimated. Our results are consistent with the\nincubation time of COVID-19 found by recent medical study. We demonstrate that\nour model could potentially be a valuable tool in the modeling of COVID-19.\nMore importantly, the proposed model of 2P-DCP could also be used as an\nimportant tool in epidemiological modelling as this type of contagion models\nwith very simple structures is adequate to describe the evolution of regional\nepidemic and worldwide pandemic.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Modeling the emergence of a new language: Naming Game with hybridization: In recent times, the research field of language dynamics has focused on the\ninvestigation of language evolution, dividing the work in three evolutive\nsteps, according to the level of complexity: lexicon, categories and grammar.\nThe Naming Game is a simple model capable of accounting for the emergence of a\nlexicon, intended as the set of words through which objects are named. We\nintroduce a stochastic modification of the Naming Game model with the aim of\ncharacterizing the emergence of a new language as the result of the interaction\nof agents. We fix the initial phase by splitting the population in two sets\nspeaking either language A or B. Whenever the result of the interaction of two\nindividuals results in an agent able to speak both A and B, we introduce a\nfinite probability that this state turns into a new idiom C, so to mimic a sort\nof hybridization process. We study the system in the space of parameters\ndefining the interaction, and show that the proposed model displays a rich\nvariety of behaviours, despite the simple mean field topology of interactions.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Large cities are less efficient for sustainable transport: The ABC of\n  mobility: The use of cars in cities has many negative impacts on its population,\nincluding pollution, noise and the use of space. Yet, detecting factors that\nreduce automobile dependency is a serious challenge, particularly across\ndifferent regions. Here we model the use of different modes of transport in a\ncity by aggregating active mobility (A), public transport (B) and cars (C),\nthus expressing the modal share of a city by its ABC triplet. Data for nearly\n800 cities across 60 countries is used to model car use and its relationship\nwith city size and income. Our findings suggest that outside the US, longer\ndistances experienced in large cities reduce the propensity of active mobility\nand of cars, but public transport is more prominent. For cities in the US,\nroughly 90\\% of its mobility depends on cars, regardless of city size. Further,\nincome is strongly related to automobile dependency. Results show that a city\nwith twice the income has 37\\% more journeys by car.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Flow of emotional messages in artificial social networks: Models of message flows in an artificial group of users communicating via the\nInternet are introduced and investigated using numerical simulations. We\nassumed that messages possess an emotional character with a positive valence\nand that the willingness to send the next affective message to a given person\nincreases with the number of messages received from this person. As a result,\nthe weights of links between group members evolve over time. Memory effects are\nintroduced, taking into account that the preferential selection of message\nreceivers depends on the communication intensity during the recent period only.\nWe also model the phenomenon of secondary social sharing when the reception of\nan emotional e-mail triggers the distribution of several emotional e-mails to\nother people.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Dominating Scale-Free Networks Using Generalized Probabilistic Methods: We study ensemble-based graph-theoretical methods aiming to approximate the\nsize of the minimum dominating set (MDS) in scale-free networks. We analyze\nboth analytical upper bounds of dominating sets and numerical realizations for\napplications. We propose two novel probabilistic dominating set selection\nstrategies that are applicable to heterogeneous networks. One of them obtains\nthe smallest probabilistic dominating set and also outperforms the\ndeterministic degree-ranked method. We show that a degree-dependent\nprobabilistic selection method becomes optimal in its deterministic limit. In\naddition, we also find the precise limit where selecting high-degree nodes\nexclusively becomes inefficient for network domination. We validate our results\non several real-world networks, and provide highly accurate analytical\nestimates for our methods.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A stochastic evolutionary model for survival dynamics: The recent interest in human dynamics has led researchers to investigate the\nstochastic processes that explain human behaviour in different contexts. Here\nwe propose a generative model to capture the essential dynamics of survival\nanalysis, traditionally employed in clinical trials and reliability analysis in\nengineering. In our model, the only implicit assumption made is that the longer\nan actor has been in the system, the more likely it is to have failed. We\nderive a power-law distribution for the process and provide preliminary\nempirical evidence for the validity of the model from two well-known survival\nanalysis data sets.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Research on two-dimensional traffic flow model based on psychological\n  field theory: In this paper, the influence of fan-shaped buffer zone on the performance of\nthe toll plaza is researched. A two-dimensional traffic flow model and a\ncomprehensive evaluation model based on mechanical model and psychological\nfield are established. The traffic flow model is simulated by creating\ncoordinate system.\n  We first establish queue theory model to analyze vehicles when entering toll\nplaza. Then, a two-dimensional steadily car-following model is established\nbased on psychological field for the analysis of vehicles when leaving toll\nplaza. According to psychological field theory, we analyze the force condition\nof each vehicle. The force of each vehicle is contributed by the vehicles in\nits observation area and obstacles. By projecting these vehicles and obstacles\nvia the equipotential line in the psychological field, the influence on the\nvalue and direction acceleration of following vehicles is obtained.\nConsequently, the changes of each vehicle's speed and position are obtained as\nwell. Next, we establish simulation based on the states of vehicles and make\nthe rules of vehicle state-changing. By simulating the system, we obtain the\nthroughput of the toll plaza's input and output. Then we obtained the bearing\npressure on the road by the max throughput and the demand of the roads. Using\nthe number of cars in per unit area as the safety factor. Then a comprehensive\nevaluation model is established based on bearing pressure on the road, cost and\nsafety factor.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Emission inventory for maritime shipping emissions in the North and\n  Baltic Sea (2015): A high temporal and spatial resolution emission inventory for the North Sea\nand Baltic Sea for the year 2015 was compiled using current emission factors\nand ship activity data. The inventory includes seagoing vessels over 100 GT\nregistered with the International Maritime Organisation traversing in the North\nand Baltic Seas. A bottom-up approach was chosen for the compilation of the\ninventory, which provides emission levels of the air pollutants CO2, NOx, SO2,\nPM2.5, CO, BC, Ash, NMVOC and POA, as well as the speed-dependent fuel and\nenergy consumption. Input data come from both main and auxiliary engines as\nwell as well-to-tank and tank-to-propeller emission and energy and fuel\nconsumption quantities. The geo-referenced data are provided in a temporal\nresolution of five minutes. The data can be used to assess, inter alia, the\nhealth effects of maritime emissions, external and social costs of maritime\ntransport, emission mitigation effects of alternative fuel scenarios and\nshore-to-ship power supply.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Lattice Gas model to describe a nightclub dynamics: In this work, we propose a simple stochastic agent-based model to describe\nthe revenue dynamics of a nightclub venue based on the relationship between\nprofit and spatial occupation. The system consists of an underlying square\nlattice (nightclub's dance floor) where every attendee (agent) is allowed to\nmove to its first neighboring cells. Each guess has a characteristic delayed\ntime between drinks, denoted as $\\tau$, after which it will show an urge to\ndrink. At this moment, the attendee will tend to move towards the bar where a\ndrink will be bought. After it has left the bar zone, $\\tau$ time steps should\npass so it shows once again the need to drink. Our model among other points\nshow that it is no use filling the bar to obtain profit, and optimization\nshould be analyzed. This can be done in a more secure way taking into\nconsideration the ratio between income and ticket cost.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Logistic Modeling of a Religious Sect Features: The financial characteristics of sects are challenging topics. The present\npaper concerns the Antoinist Cult community (ACC), which has appeared at the\nend of the 19-th century in Belgium, have had quite an expansion, and is now\ndecaying. The historical perspective is described in an Appendix. Although\nsurely of marginal importance in religious history, the numerical and analytic\ndescription of the ACC growth AND decay evolution per se should hopefully\npermit generalizations toward behaviors of other sects, with either longer life\ntime, i.e. so called religions or churches, or to others with shorter life\ntime. Due to the specific aims and rules of the community, in particular the\nlack of proselytism, and strict acceptance of only anonymous financial gifts,\nan indirect measure of their member number evolution can only be studied. This\nis done here first through the time dependence of new temple inaugurations,\nbetween 1910 and 1940. Besides, the community yearly financial reports can be\nanalyzed. They are legally known between 1920 and 2000. Interestingly, several\nregimes are seen, with different time spans. The agent based model chosen to\ndescribe both temple number and finance evolutions is the Verhulst logistic\nfunction taking into account the limited resources of the population. Such a\nfunction remarkably fits the number of temple evolution, taking into account a\nno construction time gap, historically explained. The empirical Gompertz law\ncan also be used for fitting this number of temple evolution data, as shown in\nan Appendix. It is thereby concluded that strong social forces have been acting\nboth in the growth and decay phases.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A Discrepancy-based Framework to Compare Robustness between\n  Multi-Attribute Evaluations: Multi-objective evaluation is a necessary aspect when managing complex\nsystems, as the intrinsic complexity of a system is generally closely linked to\nthe potential number of optimization objectives. However, an evaluation makes\nno sense without its robustness being given (in the sense of its reliability).\nStatistical robustness computation methods are highly dependent of underlying\nstatistical models. We propose a formulation of a model-independent framework\nin the case of integrated aggregated indicators (multi-attribute evaluation),\nthat allows to define a relative measure of robustness taking into account data\nstructure and indicator values. We implement and apply it to a synthetic case\nof urban systems based on Paris districts geography, and to real data for\nevaluation of income segregation for Greater Paris metropolitan area. First\nnumerical results show the potentialities of this new method. Furthermore, its\nrelative independence to system type and model may position it as an\nalternative to classical statistical robustness methods.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Interrupting vaccination policies can greatly spread SARS-CoV-2 and\n  enhance mortality from COVID-19 disease: the AstraZeneca case for France and\n  Italy: Several European countries have suspended the inoculation of the AstraZeneca\nvaccine out of suspicion of causing deep vein thrombosis. In this letter we\nreport some Fermi estimates performed using a stochastic model aimed at making\na risk-benefit analysis of the interruption of the delivery of the AstraZeneca\nvaccine in France and Italy. Our results clearly show that excess deaths due to\nthe interruption of the vaccination campaign injections largely overrun those\ndue to thrombosis even in worst case scenarios of frequency and gravity of the\nvaccine side effects.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Buyer feedback as a filtering mechanism for reputable sellers: We propose a continuum model for the description of buyer and seller dynamics\nin an Internet market. The relevant variables are the research effort of buyers\nand the sellers' reputation building process. We show that, if a commercial\nweb-site gives consumers the possibility to rate credibly sellers they\nbargained with, vendors are forced to be more honest. This leads to mutual\nbeneficial symbiosis between buyers and sellers; the overall enhanced volume of\ntransactions contributes ultimately to the web-site, which facilitates the\nmatchmaking service.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Non-Gibrat's law in the middle scale region: By using numerical simulation, we confirm that Takayasu--Sato--Takayasu (TST)\nmodel which leads Pareto's law satisfies the detailed balance under Gibrat's\nlaw. In the simulation, we take an exponential tent-shaped function as the\ngrowth rate distribution. We also numerically confirm the reflection law\nequivalent to the equation which gives the Pareto index $\\mu$ in TST model.\nMoreover, we extend the model modifying the stochastic coefficient under a\nNon-Gibrat's law. In this model, the detailed balance is also numerically\nobserved. The resultant pdf is power-law in the large scale Gibrat's law\nregion, and is the log-normal distribution in the middle scale Non-Gibrat's\none. These are accurately confirmed in the numerical simulation.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Estimating a Large Drive Time Matrix between Zip Codes in the United\n  States: A Differential Sampling Approach: Estimating a massive drive time matrix between locations is a practical but\nchallenging task. The challenges include availability of reliable road network\n(including traffic) data, programming expertise, and access to high-performance\ncomputing resources. This research proposes a method for estimating a\nnationwide drive time matrix between ZIP code areas in the U.S.--a geographic\nunit at which many national datasets such as health information are compiled\nand distributed. The method (1) does not rely on intensive efforts in data\npreparation or access to advanced computing resources, (2) uses algorithms of\nvarying complexity and computational time to estimate drive times of different\ntrip lengths, and (3) accounts for both interzonal and intrazonal drive times.\nThe core design samples ZIP code pairs with various intensities according to\ntrip lengths and derives the drive times via Google Maps API, and the Google\ntimes are then used to adjust and improve some primitive estimates of drive\ntimes with low computational costs. The result provides a valuable resource for\nresearchers.",
        "category": "physics_soc-ph"
    },
    {
        "text": "ASFAP impact towards the 1st African Light Source: The concrete vision of having Africa as a leader sharing equivalent\nresponsibilities and deliverables towards the global scientific societies turn\nout to be more obvious by time. Africa is not an exception when it comes to\nadvanced science and technological grounds. Many challenges do exist and many\nothers are still accumulating such as establishing cutting-edge large scale\nresearch infrastructures and institutions, reversing the brain-drain dramatic\nchallenge, addressing local and/or regional concerns (health, environment,\nwater, human heritage), as well as being a vehicle for industrial development\nand growing economy. In addition to bringing forward the African educational\nsystems, employment status, besides the human capacity building which is\nalleged to be the backbone of any advanced society. Into the discussion, and\nbesides their strong influence on education and advancing science and\ntechnology, as well as, capacity building development, are synchrotron light\nsources demonstrating the extensive capabilities with numerous techniques\nsupporting a wide range of applications of basic science for instance physics,\nchemistry and biology, along with applied science aspects including life\nsciences such as biomedicine, pharmaceuticals and drug design, in addition to\nagriculture, environment, and air and water pollution, besides materials\nscience and industrial applications, and energy and climate change.\nFurthermore, comprehensive insights can be identified and documented for\ncultural heritage and archaeology domains.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Components in time-varying graphs: Real complex systems are inherently time-varying. Thanks to new communication\nsystems and novel technologies, it is today possible to produce and analyze\nsocial and biological networks with detailed information on the time of\noccurrence and duration of each link. However, standard graph metrics\nintroduced so far in complex network theory are mainly suited for static\ngraphs, i.e., graphs in which the links do not change over time, or graphs\nbuilt from time-varying systems by aggregating all the links as if they were\nconcurrent in time. In this paper, we extend the notion of connectedness, and\nthe definitions of node and graph components, to the case of time-varying\ngraphs, which are represented as time-ordered sequences of graphs defined over\na fixed set of nodes. We show that the problem of finding strongly connected\ncomponents in a time-varying graph can be mapped into the problem of\ndiscovering the maximal-cliques in an opportunely constructed static graph,\nwhich we name the affine graph. It is therefore an NP-complete problem. As a\npractical example, we have performed a temporal component analysis of\ntime-varying graphs constructed from three data sets of human interactions. The\nresults show that taking time into account in the definition of graph\ncomponents allows to capture important features of real systems. In particular,\nwe observe a large variability in the size of node temporal in- and\nout-components. This is due to intrinsic fluctuations in the activity patterns\nof individuals, which cannot be detected by static graph analysis.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Modeling Society with Statistical Mechanics: an Application to Cultural\n  Contact and Immigration: We introduce a general modeling framework to predict the outcomes, at the\npopulation level, of individual psychology and behavior. The framework\nprescribes that researchers build a cost function that embodies knowledge of\nwhat trait values (opinions, behaviors, etc.) are favored by individual\ninteractions under given social conditions. Predictions at the population level\nare then drawn using methods from statistical mechanics, a branch of\ntheoretical physics born to link the microscopic and macroscopic behavior of\nphysical systems. We demonstrate our approach building a model of cultural\ncontact between two cultures (e.g., immigration), showing that it is possible\nto make predictions about how contact changes the two cultures.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Social dilemmas in an online social network: the structure and evolution\n  of cooperation: We investigate two paradigms for studying the evolution of\ncooperation--Prisoner's Dilemma and Snowdrift game in an online friendship\nnetwork obtained from a social networking site. We demonstrate that such social\nnetwork has small-world property and degree distribution has a power-law tail.\nBesides, it has hierarchical organizations and exhibits disassortative mixing\npattern. We study the evolutionary version of the two types of games on it. It\nis found that enhancement and sustainment of cooperative behaviors are\nattributable to the underlying network topological organization. It is also\nshown that cooperators can survive when confronted with the invasion of\ndefectors throughout the entire ranges of parameters of both games. The\nevolution of cooperation on empirical networks is influenced by various network\neffects in a combined manner, compared with that on model networks. Our results\ncan help understand the cooperative behaviors in human groups and society.",
        "category": "physics_soc-ph"
    },
    {
        "text": "How to Measure Significance of Community Structure in Complex Networks: Community structure analysis is a powerful tool for complex networks, which\ncan simplify their functional analysis considerably. Recently, many approaches\nwere proposed to community structure detection, but few works were focused on\nthe significance of community structure. Since real networks obtained from\ncomplex systems always contain error links, and most of the community detection\nalgorithms have random factors, evaluate the significance of community\nstructure is important and urgent. In this paper, we use the eigenvectors'\nstability to characterize the significance of community structures. By\nemploying the eigenvalues of Laplacian matrix of a given network, we can\nevaluate the significance of its community structure and obtain the optimal\nnumber of communities, which are always hard for community detection\nalgorithms. We apply our method to many real networks. We find that significant\ncommunity structures exist in many social networks and C.elegans neural\nnetwork, and that less significant community structures appear in\nprotein-interaction networks and metabolic networks. Our method can be applied\nto broad clustering problems in data mining due to its solid mathematical basis\nand efficiency.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Predicting Network Congestion by Extending Betweenness Centrality to\n  Interacting Agents: We present a simple model to predict network activity at the edge level, by\nextending a known approximation method to compute Betweenness Centrality (BC)\nwith a repulsive mechanism to prevent unphysical densities. By taking into\naccount the strong interaction effects often observed in real phenomena, we aim\nto obtain an improved measure of edge usage during rush hours as traffic\ncongestion patterns emerge in urban networks. In this approach, the network is\niteratively populated by agents following dynamically evolving fastest paths,\nthat are progressively attracted towards uncongested parts of the network, as\nthe global traffic volume increases. Following the transition of the network\nstate from empty to saturated, we study the emergence of congestion and the\nprogressive disruption of global connectivity due to a relatively small\nfraction of crowded edges.\n  We assess the predictive power of our model by comparing the speed\ndistribution against a large experimental dataset for the London area with\nremarkable results, which also translate into a qualitative similarity of the\ncongestion maps. Also, percolation analysis confirms a quantitative agreement\nof the model with the real data for London. For seven other topologically\ndifferent cities we performed simulations to obtain the Fisher critical\nexponent $\\tau$ that showed no common functional dependence on the traffic\nlevel. The critical exponent $\\gamma$, studied to assess the power-law decay of\nspatial correlation, was found inversely proportional to the number of vehicles\nboth for real and simulated traffic.\n  This simulation approach seems particularly fit to describe qualitative and\nquantitative properties of the network loading process, culminating in\npeak-hour congestion, by using only topological and geographical network\nfeatures.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Do good actions inspire good actions in others?: Actions such as sharing food and cooperating to reach a common goal have\nplayed a fundamental role in the evolution of human societies. These good\nactions may not maximise the actor's payoff, but they maximise the other's\npayoff. Consequently, their existence is puzzling for evolutionary theories.\nWhy should you make an effort to help others, even when no reward seems to be\nat stake? Indeed, experiments typically show that humans are heterogeneous:\nsome may help others, while others may not. With the aim of favouring the\nemergence of 'successful cultures', a number of studies has recently\ninvestigated what mechanisms promote the evolution of a particular good action.\nBut still little is known about if and how good actions can spread from person\nto person. For instance, does being recipient of an altruistic act increase\nyour probability of being cooperative with others? Plato's quote, 'Good actions\ngive strength to ourselves and inspire good actions in others', suggests that\nis possible. We have conducted an experiment on Amazon Mechanical Turk to test\nthis mechanism using economic games. We have measured willingness to be\ncooperative through a standard Prisoner's dilemma and willingness to act\naltruistically using a binary Dictator game. In the baseline treatments, the\nendowments needed to play were given by the experimenters, as usual; in the\ncontrol treatments, they came from a good action made by someone else. Across\nfour different comparisons and a total of 572 subjects, we have never found a\nsignificant increase of cooperation or altruism when the endowment came from a\ngood action. We conclude that good actions do not necessarily inspire good\nactions in others, at least in the ideal scenario of a lab experiment with\nanonymous subjects.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The evolution of cooperation in a mobile population on random networks:\n  Network topology matters only for low-degree networks: We consider a finite structured population of mobile individuals that\nstrategically explore a network using a Markov movement model and interact with\neach other via a public goods game. We extend the model of Erovenko et al.\n(2019) from complete, circle, and star graphs to various random networks to\nfurther investigate the effect of network topology on the evolution of\ncooperation. We discover that the network topology affects the outcomes of the\nevolutionary process only for networks of small average degree. Once the degree\nbecomes sufficiently high, the outcomes match those for the complete graph. The\nactual value of the degree when this happens is much smaller than that of the\ncomplete graph, and the threshold value depends on other network\ncharacteristics.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Evaluating the principle of relatedness: Estimation, drivers and\n  implications for policy: A growing body of research documents that the size and growth of an industry\nin a place depends on how much related activity is found there. This fact is\ncommonly referred to as the \"principle of relatedness\". However, there is no\nconsensus on why we observe the principle of relatedness, how best to determine\nwhich industries are related or how this empirical regularity can help inform\nlocal industrial policy. We perform a structured search over tens of thousands\nof specifications to identify robust -- in terms of out-of-sample predictions\n-- ways to determine how well industries fit the local economies of US cities.\nTo do so, we use data that allow us to derive relatedness from observing which\nindustries co-occur in the portfolios of establishments, firms, cities and\ncountries. Different portfolios yield different relatedness matrices, each of\nwhich help predict the size and growth of local industries. However, our\nspecification search not only identifies ways to improve the performance of\nsuch predictions, but also reveals new facts about the principle of relatedness\nand important trade-offs between predictive performance and interpretability of\nrelatedness patterns. We use these insights to deepen our theoretical\nunderstanding of what underlies path-dependent development in cities and expand\nexisting policy frameworks that rely on inter-industry relatedness analysis.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Population changes in residential clusters in Japan: Population dynamics in urban and rural areas are different. Understanding\nfactors that contribute to local population changes has various socioeconomic\nand political implications. In the present study, we use population census data\nin Japan to examine contributors to the population growth of residential\nclusters between years 2005 and 2010. The data set covers the entirety of Japan\nand has a high spatial resolution of 500$\\times$500$\\textrm{m}^2$, enabling us\nto examine population dynamics in various parts of the country (urban and\nrural) using statistical analysis. We found that, in addition to the area,\npopulation density, and age, the shape of the cluster and the spatial\ndistribution of inhabitants within the cluster are significantly related to the\npopulation growth rate of a residential cluster. Specifically, the population\ntends to grow if the cluster is \"round\" shaped (given the area) and the\npopulation is concentrate near the center rather than periphery of the cluster.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Cognitive mechanisms for human flocking dynamics: Low-level \"adaptive\" and higher-level \"sophisticated\" human reasoning\nprocesses have been proposed to play opposing roles in the emergence of\nunpredictable collective behaviors like crowd panics, traffic jams, and market\nbubbles. While adaptive processes are ubiquitous in mechanistic theories of\nemergent social complexity, complementary theories understand incentives,\neducation, and other inducements to rationality as able to suppress such\noutcomes.\n  We show in a series of laboratory experiments that, rather than suppressing\ncomplex social dynamics, sophisticated reasoning processes can support them.\nOur experiments elicit flocking behavior in groups and show that it is driven\nby the human ability to recursively anticipate the reasoning of others. We\nidentify this sophisticated flocking in three different games---the Beauty\nPageant, Mod Game, and Runway Game---across which game theory predicts no\nformal similarity. The persistence of sophisticated flocking across unrelated\ngame types not only speaks to the phenomenon's robustness, it also suggests\nthat people are treating three supposedly different decision settings as\nconceptually similar, implicating a second sophisticated cognitive ability:\nhuman concept formation. We also find in participants' underlying reasoning\nthat the number of recursions they perform is limited not by any individual's\ncognitive abilities, but by a social norm that emerges during flocking.\n  By implicating both recursive reasoning and concept formation in complex\ndynamics, we support interdisciplinary perspectives that emergent complexity is\ntypical of even the most intelligent populations and carefully designed social\nsystems.",
        "category": "physics_soc-ph"
    },
    {
        "text": "How does node centrality in a complex network affect prediction?: In complex financial networks, systemically important nodes usually play\nvital roles. We consider networks consisting of major global assets and explore\nhow node centrality affects price forecasting by applying a hybrid random\nforest algorithm. We find two counterintuitive phenomena: (i) factors with low\ncentrality have better forecasting ability and (ii) nodes with low centrality\ncan be predicted more accurately in direction. Using the notion of entropy,\nwhich measures the quantity of information, we show that factors with low\ncentrality have more useful information and less noise for the forecast asset\nprice than those with high centrality do. In addition, while predicting a\nsystemically unimportant node, we demonstrate that the other nodes within the\nnetwork have a higher information rate. Finally, we verify the robustness of\nour results using an alternative deep learning method.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Detection of the elite structure in a virtual multiplex social system by\n  means of a generalized $K$-core: Elites are subgroups of individuals within a society that have the ability\nand means to influence, lead, govern, and shape societies. Members of elites\nare often well connected individuals, which enables them to impose their\ninfluence to many and to quickly gather, process, and spread information. Here\nwe argue that elites are not only composed of highly connected individuals, but\nalso of intermediaries connecting hubs to form a cohesive and structured\nelite-subgroup at the core of a social network. For this purpose we present a\ngeneralization of the $K$-core algorithm that allows to identify a social core\nthat is composed of well-connected hubs together with their `connectors'. We\nshow the validity of the idea in the framework of a virtual world defined by a\nmassive multiplayer online game, on which we have complete information of\nvarious social networks. Exploiting this multiplex structure, we find that the\nhubs of the generalized $K$-core identify those individuals that are high\nsocial performers in terms of a series of indicators that are available in the\ngame. In addition, using a combined strategy which involves the generalized\n$K$-core and the recently introduced $M$-core, the elites of the different\n'nations' present in the game are perfectly identified as modules of the\ngeneralized $K$-core. Interesting sudden shifts in the composition of the elite\ncores are observed at deep levels. We show that elite detection with the\ntraditional $K$-core is not possible in a reliable way. The proposed method\nmight be useful in a series of more general applications, such as community\ndetection.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Hamiltonian Modeling of Macro-Economic Urban Dynamics: The ongoing rapid urbanization phenomena make the understanding of the\nevolution of urban environments of utmost importance to improve the well-being\nand steer societies towards better futures. Many studies have focused on the\nemerging properties of cities, leading to the discovery of scaling laws\nmirroring, for instance, the dependence of socio-economic indicators on city\nsizes. Though scaling laws allow for the definition of city-size independent\nsocio-economic indicators, only a few efforts have been devoted to the modeling\nof the dynamical evolution of cities as mirrored through socio-economic\nvariables and their mutual influence. In this work, we propose a Maximum\nEntropy (ME), non-linear, generative model of cities. We write in particular a\nHamiltonian function in terms of a few macro-economic variables, whose coupling\nparameters we infer from real data corresponding to French towns. We first\ndiscover that non-linear dependencies among different indicators are needed for\na complete statistical description of the non-Gaussian correlations among them.\nFurthermore, though the dynamics of individual cities are far from being\nstationary, we show that the coupling parameters corresponding to different\nyears turn out to be quite robust. The quasi time-invariance of the Hamiltonian\nmodel allows proposing an analytic model for the evolution in time of the\nmacro-economic variables, based on the Langevin equation. Despite no temporal\ninformation about the evolution of cities has been used to derive this model,\nits forecast accuracy of the temporal evolution of the system is compatible to\nthat of a model inferred using explicitly such information.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A model for generating tunable clustering coefficients independent of\n  the number of nodes in scale free and random networks: Probabilistic networks display a wide range of high average clustering\ncoefficients independent of the number of nodes in the network. In particular,\nthe local clustering coefficient decreases with the degree of the subtending\nnode in a complicated manner not explained by any current models. While a\nnumber of hypotheses have been proposed to explain some of these observed\nproperties, there are no solvable models that explain them all. We propose a\nnovel growth model for both random and scale free networks that is capable of\npredicting both tunable clustering coefficients independent of the network\nsize, and the inverse relationship between the local clustering coefficient and\nnode degree observed in most networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The evolution of pluralistic ignorance: Pluralistic ignorance is a social-psychological phenomenon characterized by\nindividuals privately maintaining beliefs or opinions that diverge from what\nthey perceive to be the prevailing norm within a group or society. Typical\nmodels are based on opinion dynamics involving both private and public states,\nwhere agents' binary states undergo changes influenced by social pressure.\nHowever, these models overlook a crucial aspect of pluralistic ignorance: if\nthe absence of behavioral expression matches the normative status of the\nbehavior, social pressure exerted by the initial group configuration is enough\nto induce pluralistic ignorance. Here, we show that pluralistic ignorance is\nmaintained if imitation of others is not too frequent and the social influence\nof the initial minority is high. Interestingly, the individuals are able to\novercome the pluralistic ignorance by the end of interactions, yet it\nresurfaces at the outset of each subsequent group interaction. However, if the\nlikelihood of individuals imitating others becomes excessively high,\npluralistic ignorance completely dissipates in an undesirable manner:\nindividuals internalize the dysfunctional behavior. We also show that, if\nmemory is added to the internalization process, pluralistic ignorance reaches\nhigh levels only for intermediate imitation rates.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Clustering determines the dynamics of complex contagions in multiplex\n  networks: We present the mathematical analysis of generalized complex contagions in\nclustered multiplex networks for susceptible-infected-recovered (SIR)-like\ndynamics. The model is intended to understand diffusion of influence, or any\nother spreading process implying a threshold dynamics, in setups of\ninterconnected networks with significant clustering. The contagion is assumed\nto be general enough to account for a content-dependent linear threshold model,\nwhere each link type has a different weight (for spreading influence) that may\ndepend on the content (e.g., product, rumor, political view) that is being\nspread. Using the generating functions formalism, we determine the conditions,\nprobability, and expected size of the emergent global cascades. This analysis\nprovides a generalization of previous approaches and is specially useful in\nproblems related to spreading and percolation. The results present non trivial\ndependencies between the clustering coefficient of the networks and its average\ndegree. In particular, several phase transitions are shown to occur depending\non these descriptors. Generally speaking, our findings reveal that increasing\nclustering decreases the probability of having global cascades and their size,\nhowever this tendency changes with the average degree. There exists a certain\naverage degree from which on clustering favours the probability and size of the\ncontagion. By comparing the dynamics of complex contagions over multiplex\nnetworks and their monoplex projections, we demonstrate that ignoring link\ntypes and aggregating network layers may lead to inaccurate conclusions about\ncontagion dynamics, particularly when the correlation of degrees between layers\nis high.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Social Network Analysis of Corruption Structures: Adjacency Matrices\n  Supporting the Visualization and Quantification of Layeredness: Often, corruption is described as taking place within or supported by a\nnetwork: A collection of individuals structured in such a way as to enable the\ntransaction of bribes for favors. Surprisingly, despite the network\nnomenclature, corruption is rarely analyzed from the network perspective using\nthe tools of network science. Here, we will argue that analyzing corruption\nfrom the perspective of network science is beneficial to its understanding. In\npassing this chapter, a contribution to the Liber Amicorum in honor of Leo\nHuberts, then gives a very short introduction into social network analysis.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Effect of marital status on death rates. Part 2: Transient mortality\n  spikes: We examine what happens in a population when it experiences an abrupt change\nin surrounding conditions. Several cases of such \"abrupt transitions\" for both\nphysical and living social systems are analyzed from which it can be seen that\nall share a common pattern. First, a steep rising death rate followed by a much\nslower relaxation process during which the death rate decreases as a power law\n(with an exponent close to 0.7). This leads us to propose a general principle\nwhich can be summarized as follows: \"ANY abrupt change in living conditions\ngenerates a mortality spike which acts as a kind of selection process.\" This we\nterm the Transient Shock conjecture. It provides a qualitative model which\nleads to testable predictions. For example, marriage certainly brings about a\nmajor change in environmental and social conditions and according to our\nconjecture one would expect a mortality spike in the months following marriage.\nAt first sight this may seem an unlikely proposition but we demonstrate (by\nthree different methods) that even here the existence of mortality spikes is\nsupported by solid empirical evidence.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Agreement dynamics on directed random graphs: We examine some agreement-dynamics models that are placed on directed random\ngraphs. In such systems a fraction of sites $\\exp(-z)$, where $z$ is the\naverage degree, becomes permanently fixed or flickering. In the Voter model,\nwhich has no surface tension, such zealots or flickers freely spread their\nopinions and that makes the system disordered. For models with a surface\ntension, like the Ising model or the Naming Game model, their role is limited\nand such systems are ordered at large~$z$. However, when $z$ decreases, the\ndensity of zealots or flickers increases, and below a certain threshold ($z\\sim\n1.9-2.0$) the system becomes disordered. On undirected random graphs agreement\ndynamics is much different and ordering appears as soon the graph is above the\npercolation threshold at $z=1$.",
        "category": "physics_soc-ph"
    },
    {
        "text": "On the time dependence of the $h$-index: The time dependence of the $h$-index is analyzed by considering the average\nbehaviour of $h$ as a function of the academic age $A_A$ for about 1400 Italian\nphysicists, with career lengths spanning from 3 to 46 years. The individual\n$h$-index is strongly correlated with the square root of the total citations\n$N_C$: $h \\approx 0.53 \\sqrt{N_C}$. For academic ages ranging from 12 to 24\nyears, the distribution of the time scaled index $h/\\sqrt{A_A}$ is\napproximately time-independent and it is well described by the Gompertz\nfunction. The time scaled index $h/\\sqrt{A_A}$ has an average approximately\nequal to 3.8 and a standard deviation approximately equal to 1.6. Finally, the\ntime scaled index $h/\\sqrt{A_A}$ appears to be strongly correlated with the\ncontemporary $h$-index $h_c$.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The second will be first: competition on directed networks: Multiple sinks competition is investigated for a walker diffusing on directed\ncomplex networks. The asymmetry of the imposed spatial support makes the system\nnon transitive. As a consequence, it is always possible to identify a suitable\nlocation for the second absorbing sink that screens at most the flux of agents\ndirected against the first trap, whose position has been preliminarily\nassigned. The degree of mutual competition between pairs of nodes is\nanalytically quantified through apt indicators that build on the topological\ncharacteristics of the hosting graph. Moreover, the positioning of the second\ntrap can be chosen so as to minimize, at the same time the probability of being\nin turn shaded by a thirdly added trap. Supervised placing of absorbing traps\non a asymmetric disordered and complex graph is hence possible, as follows a\nrobust optimization protocol. This latter is here discussed and successfully\ntested against synthetic data.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Coverage versus Supply Cost in Facility Location: Physics of Frustrated\n  Spin Systems: A comprehensive coverage is crucial for communication, supply and\ntransportation networks, yet it is limited by the requirement of extensive\ninfrastructure and heavy energy consumption. Here we draw an analogy between\nspins in antiferromagnet and outlets in supply networks, and apply techniques\nfrom the studies of disordered systems to elucidate the effects of balancing\nthe coverage and supply costs on the network behavior. A readily applicable,\ncoverage optimization algorithm is derived. Simulation results show that\nmagnetized and antiferromagnetic domains emerge and coexist to balance the need\nfor coverage and energy saving. The scaling of parameters with system size\nagrees with the continuum approximation in two dimensions and the tree\napproximation in random graphs. Due to frustration caused by the competition\nbetween coverage and supply cost, a transition between easy and hard\ncomputation regimes is observed. We further suggest a local expansion approach\nto greatly simplify the message updates which shed light on simplifications in\nother problems.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Signatures of currency vertices: Many real-world networks have broad degree distributions. For some systems,\nthis means that the functional significance of the vertices is also broadly\ndistributed, in other cases the vertices are equally significant, but in\ndifferent ways. One example of the latter case is metabolic networks, where the\nhigh-degree vertices -- the currency metabolites -- supply the molecular groups\nto the low-degree metabolites, and the latter are responsible for the\nhigher-order biological function, of vital importance to the organism. In this\npaper, we propose a generalization of currency metabolites to currency\nvertices. We investigate the network structural characteristics of such\nsystems, both in model networks and in some empirical systems. In addition to\nmetabolic networks, we find that a network of music collaborations and a\nnetwork of e-mail exchange could be described by a division of the vertices\ninto currency vertices and others.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Diffusion of innovations in Axelrod's model: Axelrod's model for the dissemination of culture contains two key factors\nrequired to model the process of diffusion of innovations, namely, social\ninfluence (i.e., individuals become more similar when they interact) and\nhomophily (i.e., individuals interact preferentially with similar others). The\nstrength of these social influences are controlled by two parameters: $F$, the\nnumber of features that characterizes the cultures and $q$, the common number\nof states each feature can assume. Here we assume that the innovation is a new\nstate of a cultural feature of a single individual -- the innovator -- and\nstudy how the innovation spreads through the networks among the individuals.\nFor infinite regular lattices in one (1D) and two dimensions (2D), we find that\ninitially the successful innovation spreads linearly with the time $t$, but in\nthe long-time limit it spreads diffusively ($\\sim t^{1/2}$) in 1D and\nsub-diffusively ($\\sim t/\\ln t$) in 2D. For finite lattices, the growth curves\nfor the number of adopters are typically concave functions of $t$. For random\ngraphs with a finite number of nodes $N$, we argue that the classical S-shaped\ngrowth curves result from a trade-off between the average connectivity $K$ of\nthe graph and the per feature diversity $q$. A large $q$ is needed to reduce\nthe pace of the initial spreading of the innovation and thus delimit the\nearly-adopters stage, whereas a large $K$ is necessary to ensure the onset of\nthe take-off stage at which the number of adopters grows superlinearly with\n$t$. In an infinite random graph we find that the number of adopters of a\nsuccessful innovation scales with $t^\\gamma$ with $\\gamma =1$ for $K> 2$ and\n$1/2 < \\gamma < 1$ for $K=2$. We suggest that the exponent $\\gamma$ may be a\nuseful index to characterize the process of diffusion of successful innovations\nin diverse scenarios.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Measuring the temperature and diversity of the U.S. regulatory ecosystem: Over the last 23 years, the U.S. Securities and Exchange Commission has\nrequired over 34,000 companies to file over 165,000 annual reports. These\nreports, the so-called \"Form 10-Ks,\" contain a characterization of a company's\nfinancial performance and its risks, including the regulatory environment in\nwhich a company operates. In this paper, we analyze over 4.5 million references\nto U.S. Federal Acts and Agencies contained within these reports to build a\nmean-field measurement of temperature and diversity in this regulatory\necosystem, where companies are organisms inhabiting the regulatory environment.\nWhile individuals across the political, economic, and academic world frequently\nrefer to trends in this regulatory ecosystem, far less attention has been paid\nto supporting such claims with large-scale, longitudinal data. In this paper,\nwe document an increase in the regulatory energy per filing, i.e., a warming\n\"temperature.\" We also find that the diversity of the regulatory ecosystem has\nbeen increasing over the past two decades, as measured by the dimensionality of\nthe regulatory space and distance between the \"regulatory bitstrings\" of\ncompanies. These findings support the claim that regulatory activity and\ncomplexity are increasing, and this measurement framework contributes an\nimportant step towards improving academic and policy discussions around legal\ncomplexity and regulation.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Temporal-topological properties of higher-order evolving networks: Human social interactions are typically recorded as time-specific dyadic\ninteractions, and represented as evolving (temporal) networks, where links are\nactivated/deactivated over time. However, individuals can interact in groups of\nmore than two people. Such group interactions can be represented as\nhigher-order events of an evolving network. Here, we propose methods to\ncharacterize the temporal-topological properties of higher-order events to\ncompare networks and identify their (dis)similarities. We analyzed 8 real-world\nphysical contact networks, finding the following: a) Events of different orders\nclose in time tend to be also close in topology; b) Nodes participating in many\ndifferent groups (events) of a given order tend to involve in many different\ngroups (events) of another order; Thus, individuals tend to be consistently\nactive or inactive in events across orders; c) Local events that are close in\ntopology are correlated in time, supporting observation a). Differently, in 5\ncollaboration networks, observation a) is almost absent; Consistently, no\nevident temporal correlation of local events has been observed in collaboration\nnetworks. Such differences between the two classes of networks may be explained\nby the fact that physical contacts are proximity based, in contrast to\ncollaboration networks. Our methods may facilitate the investigation of how\nproperties of higher-order events affect dynamic processes unfolding on them\nand possibly inspire the development of more refined models of higher-order\ntime-varying networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Dissecting Resilience Triangle: Unravelling Resilience Curve Archetypes\n  and Properties in Human Systems Facing Weather Hazards: Resilience curves have been the primary approach for conceptualizing and\nrepresenting the resilience behavior of communities during hazard events;\nhowever, the use of resilience curves has remained as a mere conceptual and\nvisual tool with limited data-driven characterization and empirical grounding.\nEmpirical characterizations of resilience curves provide essential insights\nregarding the manner in which differently impacted systems of communities\nabsorb perturbations and recover from disruptions. To address this gap, this\nstudy examines human mobility resilience patterns following multiple\nweather-related hazard events in the United States by analyzing more than 2000\nempirical resilience curves constructed from high-resolution location-based\nmobility data. These empirical resilience curves are then classified using\nk-means clustering based on various features into archetypes. Three main\narchetypes of human mobility resilience are identified: Type I, with rapid\nrecovery after mild impact; Type II, exhibiting bimodal recovery after moderate\nimpact; and Type III, showing slower recovery after severe impact. The results\nalso reveal critical thresholds, such as the bimodal recovery breakpoint at a\n20% impact extent, at which the recovery rate decreases, and the critical\nfunctional threshold at a 60% impact extent, above which recovery rate would be\nrather slow. The results show that a critical functional recovery rate of 2.5%\nper day is necessary to follow the bimodal resilience archetype when impact\nextent exceeds more than 20%. These findings provide novel and important\ninsights into different resilience curve archetypes and their fundamental\nproperties. Departing from using resilience curves as a mere concept and visual\ntool, the data-driven specification of resilience curve archetypes and their\nproperties improve our understanding of the resilience patterns...",
        "category": "physics_soc-ph"
    },
    {
        "text": "Patterns of cooperation during collective emergencies in the\n  help-or-escape social dilemma: Although cooperation is central to the organisation of many social systems,\nrelatively little is known about cooperation in situations of collective\nemergency. When groups of people flee from a danger such as a burning building\nor a terrorist attack, the collective benefit of cooperation is important, but\nthe cost of helping is high and the temptation to defect is strong. To explore\nthe degree of cooperation in emergencies, we develop a new social game, the\nhelp-or-escape social dilemma. Under time and monetary pressure, players decide\nhow much risk they are willing to take in order to help others. Results\nindicated that players took as much risk to help others during emergencies as\nthey did under normal conditions. In both conditions, most players applied an\negalitarian heuristic and helped others until their chance of success equalled\nthat of the group. This strategy is less efficient during emergencies, however,\nbecause the increased time pressure results in fewer people helped.\nFurthermore, emergencies tend to amplify participants initial tendency to\ncooperate, with prosocials becoming even more cooperative and individualists\nbecoming even more selfish. Our framework offers new opportunities to study\nhuman cooperation and could help authorities to better manage crowd behaviours\nduring mass emergencies.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Emerging solutions from the battle of defensive alliances: Competing strategies in an evolutionary game model, or species in a\nbiosystem, can easily form a larger unit which protects them from the invasion\nof an external actor. Such a defensive alliance may have two, three, four or\neven more members. But how effective can be such formation against an\nalternative group composed by other competitors? To address this question we\nstudy a minimal model where a two-member and a four-member alliances fight in a\nsymmetric and balanced way. By presenting representative phase diagrams, we\nsystematically explore the whole parameter range which characterizes the inner\ndynamics of the alliances and the intensity of their interactions. The group\nformed by a pair, who can exchange their neighboring positions, prevail in the\nmajority of the parameter region. The rival quartet can only win if their inner\ncyclic invasion rate is significant while the mixing rate of the pair is\nextremely low. At specific parameter values, when neither of the alliances is\nstrong enough, new four-member solutions emerge where a\nrock-paper-scissors-like trio is extended by the other member of the pair.\nThese new solutions coexist hence all six competitors can survive. The\nevolutionary process is accompanied by serious finite-size effects which can be\nmitigated by appropriately chosen prepared initial states.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Dynamical phase transition due to preferential cluster growth of\n  collective emotions in online communities: We consider a preferential cluster growth in a one-dimensional stochastic\nmodel describing the dynamics of a binary chain with long-range memory. The\nmodel is driven by data corresponding to emotional patterns observed during\nonline communities' discussions. The system undergoes a dynamical phase\ntransition. For low values of the preference exponent, both states are observed\nduring the string evolution in the majority of simulated discussion threads.\nWhen the exponent crosses a critical value, in the majority of threads an\nordered phase emerges, i.e. from a certain time moment only one state is\nrepresented. The transition becomes discontinuous in the thermodynamical limit\nwhen the discussions are infinitely long and even an infinitely small\npreference exponent leads to the ordering behavior in every discussion thread.\nNumerical simulations are in a good agreement with approximated analytical\nformula.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Reconciling long-term cultural diversity and short-term collective\n  social behavior: An outstanding open problem is whether collective social phenomena occurring\nover short timescales can systematically reduce cultural heterogeneity in the\nlong run, and whether offline and online human interactions contribute\ndifferently to the process. Theoretical models suggest that short-term\ncollective behavior and long-term cultural diversity are mutually excluding,\nsince they require very different levels of social influence. The latter\njointly depends on two factors: the topology of the underlying social network\nand the overlap between individuals in multidimensional cultural space.\nHowever, while the empirical properties of social networks are well understood,\nlittle is known about the large-scale organization of real societies in\ncultural space, so that random input specifications are necessarily used in\nmodels. Here we use a large dataset to perform a high-dimensional analysis of\nthe scientific beliefs of thousands of Europeans. We find that inter-opinion\ncorrelations determine a nontrivial ultrametric hierarchy of individuals in\ncultural space, a result unaccessible to one-dimensional analyses and in\nstriking contrast with random assumptions. When empirical data are used as\ninputs in models, we find that ultrametricity has strong and counterintuitive\neffects, especially in the extreme case of long-range online-like interactions\nbypassing social ties. On short time-scales, it strongly facilitates a\nsymmetry-breaking phase transition triggering coordinated social behavior. On\nlong time-scales, it severely suppresses cultural convergence by restricting it\nwithin disjoint groups. We therefore find that, remarkably, the empirical\ndistribution of individuals in cultural space appears to optimize the\ncoexistence of short-term collective behavior and long-term cultural diversity,\nwhich can be realized simultaneously for the same moderate level of mutual\ninfluence.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Temporal Heterogeneities Increase the Prevalence of Epidemics on\n  Evolving Networks: Empirical studies suggest that contact patterns follow heterogeneous\ninter-event times, meaning that intervals of high activity are followed by\nperiods of inactivity. Combined with birth and death of individuals, these\ntemporal constraints affect the spread of infections in a non-trivial way and\nare dependent on the particular contact dynamics. We propose a stochastic model\nto generate temporal networks where vertices make instantaneous contacts\nfollowing heterogeneous inter-event times, and leave and enter the system at\nfixed rates. We study how these temporal properties affect the prevalence of an\ninfection and estimate R0, the number of secondary infections, by modeling\nsimulated infections (SIR, SI and SIS) co-evolving with the network structure.\nWe find that heterogeneous contact patterns cause earlier and larger epidemics\non the SIR model in comparison to homogeneous scenarios. In case of SI and SIS,\nthe epidemics is faster in the early stages (up to 90% of prevalence) followed\nby a slowdown in the asymptotic limit in case of heterogeneous patterns. In the\npresence of birth and death, heterogeneous patterns always cause higher\nprevalence in comparison to homogeneous scenarios with same average inter-event\ntimes. Our results suggest that R0 may be underestimated if temporal\nheterogeneities are not taken into account in the modeling of epidemics.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Status of e-Print Servers: We make a short study of the history and evolution of scientific\npublications, in order to explain the format for near-term e-Prints servers,\nproposing a new scientific publication scheme via digital network, and\nexploring the new dynamics of publication.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Monitoring voltage collapse margin with synchrophasors across\n  transmission corridors with multiple lines and multiple contingencies: We use synchrophasor measurements of the complex voltage and current at both\nends of multiple transmission lines that connect areas of a power system to\nmonitor the online voltage collapse margin. A new reduction is used to reduce\nthe multiple transmission lines to a single line equivalent and determine how\nto combine the synchrophasor measurements. Generator reactive power limits can\nbe accommodated. The results show that this methodology can capture the effect\nof multiple contingencies inside the transmission corridors, giving awareness\nto the operators about the severity of contingencies with respect to voltage\nstability.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Adoption of Innovations with Contrarians and Repentant Agents: The dynamics of adoption of innovations is an important subject in many\nfields and areas, like technological development, industrial processes, social\nbehavior, fashion or marketing. The number of adopters of a new technology\ngenerally increases following a kind of logistic function. However, empirical\ndata provide evidences that this behavior may be more complex, as many factors\ninfluence the decision to adopt an innovation. On the one hand, although some\nindividuals are inclined to adopt an innovation if many people do the same,\nthere are others who act in the opposite direction, trying to differentiate\nfrom the \"herd\". People who prefer to behave like the others are called\nmimetic, whereas individuals who resist adopting new products, the stronger the\ngreater the number of adopters, are named contrarians. On the other hand, new\nadopters may have second thoughts and change their decisions accordingly.\nAgents who regret and abandon their decision will be denominated repentant. In\nthis paper we investigate a simple model for the adoption of an innovation for\na society composed by mimetic and contrarian individuals whose decisions depend\nmainly on three elements: the appeal of the novelty, the inertia or resistance\nto adopt it, and the social interactions with other agents. In the process,\nagents can repent and turn back to the old technology. We present analytic\ncalculations and numerical simulations to determine the conditions for the\nestablishment of the new technology. The inclusion of repentant agents modify\nthe balance between the global incentive to adopt and the number of contrarians\nwho prevent full adoption, generating a rich landscape of temporal evolution\nthat includes cycles of adoption.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Mean-field theory for double-well systems on degree-heterogeneous\n  networks: Many complex dynamical systems in the real world, including ecological,\nclimate, financial, and power-grid systems, often show critical transitions, or\ntipping points, in which the system's dynamics suddenly transit into a\nqualitatively different state. In mathematical models, tipping points happen as\na control parameter gradually changes and crosses a certain threshold. Tipping\nelements in such systems may interact with each other as a network, and\nunderstanding the behavior of interacting tipping elements is a challenge\nbecause of the high dimensionality originating from the network. Here we\ndevelop a degree-based mean-field theory for a prototypical double-well system\ncoupled on a network with the aim of understanding coupled tipping dynamics\nwith a low-dimensional description. The method approximates both the onset of\nthe tipping point and the position of equilibria with a reasonable accuracy.\nBased on the developed theory and numerical simulations, we also provide\nevidence for multistage tipping point transitions in networks of double-well\nsystems.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Conditions for Quantum Interference in Cognitive Sciences: We present a general classification of the conditions under which cognitive\nscience, concerned e.g. with decision making, requires the use of quantum\ntheoretical notions. The analysis is done in the frame of the mathematical\napproach based on the theory of quantum measurements. We stress that quantum\neffects in cognition can arise only when decisions are made under uncertainty.\nConditions for the appearance of quantum interference in cognitive sciences and\nthe conditions when interference cannot arise are formulated.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Formation of homophily in academic performance: students prefer to\n  change their friends rather than performance: Homophily, the tendency of individuals to associate with others who share\nsimilar traits, has been identified as a major driving force in the formation\nand evolution of social ties. In many cases, it is not clear if homophily is\nthe result of a socialization process, where individuals change their traits\naccording to the dominance of that trait in their local social networks, or if\nit results from a selection process, in which individuals reshape their social\nnetworks so that their traits match those in the new environment. Here we\ndemonstrate the detailed temporal formation of strong homophily in academic\nachievements of high school and university students. We analyze a unique\ndataset that contains information about the detailed time evolution of a\nfriendship network of 6,000 students across 42 months. Combining the evolving\nsocial network data with the time series of the academic performance (GPA) of\nindividual students, we show that academic homophily is a result of selection:\nstudents prefer to gradually reorganize their social networks according to\ntheir performance levels, rather than adapting their performance to the level\nof their local group. We find no signs for a pull effect, where a social\nenvironment of good performers motivates bad students to improve their\nperformance. We are able to understand the underlying dynamics of grades and\nnetworks with a simple model. The lack of a social pull effect in classical\neducational settings could have important implications for the understanding of\nthe observed persistence of segregation, inequality and social immobility in\nsocieties.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Distinguishing simple and complex contagion processes on networks: Contagion processes on networks, including disease spreading, information\ndiffusion, or social behaviors propagation, can be modeled as simple contagion,\ni.e. involving one connection at a time, or as complex contagion, in which\nmultiple interactions are needed for a contagion event. Empirical data on\nspreading processes however, even when available, do not easily allow to\nuncover which of these underlying contagion mechanisms is at work. We propose a\nstrategy to discriminate between these mechanisms upon the observation of a\nsingle instance of a spreading process. The strategy is based on the\nobservation of the order in which network nodes are infected, and on its\ncorrelations with their local topology: these correlations differ between\nprocesses of simple contagion, processes involving threshold mechanisms and\nprocesses driven by group interactions (i.e., by \"higher-order\" mechanisms).\nOur results improve our understanding of contagion processes and provide a\nmethod using only limited information to distinguish between several possible\ncontagion mechanisms.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Dynamics of the US domestic airline network during the COVID-19 pandemic: The ongoing COVID-19 pandemic has had serious impacts on the airline\nindustry. Ensuring that aviation policies in emergent situations both guarantee\nnetwork connectivity and maintain competition among airlines is crucial in\nthese circumstances. To this end, we aimed to understand the network dynamics\nof individual airlines. In this study, we quantitatively reveal the day-to-day\ndynamics of these US domestic airline networks, comprising 17 airlines, from\nJanuary 2019 to December 2021. Specifically, we applied a framework for\nanalyzing temporal networks, in which the network structure changes over time.\nWe found that, first, even though the number of nodes and edges returned to\npre-pandemic levels around July 2021, the structure of the entire US domestic\nairline network remained altered. We also found that the network dynamics\nvaried significantly from airline to airline. Full-service carriers were less\nflexible in changing their network structure and suffered higher revenue\nlosses. On the contrary, most regional carriers completely shifted to a new\nstructure, which may have contributed to reducing their revenue losses.\nLow-cost carriers were characterized by more pronounced differences between\nairlines and drastically changed their network structure immediately after the\ndeclaration of a national emergency.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A Survey of Interdependency Models for Critical Infrastructure Networks: The critical infrastructures of the nation such as the power grid and the\ncommunication network are highly interdependent. Also, it has been observed\nthat there exists complex interdependent relationships between individual\nentities of the power grid and the communication network that further\nobfuscates the analysis, and mitigation of faults in such multi-layered\nnetworks. In recent years, the research community has made significant efforts\ntowards gaining insight and understanding of the interdependency relations in\nsuch multi-layered networks, and accordingly, a number of models have been\nproposed and analyzed towards realizing this goal. In this chapter we study\nexisting interdependency models proposed in the recent literature and discuss\ntheir approach, and inherent features, towards modeling interdependent\nmulti-layer networks. We also provide a brief discussion into the drawbacks of\neach of these models and propose an alternate model that addresses these\ndrawbacks by capturing the interdependency relationships using a combination of\nconjunctive and disjunctive relations.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Proxyeconomics, the inevitable corruption of proxy-based competition: When society maintains a competitive system to promote an abstract goal,\ncompetition by necessity relies on imperfect proxy measures. For instance\nprofit is used to measure value to consumers, patient volumes to measure\nhospital performance, or the Journal Impact Factor to measure scientific value.\nHere we note that \\textit{any proxy measure in a competitive societal system\nbecomes a target for the competitors, promoting corruption of the measure},\nsuggesting a general applicability of what is best known as Campbell's or\nGoodhart's Law. Indeed, prominent voices have argued that the scientific\nreproducibility crisis or inaction to the threat of global warming represent\ninstances of such competition induced corruption. Moreover, competing\nindividuals often report that competitive pressures limit their ability to act\naccording to the societal goal, suggesting lock-in. However, despite the\nprofound implications, we lack a coherent theory of such a process. Here we\npropose such a theory, formalized as an agent based model, integrating insights\nfrom complex systems theory, contest theory, behavioral economics and cultural\nevolution theory. The model reproduces empirically observed patterns at\nmultiple levels. It further suggests that any system is likely to converge\ntowards an equilibrium level of corruption determined by i) the information\ncaptured in the proxy and ii) the strength of an intrinsic incentive towards\nthe societal goal. Overall, the theory offers mechanistic insight to subjects\nas diverse as the scientific reproducibility crisis and the threat of global\nwarming.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Significancia estad\u00edstica del exceso de muertes en Chile durante\n  pandemia COVID-19: The study of mortality and the calculation of excess deaths is a fundamental\ntool for understanding the effect of epidemics / pandemics and the seasonal\ncycles of endemic / epidemic diseases such as influenza, which is of great\nimportance in times of crisis, according to the WHO and different authors. In\nthis study, we analyzed the indicator of excess deaths in Chile during the\nCOVID-19 pandemic. Simultaneously, research is being carried out to corroborate\nand characterize possible causes of the underestimation of deaths by official\nhealth agencies.",
        "category": "physics_soc-ph"
    },
    {
        "text": "On singularities in combination-driven models of technological\n  innovation: It has been suggested that innovations occur mainly by combination: the more\ninventions accumulate, the higher the probability that new inventions are\nobtained from previous designs. Additionally, it has been conjectured that the\ncombinatorial nature of innovations naturally leads to a singularity: at some\nfinite time, the number of innovations should diverge. Although these ideas are\ncertainly appealing, no general models have been yet developed to test the\nconditions under which combinatorial technology should become explosive. Here\nwe present a generalised model of technological evolution that takes into\naccount two major properties: the number of previous technologies needed to\ncreate a novel one and how rapidly technology ages. Two different models of\ncombinatorial growth are considered, involving different forms of ageing. When\nlong-range memory is used and thus old inventions are available for novel\ninnovations, singularities can emerge under some conditions with two phases\nseparated by a critical boundary. If the ageing has a characteristic time\nscale, it is shown that no singularities will be observed. Instead, a \"black\nhole\" of old innovations appears and expands in time, making the rate of\ninvention creation slow down into a linear regime.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Multiway spectral community detection in networks: One of the most widely used methods for community detection in networks is\nthe maximization of the quality function known as modularity. Of the many\nmaximization techniques that have been used in this context, some of the most\nconceptually attractive are the spectral methods, which are based on the\neigenvectors of the modularity matrix. Spectral algorithms have, however, been\nlimited by and large to the division of networks into only two or three\ncommunities, with divisions into more than three being achieved by repeated\ntwo-way division. Here we present a spectral algorithm that can directly divide\na network into any number of communities. The algorithm makes use of a mapping\nfrom modularity maximization to a vector partitioning problem, combined with a\nfast heuristic for vector partitioning. We compare the performance of this\nspectral algorithm with previous approaches and find it to give superior\nresults, particularly in cases where community sizes are unbalanced. We also\ngive demonstrative applications of the algorithm to two real-world networks and\nfind that it produces results in good agreement with expectations for the\nnetworks studied.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Modelling election dynamics and the impact of disinformation: Complex dynamical systems driven by the unravelling of information can be\nmodelled effectively by treating the underlying flow of information as the\nmodel input. Complicated dynamical behaviour of the system is then derived as\nan output. Such an information-based approach is in sharp contrast to the\nconventional mathematical modelling of information-driven systems whereby one\nattempts to come up with essentially {\\it ad hoc} models for the outputs. Here,\ndynamics of electoral competition is modelled by the specification of the flow\nof information relevant to election. The seemingly random evolution of the\nelection poll statistics are then derived as model outputs, which in turn are\nused to study election prediction, impact of disinformation, and the optimal\nstrategy for information management in an election campaign.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Dynamics of tax evasion through an epidemic-like model: In this work we study a model of tax evasion. We considered a fixed\npopulation divided in three compartments, namely honest tax payers, tax evaders\nand a third class between the mentioned two, which we call\n\\textit{susceptibles} to become evaders. The transitions among those\ncompartments are ruled by probabilities, similarly to a model of epidemic\nspreading. These probabilities model social interactions among the individuals,\nas well as the government's fiscalization. We simulate the model on\nfully-connected graphs, as well as on scale-free and random complex networks.\nFor the fully-connected and random graph cases we observe that the emergence of\ntax evaders in the population is associated with an active-absorbing\nnonequilibrium phase transition, that is absent in scale-free networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Intelligence of agents produces a structural phase transition in\n  collective behaviour: Living organisms process information to interact and adapt to their changing\nenvironment with the goal of finding food, mates or averting hazards. The\nstructure of their niche has profound repercussions by both selecting their\ninternal architecture and also inducing adaptive responses to environmental\ncues and stimuli. Adaptive, collective behaviour underpinned by specialized\noptimization strategies is ubiquitously found in the natural world. This\nexceptional success originates from the processes of fitness and selection.\nHere we prove that a universal physical mechanism of a nonequilibrium\ntransition underlies the collective organization of information-processing\norganisms. As cognitive agents build and update an internal, cognitive\nrepresentation of the causal structure of their environment, complex patterns\nemerge in the system, where the onset of pattern formation relates to the\nspatial overlap of cognitive maps. Studying the exchange of information among\nthe agents reveals a continuous, order-disorder transition. As a result of the\nspontaneous breaking of translational symmetry, a Goldstone mode emerges, which\npoints at a collective mechanism of information transfer among cognitive\norganisms. Taken together, the characteristics of this phase transition\nconsolidate different results in cognitive and biological sciences in a\nuniversal manner. These finding are generally applicable to the design of\nartificial intelligent swarm systems that do not rely on centralized control\nschemes.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Thermodynamics approach to near future of civilization: The purpose of this study is to consider the near future of civilization in\nthe framework of thermodynamics. Kardashev's proposal to evaluate the\ndevelopment of celestial civilizations by the amount of energy they are able to\nuse was adopted to translate the concept of human activity into the language of\nphysics. The discussion is limited to considering the last 500 years of history\nfrom the beginning of the scientific and technological revolutions to our\nimmediate future. The application of classical and nonequilibrium\nthermodynamics is discussed. In the framework of classical thermodynamics, two\nsystems are compared: a) the first is a hypothetical quasi-equilibrium system,\nEarth without a population. Since biological evolution becomes almost\nimperceptible for a short period of time, such a system remains in the same\npristine state for the entire period; (b) the second is our habitable planet,\nwhich is not in equilibrium due to rapid anthropogenic evolution and can be\nconsidered as a combination of the first system with human civilization. It is\nshown that in response to the development of civilization (a) the equilibrium\nof the hypothetical system is disturbed and processes are initiated aimed at\nreducing the amount of energy produced, and (b) there is a maximum on the path\nof civilization development over time. The resistance of nature will continue\nuntil a new balance is established, corresponding to a lower level of energy\nproduction. The central problem is whether humanity is ready and able to agree\non a new balance, otherwise the degradation of our planet can lead to the\ncollapse of civilization.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A mutually beneficial approach to electricity network pricing in the\n  presence of large amounts of solar power and community-scale energy storage: Electricity distribution networks that contain large photovoltaic solar\nsystems can experience power flows between customers. These may create both\ntechnical and socio-economic challenges. This paper establishes how these\nchallenges can be addressed through the combined deployment of Community-scale\nEnergy Storage (CES) and local network tariffs. Our study simulates the\noperation of a CES under a range of local network tariff models, using current\nAustralian electricity prices and current network prices as a reference. We\nassess the financial outcomes for solar and non-solar owning customers and the\ndistribution network operator. We find that tariff settings exist that create\nmutual benefits for all stakeholders. Such tariffs all apply a discount of\ngreater than 50\\% to energy flows within the local network, relative to regular\ndistribution network tariffs. The policy implication of these findings is that\nthe, historically contentious, issue of network tariff reform in the presence\nof local solar power generation can be resolved with a mutually beneficial\narrangement of local network tariffs and CES. Furthermore, the challenge of\nsetting appropriate tariffs is eased through clear and intuitive conditions on\nlocal network tariff pricing.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Some Statistics on Women in Lattice QCD: We present a sampling of analyses concerning the gender ratio of plenary\nspeakers during the years 2000--2016 and make comparisons with other\nconferences, such as the APS April meeting. We hope this will invite discussion\nof ideas for how to make our field more accessible to women and minorities. We\nare preparing for an in-depth survey of the lattice field and welcome any ideas\nor suggestions. To leave post-conference comments and read about news affecting\nwomen in our field, see our Facebook page: https://www.facebook.com/WLQCD",
        "category": "physics_soc-ph"
    },
    {
        "text": "Epidemic model on a network: analysis and applications to COVID-19: We analyze an epidemic model on a network consisting of\nsusceptible-infected-recovered equations at the nodes coupled by diffusion\nusing a graph Laplacian. We introduce an epidemic criterion and examine\ndifferent vaccination/containment strategies: we prove that it is most\neffective to vaccinate a node of highest degree. The model is also useful to\nevaluate deconfinement scenarios and prevent a so-called second wave. The model\nhas few parameters enabling fitting to the data and the essential ingredient of\nimportation of infected; these features are particularly important for the\ncurrent COVID-19 epidemic.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Emergence of scaling in human-interest dynamics: Human behaviors are often driven by human interests. Despite intense recent\nefforts in exploring the dynamics of human behaviors, little is known about\nhuman-interest dynamics, partly due to the extreme difficulty in accessing the\nhuman mind from observations. However, the availability of large-scale data,\nsuch as those from e-commerce and smart-phone communications, makes it possible\nto probe into and quantify the dynamics of human interest. Using three\nprototypical \"big data\" sets, we investigate the scaling behaviors associated\nwith human-interest dynamics. In particular, from the data sets we uncover\npower-law scaling associated with the three basic quantities: (1) the length of\ncontinuous interest, (2) the return time of visiting certain interest, and (3)\ninterest ranking and transition. We argue that there are three basic\ningredients underlying human-interest dynamics: preferential return to\npreviously visited interests, inertial effect, and exploration of new\ninterests. We develop a biased random-walk model, incorporating the three\ningredients, to account for the observed power-law scaling relations. Our study\nrepresents the first attempt to understand the dynamical processes underlying\nhuman interest, which has significant applications in science and engineering,\ncommerce, as well as defense, in terms of specific tasks such as recommendation\nand human-behavior prediction.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Constructing Laplacian matrices with Soules vectors: inverse eigenvalue\n  problem and applications: The symmetric nonnegative inverse eigenvalue problem (SNIEP) asks which sets\nof numbers (counting multiplicities) can be the eigenvalues of a symmetric\nmatrix with nonnegative entries. While examples of such matrices are abundant\nin linear algebra and various applications, this question is still open for\nmatrices of dimension $N\\geq 5$. One of the approaches to solve the SNIEP was\nproposed by George W. Soules, relying on a specific type of eigenvectors\n(Soules vectors) to derive sufficient conditions for this problem. Elsner et\nal. later showed a canonical way to construct all Soules vectors, based on\nbinary rooted trees. While Soules vectors are typically treated as a totally\nordered set of vectors, we propose in this article to consider a relaxed\nalternative: a partially ordered set of Soules vectors. We show that this\nperspective enables a more complete characterization of the sufficient\nconditions for the SNIEP. In particular, we show that the set of eigenvalues\nthat satisfy these sufficient conditions is a convex cone, with symmetries\ncorresponding to the automorphisms of the binary rooted tree from which the\nSoules vectors were constructed. As a second application, we show how Soules\nvectors can be used to construct graph Laplacian matrices with a given spectrum\nand describe a number of interesting connections with the concepts of\nhierarchical random graphs, equitable partitions and effective resistance.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Scaling: Lost in the smog: In this commentary we discuss the validity of scaling laws and their\nrelevance for understanding urban systems and helping policy makers. We show\nhow the recent controversy about the scaling of CO2 transport-related emissions\nwith population size, where different authors reach contradictory conclusions,\nis symptomatic of the lack of understanding of the underlying mechanisms. In\nparticular, we highlight different sources of errors, ranging from incorrect\nestimate of CO2 to problems related with the definition of cities. We argue\nhere that while data are necessary to build of a new science of cities, they\nare not enough: they have to go hand in hand with a theoretical understanding\nof the main processes. This effort of building models whose predictions agree\nwith data is the prerequisite for a science of cities. In the meantime, policy\nadvice are, at best, a shot in the dark.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Uncovering the socioeconomic facets of human mobility: Given the rapid recent trend of urbanization, a better understanding of how\nurban infrastructure mediates socioeconomic interactions and economic systems\nis of vital importance. While the accessibility of location-enabled devices as\nwell as large-scale datasets of human activities, has fueled significant\nadvances in our understanding, there is little agreement on the linkage between\nsocioeconomic status and its influence on movement patterns, in particular, the\nrole of inequality. Here, we analyze a heavily aggregated and anonymized\nsummary of global mobility and investigate the relationships between\nsocioeconomic status and mobility across a hundred cities in the US and Brazil.\nWe uncover two types of relationships, finding either a clear connection or\nlittle-to-no interdependencies. The former tend to be characterized by low\nlevels of public transportation usage, inequitable access to basic amenities\nand services, and segregated clusters of communities in terms of income, with\nthe latter class showing the opposite trends. Our findings provide useful\nlessons in designing urban habitats that serve the larger interests of all\ninhabitants irrespective of their economic status.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Human migration patterns in large scale spatial with the resume data: Researches on the human mobility have made great progress in many aspects,\nbut the long-term and long-distance migration behavior is lack of in-depth and\nextensive research because of the difficult in accessing to household data. In\nthis paper, we use the resume data to discover the human migration behavior on\nthe large scale scope. It is found that the asymmetry in the flow structure\nwhich reflects the influence of population competition is caused by the\ndifference of attractiveness among cities. This flow structure can be\napproximately described by the gravity model of spatial economics. Besides, the\nvalue of scaling exponent of distance function in the gravity model is less\nthan the value of short-term travel behavior. It means that, compared with the\nshort-term travel behavior, the long-term human migration behavior is less\nsensitive. Moreover, the scaling coefficients of each variable in the gravity\nmodel are investigated. The result shows that the economic level is a mainly\nfactor on the migration.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Sensitivity of Electric Power Infrastructure Resilience to the\n  Spatial Distribution of Disaster Impacts: Credibly assessing the resilience of energy infrastructure in the face of\nnatural disasters is a salient concern facing researchers, government\nofficials, and community members. Here, we explore the influence of the spatial\ndistribution of disruptions due to hurricanes and other natural hazards on the\nresilience of power distribution systems. We find that incorporating\ninformation about the spatial distribution of disaster impacts has significant\nimplications for estimating infrastructure resilience. Specifically, the\nuncertainty associated with estimated infrastructure resilience metrics to\nspatially distributed disaster-induced disruptions is much higher than\ndetermined by previous methods. We present a case study of an electric power\ndistribution grid impacted by a major landfalling hurricane. We show that\nimproved characterizations of disaster disruption drastically change the way in\nwhich the grid recovers, including changes in emergent system properties such\nas antifragility. Our work demonstrates that previous methods for estimating\ncritical infrastructure resilience may be overstating the confidence associated\nwith estimated network recoveries due to the lack of consideration of the\nspatial structure of disruptions.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Simple Derivation of the Lifetime and the Distribution of Faces for a\n  Binary Subdivision Model: The iterative random subdivision of rectangles is used as a generation model\nof networks in physics, computer science, and urban planning. However, these\nresearches were independent. We consider some relations in them, and derive\nfundamental properties for the average lifetime depending on birth-time and the\nbalanced distribution of rectangle faces.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The maximum capability of a topological feature in link prediction: Link prediction aims to predict links of a network that are not directly\nvisible, with profound applications in biological and social systems. Despite\nintensive utilization of the topological feature in this task, it is unclear to\nwhat extent a particular feature can be leveraged to infer missing links. Here,\nwe show that the maximum capability of a topological feature follows a simple\nmathematical expression, which is independent of how an index gauges the\nfeature. Hence, a family of indexes associated with one topological feature\nshares the same performance limit. A feature's capability is lifted in the\nsupervised prediction, which in general gives rise to better results compared\nwith unsupervised prediction. The universality of the pattern uncovered is\nempirically verified by 550 structurally diverse networks, which can be applied\nto feature selection and the analysis of network characteristics associated\nwith a topological feature in link prediction.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Impact of centrality on cooperative processes: The solution of today's complex problems requires the grouping of task forces\nwhose members are usually connected remotely over long physical distances and\ndifferent time zones. Hence, understanding the effects of imposed communication\npatterns (i.e., who can communicate with whom) on group performance is\nimportant. Here, we use an agent-based model to explore the influence of the\nbetweenness centrality of the nodes on the time the group requires to find the\nglobal maxima of NK-fitness landscapes. The agents cooperate by broadcasting\nmessages, informing on their fitness to their neighbors, and use this\ninformation to copy the more successful agents in their neighborhood. We find\nthat for easy tasks (smooth landscapes), the topology of the communication\nnetwork has no effect on the performance of the group, and that the more\ncentral nodes are the most likely to find the global maximum first. For\ndifficult tasks (rugged landscapes), however, we find a positive correlation\nbetween the variance of the betweenness among the network nodes and the group\nperformance. For these tasks, the performances of individual nodes are strongly\ninfluenced by the agents dispositions to cooperate and by the particular\nrealizations of the rugged landscapes.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Phase transition in a sexual age-structured model of learning foreign\n  languages: The understanding of language competition helps us to predict extinction and\nsurvival of languages spoken by minorities. A simple agent-based model of a\nsexual population, based on the Penna model, is built in order to find out\nunder which circumstances one language dominates other ones. This model\nconsiders that only young people learn foreign languages. The simulations show\na first order phase transition where the ratio between the number of speakers\nof different languages is the order parameter and the mutation rate is the\ncontrol one.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Rapid decay in the relative efficiency of quarantine to halt epidemics\n  in networks: Several recent studies have tackled the issue of optimal network immunization\nby providing efficient criteria to identify key nodes to be removed in order to\nbreak apart a network, thus preventing the occurrence of extensive epidemic\noutbreaks. Yet, although the efficiency of those criteria has been demonstrated\nalso in empirical networks, preventive immunization is rarely applied to\nreal-world scenarios, where the usual approach is the a posteriori attempt to\ncontain epidemic outbreaks using quarantine measures. Here we compare the\nefficiency of prevention with that of quarantine in terms of the tradeoff\nbetween the number of removed and saved nodes on both synthetic and empirical\ntopologies. We show how, consistent with common sense, but contrary to common\npractice, in many cases preventing is better than curing: depending on network\nstructure, rescuing an infected network by quarantine could become inefficient\nsoon after the first infection.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Agent-Level Pandemic Simulation (ALPS) for Analyzing Effects of Lockdown\n  Measures: This paper develops an agent-level simulation model, termed ALPS, for\nsimulating the spread of an infectious disease in a confined community. The\nmechanism of transmission is agent-to-agent contact, using parameters reported\nfor Corona COVID-19 pandemic. The main goal of the ALPS simulation is analyze\neffects of preventive measures -- imposition and lifting of lockdown norms --\non the rates of infections, fatalities and recoveries. The model assumptions\nand choices represent a balance between competing demands of being realistic\nand being efficient for real-time inferences. The model provides quantification\nof gains in reducing casualties by imposition and maintenance of restrictive\nmeasures in place.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Future of Nuclear Energy: Facts and Fiction Chapter II: What is\n  known about Secondary Uranium Resources?: During 2009 nuclear power plants, with a capacity of 370 GWe, will produce\nroughly 14% of the worldwide electric energy. About 65000 tons of natural\nuranium equivalent are required to operate these reactors. For 15 years on\naverage only 2/3 of this fuel is provided by the uranium mines and 1/3 comes\nfrom secondary resources. In this paper the situation concerning the secondary\nresources at the beginning of the year 2009 is presented. The data used are\nfrom the IAEA/NEA 2007 Red Book, \"Uranium Resources, Production and Demand\",\nand from the World Nuclear Association (WNA).\n  Our analysis shows that these civilian stocks will be essentially exhausted\nwithin the next 5 years. This coincides roughly with the year 2013, when the\ndelivery of the 10000 tons of natural uranium equivalent from russian military\nstocks to the USA will end. As the majority of the remaining civilian stocks,\nabout 30000 tons, are believed to be under the control of the US government and\namerican companies, it seems rather unlikely that the USA will share their own\nstrategic uranium reserves with other large nuclear energy users. All data\nindicate that a uranium supply shortage in many OECD countries can only be\navoided if the remaining military uranium stocks from Russia and the USA,\nestimated to be roughly 500000 tons are made available to the other countries.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Jumping to male-dominated occupations: A novel way to reduce gender wage\n  gap for Chinese women: Occupational segregation is widely considered as one major reason leading to\nthe gender discrimination in labor market. Using large-scale Chinese resume\ndata of online job seekers, we uncover an interesting phenomenon that\noccupations with higher proportion of men have smaller gender wage gap measured\nby the female-male ratio on wage. We further show that the severity of\noccupational segregation in China is low both overall and regionally, and the\ninter-occupational discrimination is much smaller than the intra-occupational\ndiscrimination. That is to say, Chinese women do not face large barriers when\nchanging their occupations. Accordingly, we suggest Chineses women a new way to\nnarrow the gender wage gap: to join male-dominated occupations. Meanwhile, it\nis worth noticing that although the gender wage gap is smaller in\nmale-dominated occupations, it does not mean that the gender discrimination is\nsmaller there.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Generation and analysis of networks with a prescribed degree sequence\n  and subgraph family: Higher-order structure matters: Designing algorithms that generate networks with a given degree sequence\nwhile varying both subgraph composition and distribution of subgraphs around\nnodes is an important but challenging research problem. Current algorithms lack\ncontrol of key network parameters, the ability to specify to what subgraphs a\nnode belongs to, come at a considerable complexity cost or, critically, sample\nfrom a limited ensemble of networks. To enable controlled investigations of the\nimpact and role of subgraphs, especially for epidemics, neuronal activity or\ncomplex contagion, it is essential that the generation process be versatile and\nthe generated networks as diverse as possible. In this paper, we present two\nnew network generation algorithms that use subgraphs as building blocks to\nconstruct networks preserving a given degree sequence. Additionally, these\nalgorithms provide control over clustering both at node and global level. In\nboth cases, we show that, despite being constrained by a degree sequence and\nglobal clustering, generated networks have markedly different topologies as\nevidenced by both subgraph prevalence and distribution around nodes, and\nlarge-scale network structure metrics such as path length and betweenness\nmeasures. Simulations of standard epidemic and complex contagion models on\nthose networks reveal that degree distribution and global clustering do not\nalways accurately predict the outcome of dynamical processes taking place on\nthem. We conclude by discussing the benefits and limitations of both methods.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Detecting Core-Periphery Structures by Surprise: Detecting the presence of mesoscale structures in complex networks is of\nprimary importance. This is especially true for financial networks, whose\nstructural organization deeply affects their resilience to events like default\ncascades, shocks propagation, etc. Several methods have been proposed, so far,\nto detect communities, i.e. groups of nodes whose connectivity is significantly\nlarge. Communities, however do not represent the only kind of mesoscale\nstructures characterizing real-world networks: other examples are provided by\nbow-tie structures, core-periphery structures and bipartite structures. Here we\npropose a novel method to detect statistically-signifcant bimodular structures,\ni.e. either bipartite or core-periphery ones. It is based on a modification of\nthe surprise, recently proposed for detecting communities. Our variant allows\nfor bimodular nodes partitions to be revealed, by letting links to be placed\neither 1) within the core part and between the core and the periphery parts or\n2) just between the (empty) layers of a bipartite network. From a technical\npoint of view, this is achieved by employing a multinomial hypergeometric\ndistribution instead of the traditional (binomial) hypergeometric one; as in\nthe latter case, this allows a p-value to be assigned to any given\n(bi)partition of the nodes. To illustrate the performance of our method, we\nreport the results of its application to several real-world networks, including\nsocial, economic and financial ones.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Mean Field Games in the weak noise limit : A WKB approach to the\n  Fokker-Planck equation: Motivated by the study of a Mean Field Game toy model called the \"seminar\nproblem\", we consider the Fokker-Planck equation in the small noise regime for\na specific drift field. This gives us the opportunity to discuss the\napplication to diffusion problem of the WKB approach \"a la Maslov\", making it\npossible to solve directly the time dependant problem in an especially\ntransparent way.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Emergence of China as a Leading Nation in Science: China has become the fifth leading nation in terms of its share of the\nworld's scientific publications. The citation rate of papers with a Chinese\naddress for the corresponding author also exhibits exponential growth. More\nspecifically, China has become a major player in critical technologies like\nnanotechnology. Although it is difficult to delineate nanoscience and\nnanotechnology, we show that China has recently achieved a position second only\nto that of the USA. Funding for R&D has been growing exponentially, but since\n1997 even more in terms of business expenditure than in terms of government\nexpenditure. It seems that the Chinese government has effectively used the\npublic-sector research potential to boost the knowledge-based economy of the\ncountry. Thus, China may be achieving the (\"Lisbon\") objectives of the\ntransition to a knowledge-based economy more broadly and rapidly than its\nwestern counterparts. Because of the sustained increase in Chinese government\nfunding and the virtually unlimited reservoir of highly-skilled human\nresources, one may expect a continuation of this growth pattern in the near\nfuture.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Feyerabend and physics: Feyerabend frequently discussed physics. He also referred to the history of\nthe subject when motivating his philosophy of science. Alas, as some examples\nshow, his understanding of physics remained superficial. In this respect,\nFeyerabend is like Popper; the difference being his self-criticism later on,\nand the much more tolerant attitude toward the allowance of methods. Quite\ngenerally, partly due to the complexity of the formalism and the new challenges\nof their findings, which left philosophy proper at a loss, physicists have\nattempted to developed their own meaning of their subject. For instance, in\nrecent years, the interpretation of quantum mechanics has stimulated a new type\nof experimental philosophy, which seeks to operationalize emerging\nphilosophical issues; issues which are incomprehensible for most philosophers.\nIn this respect, physics often appears to be a continuation of philosophy by\nother means. Yet, Feyerabend has also expressed profound insights into the\npossibilities for the progress of physics, a legacy which remains to be\nimplemented in the times to come: the conquest of abundance, the richness of\nreality, the many worlds which still await discovery, and the vast openness of\nthe physical universe.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Engagement in the electoral processes: scaling laws and the role of the\n  political positions: We report on a statistical analysis of the engagement in the electoral\nprocesses of all Brazilian cities by considering the number of party\nmemberships and the number of candidates for mayor and councillor. By\ninvestigating the relationships between the number of party members and the\npopulation of voters, we have found that the functional form of these\nrelationships are well described by sub-linear power laws (allometric scaling)\nsurrounded by a multiplicative log-normal noise. We have observed that this\npattern is quite similar to those previously-reported for the relationships\nbetween the number candidates (mayor and councillor) and population of voters\n[EPL 96, 48001 (2011)], suggesting that similar universal laws may be ruling\nthe engagement in the electoral processes. We also note that the power law\nexponents display a clear hierarchy, where the more influential is the\npolitical position the smaller is the value of the exponent. We have also\ninvestigated the probability distributions of the number of candidates (mayor\nand councilor), party memberships and voters. The results indicate that the\nmost influential positions are characterized by distributions with very\nshort-tails, while less influential positions display an intermediate power law\ndecay before showing an exponential-like cutoff. We discuss that, in addition\nto the political power of the position, limitations in the number of available\nseats can also be connected with this changing of behavior. We further believe\nthat our empirical findings point out to an underrepresentation effect, where\nthe larger city is, the larger are the obstacles for more individuals to become\ndirectly engaged in the electoral process.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Passive Supporters of Terrorism and Phase Transitions: We discuss some social contagion processes to describe the formation and\nspread of radical opinions. The dynamics of opinion spread involves local\nthreshold processes as well as mean field effects. We calculate and observe\nphase transitions in the dynamical variables resulting in a rapidly increasing\nnumber of passive supporters. This strongly indicates that military solutions\nare inappropriate.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Crowdsourcing accurately and robustly predicts Supreme Court decisions: Scholars have increasingly investigated \"crowdsourcing\" as an alternative to\nexpert-based judgment or purely data-driven approaches to predicting the\nfuture. Under certain conditions, scholars have found that crowdsourcing can\noutperform these other approaches. However, despite interest in the topic and a\nseries of successful use cases, relatively few studies have applied empirical\nmodel thinking to evaluate the accuracy and robustness of crowdsourcing in\nreal-world contexts. In this paper, we offer three novel contributions. First,\nwe explore a dataset of over 600,000 predictions from over 7,000 participants\nin a multi-year tournament to predict the decisions of the Supreme Court of the\nUnited States. Second, we develop a comprehensive crowd construction framework\nthat allows for the formal description and application of crowdsourcing to\nreal-world data. Third, we apply this framework to our data to construct more\nthan 275,000 crowd models. We find that in out-of-sample historical\nsimulations, crowdsourcing robustly outperforms the commonly-accepted null\nmodel, yielding the highest-known performance for this context at 80.8% case\nlevel accuracy. To our knowledge, this dataset and analysis represent one of\nthe largest explorations of recurring human prediction to date, and our results\nprovide additional empirical support for the use of crowdsourcing as a\nprediction method.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Structure of n-clique networks embedded in a complex network: We propose the n-clique network as a powerful tool for understanding global\nstructures of combined highly-interconnected subgraphs, and provide theoretical\npredictions for statistical properties of the n-clique networks embedded in a\ncomplex network using the degree distribution and the clustering spectrum.\nFurthermore, using our theoretical predictions, we find that the statistical\nproperties are invariant between 3-clique networks and original networks for\nseveral observable real-world networks with the scale-free connectivity and the\nhierarchical modularity. The result implies that structural properties are\nidentical between the 3-clique networks and the original networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Improving power-grid systems via topological changes, or how\n  self-organized criticality can help stability: Cascade failures in power grids occur when the failure of one component or\nsubsystem causes a chain reaction of failures in other components or\nsubsystems, ultimately leading to a widespread blackout or outage. Controlling\ncascade failures on power grids is important for many reasons like economic\nimpact, national security, public safety and even rippled effects like\ntroubling transportation systems. Monitoring the networks on node level has\nbeen suggested by many, either controlling all nodes of a network or by\nsubsets. This study identifies sensitive graph elements of the weighted\nEuropean power-grids (from 2016, 2022) by two different methods. Bridges are\ndetermined between communities and \"weak\" nodes are selected by the lowest\nlocal synchronization of the swing equation. In the latter case we add bypasses\nof the same number as the bridges at weak nodes, and we compare the\nsynchronization, cascade failure behavior by the dynamical improvement with the\npurely topological changes. The results are also compared if bridges are\nremoved from networks, which results in a case similar to islanding, and with\nthe addition of links at randomly selected places. Bypassing was found to\nimprove synchronization the best, while the average cascade sizes are the\nlowest with bridge additions. However, for very large or small global couplings\nthese network changes do not help, they seem to be useful near the\nsynchronization transition region, where self-organization drives the\npower-grid. Thus, we provide a demonstration for the Braess' Paradox on\ncontinent-sized power grid simulations and uncover the limitations of this\nphenomenon. We also determine the cascade size distributions and justify the\npower-law tails near the transition point on these grids.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A paradox in community detection: Recent research has shown that virtually all algorithms aimed at the\nidentification of communities in networks are affected by the same main\nlimitation: the impossibility to detect communities, even when these are\nwell-defined, if the average value of the difference between internal and\nexternal node degrees does not exceed a strictly positive value, in literature\nknown as detectability threshold. Here, we counterintuitively show that the\nvalue of this threshold is inversely proportional to the intrinsic quality of\ncommunities: the detection of well-defined modules is thus more difficult than\nthe identification of ill-defined communities.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Impact of Technologies in Political Campaigns: Recent political campaigns have demonstrated how technologies are used to\nboost election outcomes by microtargeting voters. We propose and analyze a\nframework which analyzes how political activists use technologies to target\nvoters. Voters are represented as nodes of a network. Political activists reach\nout locally to voters and try to convince them. Depending on their\ntechnological advantage and budget, political activists target certain regions\nin the network where their activities are able to generate the largest\nvote-share gains. Analytically and numerically, we quantify vote-share gains\nand savings in terms of budget and number of activists from employing superior\ntargeting technologies compared to traditional campaigns. Moreover, we\ndemonstrate that the technological precision must surpass a certain threshold\nin order to lead to a vote-share gain or budget advantage. Finally, by\ncalibrating the technology parameters to the recent U.S. presidential election,\nwe show that a pure targeting technology advantage is consistent with Trump\nwinning against Clinton.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Inequality is rising where social network segregation interacts with\n  urban topology: Social networks amplify inequalities due to fundamental mechanisms of social\ntie formation such as homophily and triadic closure. These forces sharpen\nsocial segregation reflected in network fragmentation. Yet, little is known\nabout what structural factors facilitate fragmentation. In this paper we use\nbig data from a widely-used online social network to demonstrate that there is\na significant relationship between social network fragmentation and income\ninequality in cities and towns. We find that the organization of the physical\nurban space has a stronger relationship with fragmentation than unequal access\nto education, political segregation, or the presence of ethnic and religious\nminorities. Fragmentation of social networks is significantly higher in towns\nin which residential neighborhoods are divided by physical barriers such as\nrivers and railroads and are relatively distant from the center of town. Towns\nin which amenities are spatially concentrated are also typically more socially\nsegregated. These relationships suggest how urban planning may be a useful\npoint of intervention to mitigate inequalities in the long run.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Hydrodynamic models of preference formation in multi-agent societies: In this paper, we discuss the passage to hydrodynamic equations for kinetic\nmodels of opinion formation. The considered kinetic models feature an opinion\ndensity depending on an additional microscopic variable, identified with the\npersonal preference. This variable describes an opinion-driven polarisation\nprocess, leading finally to a choice among some possible options, as it happens\ne.g. in referendums or elections. Like in the kinetic theory of rarefied gases,\nthe derivation of hydrodynamic equations is essentially based on the\ncomputation of the local equilibrium distribution of the opinions from the\nunderlying kinetic model. Several numerical examples validate the resulting\nmodel, shedding light on the crucial role played by the distinction between\nopinion and preference formation on the choice processes in multi-agent\nsocieties.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Regrets, learning and wisdom: This contribution discusses in what respect Econophysics may be able to\ncontribute to the rebuilding of economics theory. It focuses on aggregation,\nindividual vs collective learning and functional wisdom of the crowds.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Initial growth rates of malware epidemics fail to predict their reach: Empirical studies show that epidemiological models based on an epidemic's\ninitial spread rate often fail to predict the true scale of that epidemic. Most\nepidemics with a rapid early rise die out before affecting a significant\nfraction of the population, whereas the early pace of some pandemics is rather\nmodest. Recent models suggest that this could be due to the heterogeneity of\nthe target population's susceptibility. We study a computer malware ecosystem\nexhibiting spread mechanisms resembling those of biological systems while\noffering details unavailable for human epidemics. Rather than comparing models,\nwe directly estimate reach from a new and vastly more complete data from a\nparallel domain, that offers superior details and insight as concerns\nbiological outbreaks. We find a highly heterogeneous distribution of computer\nsusceptibilities, with nearly all outbreaks initially over-affecting the tail\nof the distribution, then collapsing quickly once this tail is depleted. This\nmechanism restricts the correlation between an epidemic's initial growth rate\nand its total reach, thus preventing the majority of epidemics, including\ninitially fast-growing outbreaks, from reaching a macroscopic fraction of the\npopulation. The few pervasive malwares distinguish themselves early on via the\nfollowing key trait: they avoid infecting the tail, while preferentially\ntargeting computers unaffected by typical malware.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Agent dynamics in kinetic models of wealth exchange: We study the dynamics of individual agents in some kinetic models of wealth\nexchange, particularly, the models with savings. For the model with uniform\nsavings, agents perform simple random walks in the \"wealth space\". On the other\nhand, we observe ballistic diffusion in the model with distributed savings.\nThere is an associated skewness in the gain-loss distribution which explains\nthe steady state behavior in such models. We find that in general an agent\ngains while interacting with an agent with a larger saving propensity.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Systemic risk measured by systems resiliency to initial shocks: The study of systemic risk is often presented through the analysis of several\nmeasures referring to quantities used by practitioners and policy makers.\nAlmost invariably, those measures evaluate the size of the impact that\nexogenous events can exhibit on a financial system without analysing the nature\nof initial shock. Here we present a symmetric approach and propose a set of\nmeasures that are based on the amount of exogenous shock that can be absorbed\nby the system before it starts to deteriorate. For this purpose, we use a\nlinearized version of DebtRank that allows to clearly show the onset of\nfinancial distress towards a correct systemic risk estimation. We show how we\ncan explicitly compute localized and uniform exogenous shocks and explained\ntheir behavior though spectral graph theory. We also extend analysis to\nheterogeneous shocks that have to be computed by means of Monte Carlo\nsimulations. We believe that our approach is more general and natural and\nallows to express in a standard way the failure risk in financial systems.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Geometry of Crashes - A Measure of the Dynamics of Stock Market\n  Crises: This paper investigates the dynamics of stocks in the S&P500 index for the\nlast 30 years. Using a stochastic geometry technique, we investigate the\nevolution of the market space and define a new measure for that purpose, which\nis a robust index of the dynamics of the market structure and provides\ninformation on the intensity and the sectoral impact of the crises. With this\nmeasure, we analyze the effects of some extreme phenomena on the geometry of\nthe market. Nine crashes between 1987 and 2001 are compared by looking at the\nway they modify the shape of the manifold that describes the S&P500 market\nspace. These crises are identified as (a) structural, (b) general and (c)\nlocal.",
        "category": "physics_soc-ph"
    },
    {
        "text": "How political parties adjust to fixed voter opinions: We propose a new version of the spatial model of voting. Platforms of five\nparties are evolving in a two-dimensional landscape of political issues so as\nto get maximal numbers of voters. For a Gaussian landscape the evolution leads\nto a spatially symmetric state, where the platform centers form a pentagon\naround the Gaussian peak. For a bimodal landscape the platforms located at\ndifferent peaks get different numbers of voters.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Reconstructing social sensitivity from evolution of content volume in\n  Twitter: We set up a simple mathematical model for the dynamics of public interest in\nterms of media coverage and social interactions. We test the model on a series\nof events related to violence in the US during 2020, using the volume of tweets\nand retweets as a proxy of public interest, and the volume of news as a proxy\nof media coverage. The model succesfully fits the data and allows inferring a\nmeasure of social sensibility that correlates with human mobility data. These\nfindings suggest the basic ingredients and mechanisms that regulate social\nresponses capable of ignite social mobilizations.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Cliophysics: Socio-political Reliability Theory, Polity Duration and\n  African Political (In)stabilities: Quantification of historical sociological processes have recently gained\nattention among theoreticians in the effort of providing a solid theoretical\nunderstanding of the behaviors and regularities present in sociopolitical\ndynamics. Here we present a reliability theory of polity processes with\nemphases on individual political dynamics of African countries. We found that\nthe structural properties of polity failure rates successfully capture the risk\nof political vulnerability and instabilities in which 87.50%, 75%, 71.43%, and\n0% of the countries with monotonically increasing, unimodal, U-shaped and\nmonotonically decreasing polity failure rates, respectively, have high level of\nstate fragility indices. The quasi-U-shape relationship between average polity\nduration and regime types corroborates historical precedents and explains the\nstability of the autocracies and democracies.",
        "category": "physics_soc-ph"
    },
    {
        "text": "An analysis of gamma-ray data collected at traffic intersections in\n  Northern Virginia: Gamma-ray spectral data were collected from sensors mounted to traffic\nsignals around Northern Virginia. The data were collected over a span of\napproximately fifteen months. A subset of the data were analyzed manually and\nsubsequently used to train machine-learning models to facilitate the evaluation\nof the remaining 50k anomalous events identified in the dataset. We describe\nthe analysis approach used here and discuss the results in terms of\nradioisotope classes and frequency patterns over day-of-week and time-of-day\nspans. Data from this work has been archived and is available for future and\nongoing research applications.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Self-sustained nonlinear waves in traffic flow: In analogy to gas-dynamical detonation waves, which consist of a shock with\nan attached exothermic reaction zone, we consider herein nonlinear traveling\nwave solutions, termed \"jamitons,\" to the hyperbolic (\"inviscid\") continuum\ntraffic equations. Generic existence criteria are examined in the context of\nthe Lax entropy conditions. Our analysis naturally precludes traveling wave\nsolutions for which the shocks travel downstream more rapidly than individual\nvehicles. Consistent with recent experimental observations from a periodic\nroadway (Sugiyama et al., New Journal of Physics, 10, 2008), our numerical\ncalculations show that, under appropriate road conditions, jamitons are\nattracting solutions, with the time evolution of the system converging towards\na jamiton-dominated configuration. Jamitons are characterized by a sharp\nincrease in density over a relatively compact section of the roadway.\nApplications of our analysis to traffic modeling and control are examined by\nway of a detailed example.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Age-specific contacts and travel patterns in the spatial spread of 2009\n  H1N1 influenza pandemic: Confirmed cases during the early stage of the 2009 H1N1 pdm in various\ncountries showed an age shift between importations and local transmission\ncases, with adults mainly responsible for seeding unaffected regions and\nchildren most frequently driving community outbreaks. We introduce a multi-host\nstochastic metapopulation model with two age classes to analytically address\nthe role of a heterogeneously mixing population and its associated\nnon-homogeneous travel behaviors on the risk of a major epidemic. We inform the\nmodel with statistics on demography, mixing and travel behavior for Europe and\nMexico, and calibrate it to the 2009 H1N1 pdm early outbreak. We varied model\nparameters to explore the invasion conditions under different scenarios. We\nderive the expression for the global invasion potential of the epidemic that\ndepends on disease transmissibility, transportation network and mobility\nfeatures, demographic profile and mixing pattern. Highly assortative mixing\nfavor the spatial containment of the epidemic, this effect being contrasted by\nan increase in the social activity of adults vs. children. Heterogeneity of the\nmobility network topology and traffic flows strongly favor the disease\ninvasion, as also a larger fraction of children traveling. Variations in the\ndemography and mixing habits across countries lead to heterogeneous outbreak\nsituations. Results are compatible with the H1N1 spatial spread observed. The\nwork illustrates the importance of age-dependent mixing profiles and mobility\nfeatures in the study of the conditions for the spatial invasion of an emerging\ninfluenza pandemic. Its results allow the immediate assessment of the risk of a\nmajor epidemic for a specific scenario upon availability of data, and the\nevaluation of the effectiveness of public health interventions targeting\nspecific age groups, their interactions and mobility behaviors.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Bribery Games on Interdependent Complex Networks: Bribe demands present a social conflict scenario where decisions have\nwide-ranging economic and ethical consequences. Nevertheless, such incidents\noccur daily in many countries across the globe. Harassment bribery constitute a\nsignificant sub-set of such bribery incidents where a government official\ndemands a bribe for providing a service to a citizen legally entitled to it. We\nemploy an evolutionary game-theoretic framework to analyse the evolution of\ncorrupt and honest strategies in structured populations characterized by an\ninterdependent complex network. The effects of changing network topology,\naverage number of links and asymmetry in size of the citizen and officer\npopulation on the proliferation of incidents of bribery are explored. A complex\nnetwork topology is found to be beneficial for the dominance of corrupt\nstrategies over a larger region of phase space when compared with the outcome\nfor a regular network, for equal citizen and officer population sizes. However,\nthe extent of the advantage depends critically on the network degree and\ntopology. A different trend is observed when there is a difference between the\ncitizen and officer population sizes. Under those circumstances, increasing\nrandomness of the underlying citizen network can be beneficial to the fixation\nof honest officers up to a certain value of the network degree. Our analysis\nreveals how the interplay between network topology, connectivity and strategy\nupdate rules can affect population level outcomes in such asymmetric games.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Intra-community link formation and modularity in ultracold growing\n  hyperbolic networks: Hyperbolic network models, centered around the idea of placing nodes at\nrandom in a hyperbolic space and drawing links according to a probability that\ndecreases as a function of the distance, provide a simple, yet also very\ncapable framework for grasping the small-world, scale-free, highly clustered\nand modular nature of complex systems that are often referred to as real-world\nnetworks. In the present work we study the community structure of networks\ngenerated by the Popularity Similarity Optimization model (corresponding to one\nof the fundamental, widely known hyperbolic models) when the temperature\nparameter (responsible for tuning the clustering coefficient) is set to the\nlimiting value of zero. By focusing on the intra-community link formation we\nderive analytical expressions for the expected modularity of a partitioning\nconsisting of equally sized angular sectors in the native disk representation\nof the 2d hyperbolic space. Our formulas improve earlier results to a great\nextent, being able to estimate the average modularity (measured by numerical\nsimulations) with high precision in a considerably larger range both in terms\nof the model parameters and also the relative size of the communities with\nrespect to the entire network. These findings enhance our comprehension of how\nmodules form in hyperbolic networks. The existence of these modules is somewhat\nunexpected, given the absence of explicit community formation steps in the\nmodel definition.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Continuous measurements of real-life bidirectional pedestrian flows on a\n  wide walkway: Employing partially overlapping overhead \\kinectTMS sensors and automatic\npedestrian tracking algorithms we recorded the crowd traffic in a rectilinear\nsection of the main walkway of Eindhoven train station on a 24/7 basis. Beside\ngiving access to the train platforms (it passes underneath the railways), the\nwalkway plays an important connection role in the city. Several crowding\nscenarios occur during the day, including high- and low-density dynamics in\nuni- and bi-directional regimes. In this paper we discuss our recording\ntechnique and we illustrate preliminary data analyses. Via fundamental\ndiagrams-like representations we report pedestrian velocities and fluxes vs.\npedestrian density. Considering the density range $0$ - $1.1\\,$ped/m$^2$, we\nfind that at densities lower than $0.8\\,$ped/m$^2$ pedestrians in\nunidirectional flows walk faster than in bidirectional regimes. On the\nopposite, velocities and fluxes for even bidirectional flows are higher above\n$0.8\\,$ped/m$^2$.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Identifying the structure patterns to govern the performance of\n  localization in regulating innovation diffusion: The macro social influence is recognized as a non-negligible ingredient in\ninnovation propagation: more adopters in the network lead to a higher adoption\ntendency for the rest individuals. A recent study to incorporate such a crucial\nmechanism shows that sufficient intensity of macro-level social influence can\ncause a change from a continuous to discontinuous transition, further\nindicating the existence of a tricritical point. Although network localization\nstrength determines the tricritical point, it remains unclear what network\nquantities govern the performance of localization in regulating innovation\ndiffusion. To address this issue, we herein consider the model incorporating\nboth the micro- and macro-levels social influence. We present a dynamic\nmessage-passing method to analytically treat both the outbreak threshold and\nrecovered population, and validate the predictions through agent-based\nsimulations. Extensive analysis on the classical synthetic networks shows that\nsparsely available connections, and relatively heterogeneous degree\ndistribution, either assortative or extremely disassortative configurations are\nfavorable for continuous transition. In such cases, the employed network can\nyield a strong localization effect so that the innovation is trapped in the\nconfigurations composed of the hubs with high non-backtracking centrality. We\nfurther explore the dependence of both tricritical point and localization\nstrength on three structural quantities: network density, heterogeneity, and\nassortativity, which gives a clear physical picture of the joint effects of the\nthree structure quantities on the localization strength. Finally, we conclude\nthat the core-periphery structure, being sensitive to the change of the three\nstructure quantities, essentially determines localization strength, and further\nregulates the phase transition.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Sociophysics Simulations IV: Hierarchies of Bonabeau et al: The model of Bonabeau et al explains social hierarchies as random: People\nkeep a memory of recent fights, and winners have a higher probability to win\nagain. The question of phase transition and the generalization from square\nlattices to networks is reviewed here.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Social Network Analysis: Bibliographic Network Analysis of the Field and\n  its Evolution / Part 1. Basic Statistics and Citation Network Analysis: In this paper, we present the results of the study on the development of\nsocial network analysis (SNA) discipline and its evolution over time, using the\nanalysis of bibliographic networks. The dataset consists of articles from the\nWeb of Science Clarivate Analytics database and those published in the main\njournals in the field (70,000+ publications), created by searching for the key\nword \"social network*.\" From the collected data, we constructed several\nnetworks (citation and two-mode, linking publications with authors, keywords\nand journals). Analyzing the obtained networks, we evaluated the trends in the\nfield`s growth, noted the most cited works, created a list of authors and\njournals with the largest amount of works, and extracted the most often used\nkeywords in the SNA field. Next, using the Search path count approach, we\nextracted the main path, key-route paths and link islands in the citation\nnetwork. Based on the probabilistic flow node values, we identified the most\nimportant articles. Our results show that authors from the social sciences, who\nwere most active through the whole history of the field development,\nexperienced the \"invasion\" of physicists from 2000's. However, starting from\nthe 2010's, a new very active group of animal social network analysis has\nemerged.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Sparse game changers restore collective motion in panicked human crowds: Using a dynamic variant of the Vicsek model, we show that emergence of a\ncrush from an orderly moving human crowd is a non-equilibrium first order phase\ntransition. We also show that this transition can be reversed by modifying the\ndynamics of a few people, deemed as game changers. Surprisingly, the optimal\nplacement of these game changers is found to be in regions of maximum local\ncrowd speed. The presence of such game changers is effective owing to the\ndiscontinuous nature of the underlying phase transition. Thus our generic\napproach provides (i) strategies to delay crush formation and (ii) paths to\nrecover from a crush, two aspects that are of paramount importance in\nmaintaining safety of mass gatherings of people.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Performance assessment of vehicle interdiction strategies in a\n  simulation based environment on a complex transportation network: We consider the escape interdiction problem in a transportation network. In\nthe absence of traffic in the network, the criminal/attacker tries to escape\nfrom the city using any of the shortest paths from the crime scene to any\nrandomly chosen exit point. In the presence of traffic, the attacker chooses\nthe optimal path, which takes minimum time to reach his destination. On the\ncontrary, police/defenders try to interdict the criminal on his escape route.\nThis is a challenging task for police with limited resources. Again, a real\ncity road network is also complex in nature. First, we develop a\nsimulation-based model for the optimal allocation of resources using the SUMO\nsimulator. Next, we focus on developing a more advanced search strategy like\nrouting with optimal resource allocation. We develop a novel meta-heuristic\napproach in a simulation environment to interdict the attacker in a dynamic\ncrime scenario. Like the previous approach, the attacker follows the path with\noptimal time to escape from the city. In contrast, defenders try to catch the\nattacker regardless of the path which the attacker takes. The defenders aim is\nto maximize the interdiction probability. As time plays a vital role, we choose\na meta-heuristic approach to provide quality solutions in a time-efficient\nmanner. We test the developed meta-heuristic with a case study on the IIT\nKharagpur map, India. We analyze the performance of the mentioned approaches\nusing the SUMO simulator both in the presence of traffic and without traffic.\nWe develop a linear regression model to generate optimal path in the presence\nof traffic. Here traffic is generated randomly in the whole network using the\nSUMO simulator.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A Rational Indicator of Scientific Creativity: A model is proposed for the creation and transmission of scientific\nknowledge, based on the network of citations among research articles. The model\nallows to assign to each article a nonnegative value for its creativity, i. e.\nits creation of new knowledge. If the entire publication network is truncated\nto the first neighbors of an article (the n references that it makes and the m\ncitations that it receives), its creativity value becomes a simple function of\nn and m. After splitting the creativity of each article among its authors, the\ncumulative creativity of an author is then proposed as an indicator of her or\nhis merit of research. In contrast with other merit indicators, this creativity\nindex yields similar values for the top scientists in two very different areas\n(life sciences and physics), thus offering good promise for interdisciplinary\nanalyses.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Growth principles of natural hypergraphs: Several systems can be represented by hypergraphs, an extension of graphs\nwith associations between any number of vertices. These natural hypergraphs doe\nnot appear at once. They are generated by some dynamical process of hypergraph\nevolution. Here I investigate what are the minimal growth principles of natural\nhypergraphs. I postulate edge duplication and vertex addition at edge\nduplications as the key principles of hypergraph growth. The implementation of\nthese two principles induce the emergence of preferential attachment, power law\ndegree distribution, the small-world property, high clustering coefficient and\nthe founder effect. This work clarifies the distinction between principles,\nemergent properties and context specific details in the context of hypergraph\ngrowth dynamics.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Risk portofolio management under Zipf analysis based strategies: A so called Zipf analysis portofolio management technique is introduced in\norder to comprehend the risk and returns. Two portofoios are built each from a\nwell known financial index. The portofolio management is based on two\napproaches: one called the \"equally weighted portofolio\", the other the\n\"confidence parametrized portofolio\". A discussion of the (yearly) expected\nreturn, variance, Sharpe ratio and $\\beta$ follows. Optimization levels of high\nreturns or low risks are found.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Paradoxes in the co-evolution of contagions and institutions: Epidemic models study the spread of an undesired agent through a population,\nbe it infectious diseases through a country, misinformation in online social\nmedia, or pests infesting a region. In combating these epidemics, we rely\nneither on global top-down interventions, nor solely on individual adaptations.\nInstead, interventions most commonly come from local institutions such as\npublic health departments, moderation teams on social media platforms, or other\nforms of group governance. Classic models, which are often individual or\nagent-based, are ill-suited to capture local adaptations. We leverage recent\ndevelopment of institutional dynamics based on cultural group selection to\nstudy how groups can attempt local control of an epidemic by taking inspiration\nfrom the successes and failures of other groups. Incorporating these\ninstitutional changes into the epidemic dynamics reveals paradoxes: a higher\ntransmission rate can result in smaller outbreaks and decreasing the speed of\ninstitutional adaptation generally reduces outbreak size. When groups perceive\na contagion as more worrisome, they can invest in improved policies and, if\nthey maintain these policies long enough to have impact, lead to a reduction in\nendemicity. By looking at the interplay between the speed of institutions and\nthe transmission rate of the contagions, we find rich co-evolutionary dynamics\nthat reflect the complexity of known biological and social contagions.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A computer simulation of language families: This paper presents Monte Carlo simulations of language populations and the\ndevelopment of language families, showing how a simple model can lead to\ndistributions similar to the ones observed empirically. The model used combines\nfeatures of two models used in earlier work by phycisists for the simulation of\ncompetition among languages: the \"Viviane\" model for the migration of people\nand propagation of languages and the \"Schulze\" model, which uses bitstrings as\na way of characterising structural features of languages.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Roughness and Finite Size Effect in the NYSE Stock-Price Fluctuations: We consider the roughness properties of NYSE (New York Stock Exchange)\nstock-price fluctuations. The statistical properties of the data are relatively\nhomogeneous within the same day but the large jumps between different days\nprevent the extension of the analysis to large times. This leads to intrinsic\nfinite size effects which alter the apparent Hurst (H) exponent. We show, by\nanalytical methods, that finite size effects always lead to an enhancement of\nH. We then consider the effect of fat tails on the analysis of the roughness\nand show that the finite size effects are strongly enhanced by the fat tails.\nThe non stationarity of the stock price dynamics also enhances the finite size\neffects which, in principle, can become important even in the asymptotic\nregime. We then compute the Hurst exponent for a set of stocks of the NYSE and\nargue that the interpretation of the value of H is highly ambiguous in view of\nthe above results. Finally we propose an alternative determination of the\nroughness in terms of the fluctuations from moving averages with variable\ncharacteristic times. This permits to eliminate most of the previous problems\nand to characterize the roughness in useful way. In particular this approach\ncorresponds to the automatic elimination of trends at any scale.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Second Parrondo's Paradox in Scale Free Networks: Parrondo's paradox occurs in sequences of games in which a winning\nexpectation value of a payoff may be obtained by playing two games in a random\norder, even though each game in the sequence may be lost when played\nindividually.Several variations of Parrondo's games apparently with the same\nparadoxical property have been introduced by G.P. Harmer and D. Abbott; history\ndependence, one dimensional line, two dimensional lattice and so on. I have\nshown that Parrondo's paradox does not occur in scale free networks in the\nsimplest case with the same number of parameters as the original Parrondo's\nparadox. It suggests that some technical complexities are needed to present\nParrondo's paradox in scale free networks. In this article, I show that a\nsimple modification with the same number of parameters as the original\nParrondo's paradox creates Parrondo's paradox in scale free. This paradox is,\nhowever, created by a quite different mechanism from the original Parrondo's\nparadox and a considerably rare phenomenon, where the discrete property of\ndegree of nodes is crucial. I call it the second Parrondo's paradox.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Heterogeneity in evolutionary games: an analysis of the risk perception: In this work, we analyse the relationship between heterogeneity and\ncooperation. Previous investigations suggest that this relation is nontrivial,\nas some authors found that heterogeneity sustains cooperation, while others\nobtained different results. Among the possible forms of heterogeneity, we focus\non the individual perception of risks and rewards related to a generic event,\nthat can show up in a number of social and biological systems. The modelling\napproach is based on the framework of Evolutionary Game Theory. To represent\nthis kind of heterogeneity, we implement small and local perturbations on the\npayoff matrix of simple 2-strategy games, as the Prisoner's Dilemma. So, while\nusually the payoff is considered as a global and time-invariant structure, i.e.\nit is the same for all individuals of a population at any time, in our model\nits value is continuously affected by small variations, both in time and space\n(i.e. position on a lattice). We found that such perturbations can be\nbeneficial or detrimental to cooperation, depending on their setting. Notably,\ncooperation is strongly supported when perturbations act on the main diagonal\nof the payoff matrix, whereas when they act on the off-diagonal the resulting\neffect is more difficult to quantify. To conclude, the proposed model shows a\nrich spectrum of possible equilibria, whose interpretation might offer insights\nand enrich the description of several systems.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Fragmentation transitions in a coevolving nonlinear voter model: We study a coevolving nonlinear voter model describing the coupled evolution\nof the states of the nodes and the network topology. Nonlinearity of the\ninteraction is measured by a parameter q. The network topology changes by\nrewiring links at a rate p. By analytical and numerical analysis we obtain a\nphase diagram in p, q parameter space with three different phases: Dynamically\nactive coexistence phase in a single component network, absorbing consensus\nphase in a single component network, and absorbing phase in a fragmented\nnetwork. For finite systems the active phase has a lifetime that grows\nexponentially with system size, at variance with the similar phase for the\nlinear voter model that has a lifetime proportional to system size. We find\nthree transition lines that meet at the point of the fragmentation transition\nof the linear voter model. A first transition line corresponds to a continuous\nabsorbing transition between the active and fragmented phases. The other two\ntransition lines are discontinuous transitions fundamentally different from the\ntransition of the linear voter model. One is a fragmentation transition between\nthe consensus and fragmented phases, and the other is an absorbing transition\nin a single component network between the active and consensus phases.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Prisoner's Dilemma cellular automata revisited: evolution of cooperation\n  under environmental pressure: We propose an extension of the evolutionary Prisoner's Dilemma cellular\nautomata, introduced by Nowak and May \\cite{nm92}, in which the pressure of the\nenvironment is taken into account. This is implemented by requiring that\nindividuals need to collect a minimum score $U_{min}$, representing\nindispensable resources (nutrients, energy, money, etc.) to prosper in this\nenvironment. So the agents, instead of evolving just by adopting the behaviour\nof the most successful neighbour (who got $U^{msn}$), also take into account if\n$U^{msn}$ is above or below the threshold $U_{min}$. If $U^{msn}<U_{min}$ an\nindividual has a probability of adopting the opposite behaviour from the one\nused by its most successful neighbour. This modification allows the evolution\nof cooperation for payoffs for which defection was the rule (as it happens, for\nexample, when the sucker's payoff is much worse than the punishment for mutual\ndefection). We also analyse a more sophisticated version of this model in which\nthe selective rule is supplemented with a \"win-stay, lose-shift\" criterion. The\ncluster structure is analyzed and, for this more complex version we found\npower-law scaling for a restricted region in the parameter space.",
        "category": "physics_soc-ph"
    },
    {
        "text": "On arXiv moderation system: The advent of arXiv has revolutionized scientific communication. However, its\ncultural significance goes far beyond simply accelerating scholarly\ncommunication. The arXiv gave a powerful impetus to the democratization of\nscience, freeing young scientists and not only, especially from totalitarian\ncountries, from authoritarian oppression. Many of arXiv's innovative features\nhave been blurred by the introduction of a moderation system. Without a doubt,\na moderation system is essential to maintain the quality of arXiv content.\nHowever, I believe that it can be improved in line with arXiv's original\nintentions, using the very successful experience of the MathOverflow moderation\nsystem.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Information Recovery In Behavioral Networks: In the context of agent based modeling and network theory, we focus on the\nproblem of recovering behavior-related choice information from\norigin-destination type data, a topic also known under the name of network\ntomography. As a basis for predicting agents' choices we emphasize the\nconnection between adaptive intelligent behavior, causal entropy maximization\nand self-organized behavior in an open dynamic system. We cast this problem in\nthe form of binary and weighted networks and suggest information theoretic\nentropy-driven methods to recover estimates of the unknown behavioral flow\nparameters. Our objective is to recover the unknown behavioral values across\nthe ensemble analytically, without explicitly sampling the configuration space.\nIn order to do so, we consider the Cressie-Read family of entropic functionals,\nenlarging the set of estimators commonly employed to make optimal use of the\navailable information. More specifically, we explicitly work out two cases of\nparticular interest: Shannon functional and the likelihood functional. We then\nemploy them for the analysis of both univariate and bivariate data sets,\ncomparing their accuracy in reproducing the observed trends.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Co-community Structure in Time-varying Networks: In this report, we introduce the concept of co-community structure in\ntime-varying networks. We propose a novel optimization algorithm to rapidly\ndetect co-community structure in these networks. Both theoretical and numerical\nresults show that the proposed method not only can resolve detailed\nco-communities, but also can effectively identify the dynamical phenomena in\nthese networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Statistical dynamics of religion evolutions: A religion affiliation can be considered as a \"degree of freedom\" of an agent\non the human genre network. A brief review is given on the state of the art in\ndata analysis and modelization of religious \"questions\" in order to suggest and\nif possible initiate further research, ... after using a \"statistical physics\nfilter\". We present a discussion of the evolution of 18 so called religions, as\nmeasured through their number of adherents between 1900 and 2000. Some emphasis\nis made on a few cases presenting a minimum or a maximum in the investigated\ntime range, - thereby suggesting a competitive ingredient to be considered,\nbeside the well accepted \"at birth\" attachement effect. The importance of the\n\"external field\" is still stressed through an Avrami late stage crystal\ngrowth-like parameter. The observed features and some intuitive interpretations\npoint to opinion based models with vector, rather than scalar, like agents.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Substantial but heterogeneous impacts of high-speed rail on talent flow\n  in China: The great expansion of high-speed rail (HSR) in China facilitates\ncommunications and interactions among people across cities. Despite extensive\nliterature documenting the effects of HSR on a variety of variables such as\nlocal economic development, research collaboration, tourism, and capital\nmobility, not much is known about how HSR affects the flow of well-educated\nworkers, says talents. Here we estimate talent flow among Chinese cities based\non large-scale resume data of online job seekers and explore how it is affected\nby HSR. Specifically, we employ both a multiple linear regression model that\ncontrols for several socioeconomic factors and a two-stage least square\nregression model that instruments the introduction of HSR to a city to address\nendogeneity concerns. We find that the introduction of HSR has an overall\npositive effect on the talent net inflow of a city although both inflow and\noutflow are increased. Moreover, the effects of HSR on talent flow are rather\nheterogeneous for cities with different levels of economic development and for\ntalents working in different industries. Specifically, developed cities benefit\nfrom HSR, whereas less-developed cities are relatively impaired. Cities\nconnected by HSR show significant advantage in attracting talents from\nsecondary and tertiary industries. These substantial but heterogeneous effects\nof HSR suggest a critical need for more comprehensive thinking about the\nlong-term benefits of entering the HSR network, especially for less-developed\ncities and those with comparative advantage in manufacturing and service\nindustries.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Generalized model for human dynamics: Human dynamics model consistent with our natural ability to perform different\nactivities is put forward by first arguing limitations of the model suggested\nby Barabasi (Nature, 435, 207-211, 2005).",
        "category": "physics_soc-ph"
    },
    {
        "text": "The production of information in the attention economy: Online traces of human activity offer novel opportunities to study the\ndynamics of complex knowledge exchange networks, and in particular how the\nrelationship between demand and supply of information is mediated by\ncompetition for our limited individual attention. The emergent patterns of\ncollective attention determine what new information is generated and consumed.\nCan we measure the relationship between demand and supply for new information\nabout a topic? Here we propose a normalization method to compare attention\nbursts statistics across topics that have an heterogeneous distribution of\nattention. Through analysis of a massive dataset on traffic to Wikipedia, we\nfind that the production of new knowledge is associated to significant shifts\nof collective attention, which we take as a proxy for its demand. What we\nobserve is consistent with a scenario in which the allocation of attention\ntoward a topic stimulates the demand for information about it, and in turn the\nsupply of further novel information. Our attempt to quantify demand and supply\nof information, and our finding about their temporal ordering, may lead to the\ndevelopment of the fundamental laws of the attention economy, and a better\nunderstanding of the social exchange of knowledge in online and offline\ninformation networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Beyond Nadel's Paradox. A computational approach to structural and\n  cultural dimensions of social cohesion: Nadel's Paradox states that it is not possible to take into account\nsimultaneously cultural and relational dimensions of social structure. By means\nof a simple computational model, the authors explore a dynamic perspective of\nthe concept of social cohesion that enables the integration of both structural\nand cultural dimensions in the same analysis. The design of the model\nreproduces a causal path from the level of conflict suffered by a population to\nvariations on its social cohesiveness, observed both from a structural and\ncognitive viewpoint. Submitted to sudden variations on its environmental\nconflict level, the model is able to reproduce certain characteristics\npreviously observed in real populations under situations of emergency or\ncrisis.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Quantifying patterns of research interest evolution: Our quantitative understanding of how scientists choose and shift their\nresearch focus over time is highly consequential, because it affects the ways\nin which scientists are trained, science is funded, knowledge is organized and\ndiscovered, and excellence is recognized and rewarded. Despite extensive\ninvestigations of various factors that influence a scientist's choice of\nresearch topics, quantitative assessments of mechanisms that give rise to\nmacroscopic patterns characterizing research interest evolution of individual\nscientists remain limited. Here we perform a large-scale analysis of\npublication records, finding that research interest change follows a\nreproducible pattern characterized by an exponential distribution. We identify\nthree fundamental features responsible for the observed exponential\ndistribution, which arise from a subtle interplay between exploitation and\nexploration in research interest evolution. We develop a random walk based\nmodel, allowing us to accurately reproduce the empirical observations. This\nwork presents a quantitative analysis of macroscopic patterns governing\nresearch interest change, discovering a high degree of regularity underlying\nscientific research and individual careers.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Temporal profiles of avalanches on networks: An avalanche or cascade occurs when one event causes one or more subsequent\nevents, which in turn may cause further events in a chain reaction. Avalanching\ndynamics are studied in many disciplines, with a recent focus on average\navalanche shapes, i.e., the temporal profiles that characterize the growth and\ndecay of avalanches of fixed duration. At the critical point of the dynamics\nthe average avalanche shapes for different durations can be rescaled so that\nthey collapse onto a single universal curve. We apply Markov branching process\ntheory to derive a simple equation governing the average avalanche shape for\ncascade dynamics on networks. Analysis of the equation at criticality\ndemonstrates that nonsymmetric average avalanche shapes (as observed in some\nexperiments) occur for certain combinations of dynamics and network topology;\nspecifically, on networks with heavy-tailed degree distributions. We give\nexamples using numerical simulations of models for information spreading,\nneural dynamics, and behaviour adoption and we propose simple experimental\ntests to quantify whether cascading systems are in the critical state.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Emergence of scale-free leadership structure in social recommender\n  systems: The study of the organization of social networks is important for\nunderstanding of opinion formation, rumor spreading, and the emergence of\ntrends and fashion. This paper reports empirical analysis of networks extracted\nfrom four leading sites with social functionality (Delicious, Flickr, Twitter\nand YouTube) and shows that they all display a scale-free leadership structure.\nTo reproduce this feature, we propose an adaptive network model driven by\nsocial recommending. Artificial agent-based simulations of this model highlight\na \"good get richer\" mechanism where users with broad interests and good\njudgments are likely to become popular leaders for the others. Simulations also\nindicate that the studied social recommendation mechanism can gradually improve\nthe user experience by adapting to tastes of its users. Finally we outline\nimplications for real online resource-sharing systems.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Self-citation corrections for the Hirsch index: I propose to sharpen the index h, proposed by Hirsch as a useful index to\ncharacterize the scientific output of a researcher, by excluding the\nself-citations. Performing a self-experiment and also analyzing two anonymous\ndata sets, it is shown that self-citations can significantly reduce the $h$\nindex in contrast to Hirsch's expectations. This result is confirmed by an\nanalysis of 13 further data sets.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The complex network of global cargo ship movements: Transportation networks play a crucial role in human mobility, the exchange\nof goods, and the spread of invasive species. With 90% of world trade carried\nby sea, the global network of merchant ships provides one of the most important\nmodes of transportation. Here we use information about the itineraries of\n16,363 cargo ships during the year 2007 to construct a network of links between\nports. We show that the network has several features which set it apart from\nother transportation networks. In particular, most ships can be classified in\nthree categories: bulk dry carriers, container ships and oil tankers. These\nthree categories do not only differ in the ships' physical characteristics, but\nalso in their mobility patterns and networks. Container ships follow regularly\nrepeating paths whereas bulk dry carriers and oil tankers move less predictably\nbetween ports. The network of all ship movements possesses a heavy-tailed\ndistribution for the connectivity of ports and for the loads transported on the\nlinks with systematic differences between ship types. The data analyzed in this\npaper improve current assumptions based on gravity models of ship movements, an\nimportant step towards understanding patterns of global trade and bioinvasion.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Fundamental Diagram of Traffic Flow from Prigogine-Herman-Enskog\n  Equation: Recent applications of a new methodology to measure fundamental traffic\nrelations on freeways shows that many of the critical parameters of the\nflow-density and speed-spacing diagrams depend on vehicle length. In response\nto this fact, we present in this work a generalization of the Prigogine-Herman\ntraffic equation for aggressive drivers which takes into account the fact that\nvehicles are not point-like objects but have an effective length. Our approach\nis similar to that introduced by Enskog for dense gases and provides the\nconstruction of fundamental diagrams which are in excellent agreement with\nempirical traffic data.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Maximum Entropy Approach for the Prediction of Urban Mobility Patterns: The science of cities is a relatively new and interdisciplinary topic. It\nborrows techniques from agent-based modeling, stochastic processes, and partial\ndifferential equations. However, how the cities rise and fall, how they evolve,\nand the mechanisms responsible for these phenomena are still open questions.\nScientists have only recently started to develop forecasting tools, despite\ntheir importance in urban planning, transportation planning, and epidemic\nspreading modeling. Here, we build a fully interpretable statistical model\nthat, incorporating only the minimum number of constraints, can predict\ndifferent phenomena arising in the city. Using data on the movements of\ncar-sharing vehicles in different Italian cities, we infer a model using the\nMaximum Entropy (MaxEnt) principle. With it, we describe the activity in\ndifferent city zones and apply it to activity forecasting and anomaly detection\n(e.g., strikes, and bad weather conditions). We compare our method with\ndifferent models explicitly made for forecasting: SARIMA models and Deep\nLearning Models. We find that MaxEnt models are highly predictive,\noutperforming SARIMAs and having similar results as a Neural Network. These\nresults show how relevant statistical inference can be in building a robust and\ngeneral model describing urban systems phenomena. This article identifies the\nsignificant observables for processes happening in the city, with the\nperspective of a deeper understanding of the fundamental forces driving its\ndynamics.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Investigation of Voronoi diagram based Direction Choices Using Uni- and\n  Bi-directional Trajectory Data: In a crowd, individuals make different motion choices such as \"moving to\ndestination\", \"following another pedestrian\", and \"making a detour\". For the\nsake of convenience, the three direction choices are respectively called\ndestination direction, following direction and detour direction in this paper.\nHere, it is found that the featured direction choices could be inspired by the\nshape characteristics of Voronoi diagram. To be specific, in the Voronoi cell\nof a pedestrian, the direction to a Voronoi node is regarded as a potential\n\"detour\" direction, and the direction perpendicular to a Voronoi link is\nregarded as a potential \"following\" direction. A pedestrian generally owns\nseveral alternative Voronoi nodes and Voronoi links in a Voronoi cell, and the\noptimal detour and following direction are determined by considering related\nfactors such as deviation. Plus the destination direction which is directly\npointing to the destination, the three basic direction choices are defined in a\nVoronoi cell. In order to evaluate the Voronoi diagram based basic directions,\nthe empirical trajectory data in both uni- and bi-directional flow experiments\nare extracted. A time series method considering the step frequency is used to\nreduce the original trajectories' swaying phenomena which might disturb the\nrecognition of actual forward direction. The deviations between the empirical\nvelocity direction and the basic directions are investigated, and each velocity\ndirection is classified into a basic direction or regarded as an inexplicable\ndirection according to the deviations. The analysis results show that each\nbasic direction could be a potential direction choice for a pedestrian. The\ncombination of the three basic directions could cover most empirical velocity\ndirection choices in both uni- and bi-directional flow experiments.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A Power Law for the Duration of High-Flow States in Heterogeneous\n  Traffic Flows: We study the duration of \"high-flow states\" in freeway traffic, defined as\nthe time periods for which traffic flows exceed a given flow threshold. Our\nempirical data are surprisingly well represented by a power law. Moreover, the\npower law exponent is approximately 2, in dependently of the chosen flow\nthreshold. In order to explain this discovery, we investigate a simple\ntheoretical model of heterogeneous traffic with overtaking maneuvers, which is\nable to reproduce both, the empirical power law and its exponent.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The uniqueness of company size distribution function from tent-shaped\n  growth rate distribution: We report the proof that the extension of Gibrat's law in the middle scale\nregion is unique and the probability distribution function (pdf) is also\nuniquely derived from the extended Gibrat's law and the law of detailed\nbalance. In the proof, two approximations are employed. The pdf of growth rate\nis described as tent-shaped exponential functions and the value of the origin\nof the growth rate distribution is constant. These approximations are confirmed\nin profits data of Japanese companies 2003 and 2004. The resultant profits pdf\nfits with the empirical data with high accuracy. This guarantees the validity\nof the approximations.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Spectral estimation of the percolation transition in clustered networks: There have been several spectral bounds for the percolation transition in\nnetworks, using spectrum of matrices associated with the network such as the\nadjacency matrix and the non-backtracking matrix. However they are far from\nbeing tight when the network is sparse and displays clustering or transitivity,\nwhich is represented by existence of short loops e.g. triangles. In this work,\nfor the bond percolation, we first propose a message passing algorithm for\ncalculating size of percolating clusters considering effects of triangles, then\nrelate the percolation transition to the leading eigenvalue of a matrix that we\nname the triangle-non-backtracking matrix, by analyzing stability of the\nmessage passing equations. We establish that our method gives a tighter\nlower-bound to the bond percolation transition than previous spectral bounds,\nand it becomes exact for an infinite network with no loops longer than 3. We\nevaluate numerically our methods on synthetic and real-world networks, and\ndiscuss further generalizations of our approach to include higher-order\nsub-structures.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Compressibility of complex networks: Many complex networks depend upon biological entities for their preservation.\nSuch entities, from human cognition to evolution, must first encode and then\nreplicate those networks under marked resource constraints. Networks that\nsurvive are those that are amenable to constrained encoding, or, in other\nwords, are compressible. But how compressible is a network? And what features\nmake one network more compressible than another? Here we answer these questions\nby modeling networks as information sources before compressing them using\nrate-distortion theory. Each network yields a unique rate-distortion curve,\nwhich specifies the minimal amount of information that remains at a given scale\nof description. A natural definition then emerges for the compressibility of a\nnetwork: the amount of information that can be removed via compression,\naveraged across all scales. Analyzing an array of real and model networks, we\ndemonstrate that compressibility increases with two common network properties:\ntransitivity (or clustering) and degree heterogeneity. These results indicate\nthat hierarchical organization -- which is characterized by modular structure\nand heterogeneous degrees -- facilitates compression in complex networks.\nGenerally, our framework sheds light on the interplay between a network's\nstructure and its capacity to be compressed, enabling investigations into the\nrole of compression in shaping real-world networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "U.S. Social Fragmentation at Multiple Scales: Despite global connectivity, societies seem to be increasingly polarized and\nfragmented. This phenomenon is rooted in the underlying complex structure and\ndynamics of social systems. Far from homogeneously mixing or adopting\nconforming views, individuals self-organize into groups at multiple scales,\nranging from families up to cities and cultures. In this paper, we study the\nfragmented structure of the American society using mobility and communication\nnetworks obtained from geo-located social media data. We find self-organized\npatches with clear geographical borders that are consistent between physical\nand virtual spaces. The patches have multi-scale structure ranging from parts\nof a city up to the entire nation. Their significance is reflected in distinct\npatterns of collective interests and conversations. Finally, we explain the\npatch emergence by a model of network growth that combines mechanisms of\ngeographical distance gravity, preferential attachment, and spatial growth. Our\nobservations are consistent with the emergence of social groups whose separated\nassociation and communication reinforce distinct identities. Rather than\neliminating borders, the virtual space reproduces them as people mirror their\noffline lives online. Understanding the mechanisms driving the emergence of\nfragmentation in hyper-connected social systems is imperative in the age of the\nInternet and globalization.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Evolving network structure of academic institutions: Today's colleges and universities consist of highly complex structures that\ndictate interactions between the administration, faculty, and student body.\nThese structures can play a role in dictating the efficiency of policy enacted\nby the administration and determine the effect that curriculum changes in one\ndepartment have on other departments. Despite the fact that the features of\nthese complex structures have a strong impact on the institutions, they remain\nby-and-large unknown in many cases. In this paper we study the academic\nstructure of our home institution of Trinity College in Hartford, CT using the\nmajor and minor patterns between graduating students to build a temporal\nmultiplex network describing the interactions between different departments.\nUsing recent network science techniques developed for such temporal networks we\nidentify the evolving community structures that organize departments'\ninteractions, as well as quantify the interdisciplinary centrality of each\ndepartment. We implement this framework for Trinity College, finding practical\ninsights and applications, but also present it as a general framework for\ncolleges and universities to better understand their own structural makeup in\norder to better inform academic and administrative policy.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Thermostatistics, information, subjectivity, why is this association so\n  disturbing?: Although information theory resolves inconsistencies (known under the form of\nfamous enigmas) of the traditional approach of thermostatistics, its place in\nthe corresponding literature is not what it deserves. This article supports the\nidea that this is mainly due to epistemological rather than scientific reasons:\nthe subjectivity introduced into physics is perceived as a problem. Here is an\nattempt to expose and clarify where exactly this subjectivity lies: in the\nrepresentation of the reality and in probabilistic inference. Two aspects which\nhave been integrated into the practice of science for a long time and which\nshould no longer frighten anyone, but which become explicit with information\ntheory.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Emergence of protective behaviour under different risk perceptions to\n  disease spreading: The behaviour of individuals is a main actor in the control of the spread of\na communicable disease and, in turn, the spread of an infectious disease can\ntrigger behavioural changes in a population. Here, we study the emergence of\nthe individuals protective behaviours in response to the spread of a disease by\nconsidering two different social attitudes within the same population:\nconcerned and risky. Generally speaking, concerned individuals have a larger\nrisk aversion than risky individuals. To study the emergence of protective\nbehaviours, we couple, to the epidemic evolution of a\nsusceptible-infected-susceptible model, a decision game based on the perceived\nrisk of infection. Using this framework, we find the effect of the protection\nstrategy on the epidemic threshold for each of the two subpopulations\n(concerned and risky), and study under which conditions risky individuals are\npersuaded to protect themselves or, on the contrary, can take advantage of a\nherd immunity by remaining healthy without protecting themselves, thanks to the\nshield provided by concerned individuals.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Clean up or mess up: the effect of sampling biases on measurements of\n  degree distributions in mobile phone datasets: Mobile phone data have been extensively used in the recent years to study\nsocial behavior. However, most of these studies are based on only partial data\nwhose coverage is limited both in space and time. In this paper, we point to an\nobservation that the bias due to the limited coverage in time may have an\nimportant influence on the results of the analyses performed. In particular, we\nobserve significant differences, both qualitatively and quantitatively, in the\ndegree distribution of the network, depending on the way the dataset is\npre-processed and we present a possible explanation for the emergence of Double\nPareto LogNormal (DPLN) degree distributions in temporal data.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Moving efficiently through a crowd: a nature inspired traffic rule: In this article, we propose a traffic rule inspired from nature, that\nfacilitates an elite agent to move efficiently through a crowd of inert agents.\nWhen an object swims in a fluid medium or an intruder is forced through\ngranular matter, characteristic flow-fields are created around them. We show\nthat if inert agents, made small movements based on a traffic rule derived from\nthese characteristic flow-fields, they efficiently reorganize and transport\nenough space for the elite to pass through. The traffic rule used in the\narticle is a dipole-field which satisfactorily captures the features of the\nflow-fields around a moving intruder. We study the effectiveness of this dipole\ntraffic rule using numerical simulations in a 2D periodic domain, where one\nself-propelled elite agent tries to move through a crowd of inert agents that\nprefer to stay in a state of rest. Simulations are carried out for a wide range\nof strengths of the traffic rule and packing density of the crowd. We\ncharacterize and analyze four regions in the parameter space, free-flow, motion\ndue to cooperation, frozen and collective drift regions, and discuss the\nconsequence of the traffic rule in light of the collective behavior observed.\nWe believe that the proposed method can be of use in a swarm of robots working\nin constrained environments.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A simple mechanism leading to first-order phase transitions in a model\n  of tax evasion: In this work we study a dynamics of tax evasion. We considered a\nfully-connected population divided in three compartments, namely honest tax\npayers, tax evaders and susceptibles, a class that is composed by honest tax\npayers that can become evaders. We consider a contagion model where the\ntransitions among the compartments are governed by probabilities. Such\nprobabilities represent the possible interactions among the indiviudals, as\nwell as the government's fiscalization. We show by analytical and numerical\ncalculations that the emergence of tax evaders in the population is associated\nwith an active-absorbing nonequilibrium first-order phase transition. In the\nabsorbing phase only honest tax payers survive in the steady states of the\nmodel, and we observe a coexistence of the three subpopulations in the active\nphase.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A Framework for Reconstructing COVID-19 Transmission Network to Inform\n  Betweenness Centrality-Based Control Measures: In this paper, we propose a general framework for optimal control measures,\nwhich follows the evolution of COVID-19 infection counts collected by\nSurveillance Units on a country level. We employ an autoregressive model that\nallows to decompose the mean number of infections into three components that\ndescribe: intra-locality infections, inter-locality infections, and infections\nfrom other sources such as travelers arriving to a country from abroad. We\nidentify the inter-locality term as a time-evolving network and when it drives\nthe dynamics of the disease we focus on its properties. Tools from network\nanalysis are then employed to get insight into its topology. Building on this,\nand particularly on the centrality of the nodes of the identified network, a\nstrategy for intervention and disease control is devised.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Characteristics of human mobility patterns revealed by high-frequency\n  cell-phone position data: Human mobility is an important characteristic of human behavior, but since\ntracking personalized position to high temporal and spatial resolution is\ndifficult, most studies on human mobility patterns rely largely on mathematical\nmodels. Seminal models which assume frequently visited locations tend to be\nre-visited, reproduce a wide range of statistical features including collective\nmobility fluxes and numerous scaling laws. However, these models cannot be\nverified at a time-scale relevant to our daily travel patterns as most\navailable data do not provide the necessary temporal resolution. In this work,\nwe re-examined human mobility mechanisms via comprehensive cell-phone position\ndata recorded at a high frequency up to every second. We found that the next\nlocation visited by users is not their most frequently visited ones in many\ncases. Instead, individuals exhibit origin-dependent, path-preferential\npatterns in their short time-scale mobility. These behaviors are prominent when\nthe temporal resolution of the data is high, and are thus overlooked in most\nprevious studies. Incorporating measured quantities from our high frequency\ndata into conventional human mobility models shows contradictory statistical\nresults. We finally revealed that the individual preferential transition\nmechanism characterized by the first-order Markov process can quantitatively\nreproduce the observed travel patterns at both individual and population levels\nat all relevant time-scales.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Simulation of the behavior of the Leopardus guigna using random walkers: Considering that there is very little information on the behavior habits of\nguigna cats, as well as investigations in which small populations are captured\nto place radiocollars on them and then release them in the place where they\nwere captured, which is done with the intention of collecting data on their\npositions in a territory and thus make estimates of the mean distances they\nusually travel. Under the hypothesis that guignas maintain a sedentary behavior\nin a specific area of a given territory, this paper shows one way to simulate a\ndistribution of points in a territory using random walkers to emulate the\ndistribution of the data that would be obtained by placing radiocollars in a\npopulation of guignas, with which it is possible to make estimates of the mean\ndistances that move away from a certain fixed position, and the interactions\nthey can have with points in the territory that represent a high probability of\nlethality, such as farms, packs of dogs, roads, urban areas, etc. It is\nnecessary to mention that by estimating the possible interactions that a\nguignas population may have with possible predators in a territory with the\nhelp of a satellite image, it is possible to evaluate the points of a territory\nthat represent a potentially lethal risk for the guignas, and thus generate\nrelocation strategies that help preserve them.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Segregation in spatially structured cities: Half of the world population resides in cities and urban segregation is\nbecoming a global issue. One of the best known attempts to understand it is the\nSchelling model, which considers two types of agents that relocate whenever a\ntransfer rule depending on the neighbor distribution is verified. The main aim\nof the present study is to broaden our understanding of segregated\nneighborhoods in the city, i.e. ghettos, extending the Schelling model to\nconsider economic aspects and their spatial distribution. To this end we have\nconsidered a monetary gap between the two social groups and five types of urban\nstructures, defined by the house pricing city map. The results show that ghetto\nsizes tend to follow a power law distribution in all the considered cases. For\neach city framework the interplay between economical aspects and the\ngeometrical features determine the location where ghettos reach their maximum\nsize. The system first steps shape greatly the city's final appearance.\nMoreover, the segregated population ratios depends largely on the monetary gap\nand not on the city type, implying that ghettos are able to adapt to different\nurban frameworks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Optimal Stabilization of Boolean Networks through Collective Influence: The stability of Boolean networks has attracted much attention due to its\nwide applications in describing the dynamics of biological systems. During the\npast decades, much effort has been invested in unveiling how network structure\nand update rules will affect the stability of Boolean networks. In this paper,\nwe aim to identify and control a minimal set of influential nodes that is\ncapable of stabilizing an unstable Boolean network. By minimizing the largest\neigenvalue of a modified non-backtracking matrix, we propose a method using the\ncollective influence theory to identify the influential nodes in Boolean\nnetworks with high computational efficiency. We test the performance of\ncollective influence on four different networks. Results show that the\ncollective influence algorithm can stabilize each network with a smaller set of\nnodes than other heuristic algorithms. Our work provides a new insight into the\nmechanism that determines the stability of Boolean networks, which may find\napplications in identifying the virulence genes that lead to serious disease.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Massive Case Study of Opinion Distribution in a Relationship with Mixed\n  Trust and Distrust: The simulations in this paper are based on the theory of opinion dynamics,\nwhich incorporates both Opinion A and Opinion B, a case that is the inverse of\nOpinion A, in human relationships. It was confirmed that aspects of consensus\nbuilding depend on the ratio of the trust coefficient to the distrust\ncoefficient. In this study, the ratio of trust to distrust tended to vary like\na phase transition around 55%, but we wanted to see if the same phenomenon\ncould be confirmed in large-scale cases. In the previous case studies, this\ntendency has been observed from N = 300 to N = 1600 , and we will discuss the\ncase of N = 10000 with N = 3000. By verifying the extent of the phenomenon on\nthe social scale, we intend to consider simulation items for consensus\nbuilding, such as consideration of the sensitivities of topics in online and\noffline opinion formation.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Multifractal cross-correlation effects in two-variable time series of\n  complex network vertex observables: We investigate the scaling of the cross-correlations calculated for\ntwo-variable time series containing vertex properties in the context of complex\nnetworks. Time series of such observables are obtained by means of stationary,\nunbiased random walks. We consider three vertex properties that provide,\nrespectively, short, medium, and long-range information regarding the\ntopological role of vertices in a given network. In order to reveal the\nrelation between these quantities, we applied the multifractal\ncross-correlation analysis technique, which provides information about the\nnonlinear effects in coupling of time series. We show that the considered\nnetwork models are characterized by unique multifractal properties of the\ncross-correlation. In particular, it is possible to distinguish between\nErd\\\"{o}s-R\\'{e}nyi, Barab\\'{a}si-Albert, and Watts-Strogatz networks on the\nbasis of fractal cross-correlation. Moreover, the analysis of protein contact\nnetworks reveals characteristics shared with both scale-free and small-world\nmodels.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Behavioural House Indicator: A faster and real time small-area\n  indicative deprivation measure for England: Researchers have been long preoccupied with the measuring and monitoring of\neconomic and social deprivation at small scales, neighbourhood, level in order\nto provide official government agencies and policy makers with more precise\ndata insights. Whist valuable methodologies have been developed, the exercise\nof data collection associated with these methods tend to be expensive, time\nconsuming, published infrequently with significant time delays, and subject to\nrecurring changes to methodology. Here, we propose a novel method based on a\nstraightforward methodology and data sources to generate a faster and real time\nindicator for deprivation at different scaling, small to larger areas. The\nresults of our work show that our method provides a consistent view of\ndeprivation across the regions of England and Wales, which are inline with the\nother indexes, but also highlight specific flash points of deep rural and\nhighly dense urban deprivation areas that are not well captured by existing\nindexes. Our method is intended to aid researchers and policy makers by\ncomplementing existing but infrequent indexes.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Time varying networks and the weakness of strong ties: In most social and information systems the activity of agents generates\nrapidly evolving time-varying networks. The temporal variation in networks'\nconnectivity patterns and the ongoing dynamic processes are usually coupled in\nways that still challenge our mathematical or computational modelling. Here we\nanalyse a mobile call dataset and find a simple statistical law that\ncharacterize the temporal evolution of users' egocentric networks. We encode\nthis observation in a reinforcement process defining a time-varying network\nmodel that exhibits the emergence of strong and weak ties. We study the effect\nof time-varying and heterogeneous interactions on the classic rumour spreading\nmodel in both synthetic, and real-world networks. We observe that strong ties\nseverely inhibit information diffusion by confining the spreading process among\nagents with recurrent communication patterns. This provides the\ncounterintuitive evidence that strong ties may have a negative role in the\nspreading of information across networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A reconstruction of Florida Traffic Flow During Hurricane Irma (2017): Recent Hurricane Irma (2017) created the most extensive scale of evacuation\nin Florida's history, involving about 6.5 million people on mandatory\nevacuation order and 4 million evacuation vehicles. To understand the hurricane\nevacuation process, the spatial and temporal evolution of the traffic flow is a\ncritical piece of information, but it is usually not fully observed. Based on\nthe game theory, this paper employs the available traffic observation on main\nhighways (20 cameras; including parts of Route 1, 27 and I-75, 95, 4,10) during\nIrma to reconstruct the traffic flow in Florida during Irma. The model is\nvalidated with self-reported twitters. The traffic reconstruction estimates the\ntraffic demand (about 4 million cars in total) and the temporal and spatial\ndistribution of congestion during the evacuation. The results compare well with\navailable information from news reports and Twitter records. The reconstructed\ndata can be used to analyze hurricane evacuation decisions and travel behavior.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Quantifying dynamics of failure across science, startups, and security: Human achievements are often preceded by repeated attempts that initially\nfail, yet little is known about the mechanisms governing the dynamics of\nfailure. Here, building on the rich literature on innovation, human dynamics\nand learning, we develop a simple one-parameter model that mimics how\nsuccessful future attempts build on those past. Analytically solving this model\nreveals a phase transition that separates dynamics of failure into regions of\nstagnation or progression, predicting that near the critical threshold, agents\nwho share similar characteristics and learning strategies may experience\nfundamentally different outcomes following failures. Below the critical point,\nwe see those who explore disjoint opportunities without a pattern of\nimprovement, and above it, those who exploit incremental refinements to\nsystematically advance toward success. The model makes several empirically\ntestable predictions, demonstrating that those who eventually succeed and those\nwho do not may be initially similar, yet are characterized by fundamentally\ndistinct failure dynamics in terms of the efficiency and quality of each\nsubsequent attempt. We collected large-scale data from three disparate domains,\ntracing repeated attempts by (i) NIH investigators to fund their research, (ii)\ninnovators to successfully exit their startup ventures, and (iii) terrorist\norganizations to post casualties in violent attacks, finding broadly consistent\nempirical support across all three domains. Together, our findings unveil\nidentifiable yet previously unknown early signals that allow us to identify\nfailure dynamics that will lead to ultimate victory or defeat. Given the\nubiquitous nature of failures and the paucity of quantitative approaches to\nunderstand them, these results represent a crucial step toward deeper\nunderstanding of the complex dynamics beneath failures, the essential\nprerequisites for success.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Diffusion dynamics on multiplex networks: We study the time scales associated to diffusion processes that take place on\nmultiplex networks, i.e. on a set of networks linked through interconnected\nlayers. To this end, we propose the construction of a supra-Laplacian matrix,\nwhich consists of a dimensional lifting of the Laplacian matrix of each layer\nof the multiplex network. We use perturbative analysis to reveal analytically\nthe structure of eigenvectors and eigenvalues of the complete network in terms\nof the spectral properties of the individual layers. The spectrum of the\nsupra-Laplacian allows us to understand the physics of diffusion-like processes\non top of multiplex networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Zipf's, Heaps' and Taylor's laws are determined by the expansion into\n  the adjacent possible: Zipf's, Heaps' and Taylor's laws are ubiquitous in many different systems\nwhere innovation processes are at play. Together, they represent a compelling\nset of stylized facts regarding the overall statistics, the innovation rate and\nthe scaling of fluctuations for systems as diverse as written texts and cities,\necological systems and stock markets. Many modeling schemes have been proposed\nin literature to explain those laws, but only recently a modeling framework has\nbeen introduced that accounts for the emergence of those laws without deducing\nthe emergence of one of the laws from the others or without ad hoc assumptions.\nThis modeling framework is based on the concept of adjacent possible space and\nits key feature of being dynamically restructured while its boundaries get\nexplored, i.e., conditional to the occurrence of novel events. Here, we\nillustrate this approach and show how this simple modelling framework,\ninstantiated through a modified Polya's urn model, is able reproduce Zipf's,\nHeaps' and Taylor's laws within a unique self-consistent scheme. In addition\nthe same modelling scheme embraces other less common evolutionary laws (Hoppe's\nmodel and Dirichlet processes) as particular cases.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The dynamics of correlated novelties: One new thing often leads to another. Such correlated novelties are a\nfamiliar part of daily life. They are also thought to be fundamental to the\nevolution of biological systems, human society, and technology. By opening new\npossibilities, one novelty can pave the way for others in a process that\nKauffman has called \"expanding the adjacent possible\". The dynamics of\ncorrelated novelties, however, have yet to be quantified empirically or modeled\nmathematically. Here we propose a simple mathematical model that mimics the\nprocess of exploring a physical, biological or conceptual space that enlarges\nwhenever a novelty occurs. The model, a generalization of Polya's urn, predicts\nstatistical laws for the rate at which novelties happen (analogous to Heaps'\nlaw) and for the probability distribution on the space explored (analogous to\nZipf's law), as well as signatures of the hypothesized process by which one\nnovelty sets the stage for another. We test these predictions on four data sets\nof human activity: the edit events of Wikipedia pages, the emergence of tags in\nannotation systems, the sequence of words in texts, and listening to new songs\nin online music catalogues. By quantifying the dynamics of correlated\nnovelties, our results provide a starting point for a deeper understanding of\nthe ever-expanding adjacent possible and its role in biological, linguistic,\ncultural, and technological evolution.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Emergent lifetime distribution from complex network systems aging: Most theoretical analysis for lifetime distribution explains origins of\nspecific distribution based on independent failure. We develop a unified\nframework encompassing different lifetime distribution for failure-coupled\nnetwork systems. We find three types of system lifetime distributions emerged\nfrom competence between system size N and failure coupling strength $\\phi$.\nSystem lifetime distribution can be describe by modified Weibull model, which\ndegenerates into Gompertz model when N dominates and exponential distribution\nwhen $\\phi$ dominates. We derive asymptotic lifetime distribution. Specially,\nwe derive a fundamental equation of thermodynamics for failure-coupled systems.\nOur study will help design highly reliable systems.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The dynamic pattern of human attention: A mass of traces of human activities show diverse dynamic patterns. In this\npaper, we comprehensively investigate the dynamic pattern of human attention\ndefined by the quantity of interests on subdisciplines in an online academic\ncommunication forum. Both the expansion and exploration of human attention have\na power-law scaling relation with browsing actions, of which the exponent is\nclose to that in one-dimension random walk. Furthermore, the memory effect of\nhuman attention is characterized by the power-law distributions of both the\nreturn interval time and return interval steps, which is reinforced by studying\nthe attention shift that monotonically increase with the interval order between\npairs of continuously segmental sequences of expansion. At last, the observing\ndynamic pattern of human attention in the browsing process is analytically\ndescribed by a dynamic model whose generic mechanism is analogy to that of\nhuman spatial mobility. Thus, our work not only enlarges the research scope of\nhuman dynamics, but also provides an insight to understand the relationship\nbetween the interest transitivity in online activities and human spatial\nmobility in real world.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Exact Scale Invariance in Mixing of Binary Candidates in Voting Model: We introduce a voting model and discuss the scale invariance in the mixing of\ncandidates. The Candidates are classified into two categories $\\mu\\in \\{0,1\\}$\nand are called as `binary' candidates. There are in total $N=N_{0}+N_{1}$\ncandidates, and voters vote for them one by one. The probability that a\ncandidate gets a vote is proportional to the number of votes. The initial\nnumber of votes (`seed') of a candidate $\\mu$ is set to be $s_{\\mu}$. After\ninfinite counts of voting, the probability function of the share of votes of\nthe candidate $\\mu$ obeys gamma distributions with the shape exponent $s_{\\mu}$\nin the thermodynamic limit $Z_{0}=N_{1}s_{1}+N_{0}s_{0}\\to \\infty$. Between the\ncumulative functions $\\{x_{\\mu}\\}$ of binary candidates, the power-law relation\n$1-x_{1} \\sim (1-x_{0})^{\\alpha}$ with the critical exponent\n$\\alpha=s_{1}/s_{0}$ holds in the region $1-x_{0},1-x_{1}<<1$. In the double\nscaling limit $(s_{1},s_{0})\\to (0,0)$ and $Z_{0} \\to \\infty$ with\n$s_{1}/s_{0}=\\alpha$ fixed, the relation $1-x_{1}=(1-x_{0})^{\\alpha}$ holds\nexactly over the entire range $0\\le x_{0},x_{1} \\le 1$. We study the data on\nhorse races obtained from the Japan Racing Association for the period 1986 to\n2006 and confirm scale invariance.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Incentive-driven transition to high ride-sharing adoption: Ride-sharing - the combination of multiple trips into one - may substantially\ncontribute towards sustainable urban mobility. It is most efficient at high\ndemand locations with many similar trip requests. However, here we reveal that\npeople's willingness to share rides does not follow this trend. Modeling the\nfundamental incentives underlying individual ride-sharing decisions, we find\ntwo opposing adoption regimes, one with constant and another one with\ndecreasing adoption as demand increases. In the high demand limit, the\ntransition between these regimes becomes discontinuous, switching abruptly from\nlow to high ride-sharing adoption. Analyzing over 360 million ride requests in\nNew York City and Chicago illustrates that both regimes coexist across the\ncities, consistent with our model predictions. These results suggest that even\na moderate increase in the financial incentives may have a disproportionately\nlarge effect on the ride-sharing adoption of individual user groups.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A common origin of the power law distributions in models of market and\n  earthquake: We show that there is a common mode of origin for the power laws observed in\ntwo different models: (i) the Pareto law for the distribution of money among\nthe agents with random saving propensities in an ideal gas-like market model\nand (ii) the Gutenberg-Richter law for the distribution of overlaps in a\nfractal-overlap model for earthquakes. We find that the power laws appear as\nthe asymptotic forms of ever-widening log-normal distributions for the agents'\nmoney and the overlap magnitude respectively. The identification of the generic\norigin of the power laws helps in better understanding and in developing\ngeneralized views of phenomena in such diverse areas as economics and\ngeophysics.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Mathematical measures of societal polarisation: In opinion dynamics, as in general usage, polarisation is subjective. To\nunderstand polarisation, we need to develop more precise methods to measure the\nagreement in society. This paper presents four mathematical measures of\npolarisation derived from graph and network representations of societies and\ninformation theoretic divergences or distance metrics. Two of the methods,\nmin-max flow and spectral radius, rely on graph theory and define polarisation\nin terms of the structural characteristics of networks. The other two methods\nrepresent opinions as probability density functions and use the Kullback\nLeibler divergence and the Hellinger distance as polarisation measures. We\npresent a series of opinion dynamics simulations from two common models to test\nthe effectiveness of the methods. Results show that the four measures provide\ninsight into the different aspects of polarisation and allow real-time\nmonitoring of social networks for indicators of polarisation. The three\nmeasures, the spectral radius, Kullback Leibler divergence and Hellinger\ndistance, smoothly delineated between different amounts of polarisation, i.e.\nhow many cluster there were in the simulation, while also measuring with more\ngranularity how close simulations were to consensus. Min-max flow failed to\naccomplish such nuance.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Economic complexity and capabilities of Indian States: We explore economic competitiveness of Indian states based on the economic\ncomplexity algorithm, using a pair of coupled non-linear maps to characterize\nthe Fitness of states and Complexity of products exported by them. We find that\nstates produce almost all products within their productive capabilities -\ndiversifying rather than specializing, and that the probability of coexistence\nof any pair of productive capabilities is maximized when capabilities are of\nsimilar complexity. Therefore, states require long time horizons to build\ncomplex capabilities and diverse products. We contextualize Fitness using human\ndevelopment, and find an emergent typology of states. Of most concern are the\nstates of Odisha, Uttar Pradesh, and Bihar, stuck in vicious feedback cycles of\npoor economic complexity and low human development. Economic complexity also\nreveals significant concerns with the economic trajectories of Punjab, Gujarat,\nand West Bengal. We discuss these emergent trends within the framework of\nIndia's modern economic history.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A Microscopic Decision Model for Route Choice and Event-Driven Revisions: We propose a microscopic decision model for route choice based on discrete\nchoice theory. The correlation of overlapping routes is included in the random\nportions of the utility explicitly. For computational efficiency, we restrict\nthe choice set to the turning possibilities at the next intersection, assuming\nshortest paths to the destination afterwards. The proposed decision model also\nregards traffic conditions (e.g. traffic lights, long queues) such that drivers\nmay revise their previously taken decision. Due to its compatibility to already\nexisting microscopic traffic flow models, the proposed route choice model can\nbe readily simulated with available software. Combined, the proposed decision\nmodel features realistic behavior, i.e., adaptive choice based on incomplete\ninformation and simultaneously allows for a straightforward implementation.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Phase Transitions of Civil Unrest across Countries and Time: Phase transitions, characterized by abrupt shifts between macroscopic\npatterns of organization, are ubiquitous in complex systems. Despite\nconsiderable research in the physical and natural sciences, the empirical study\nof this phenomenon in societal systems is relatively underdeveloped. The goal\nof this study is to explore whether the dynamics of collective civil unrest can\nbe plausibly characterized as a sequence of recurrent phase shifts, with each\nphase having measurable and identifiable latent characteristics. Building on\nprevious efforts to characterize civil unrest as a self-organized critical\nsystem, we introduce a macro-level statistical model of civil unrest and\nevaluate its plausibility using a comprehensive dataset of civil unrest events\nin 170 countries from 1946 to 2017. Our findings demonstrate that the\nmacro-level phase model effectively captures the characteristics of civil\nunrest data from diverse countries globally and that universal mechanisms may\nunderlie certain aspects of the dynamics of civil unrest. We also introduce a\nscale to quantify a country's long-term unrest per unit of time and show that\ncivil unrest events tend to cluster geographically, with the magnitude of civil\nunrest concentrated in specific regions. Our approach has the potential to\nidentify and measure phase transitions in various collective human phenomena\nbeyond civil unrest, contributing to a better understanding of complex social\nsystems.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Estimating Mixed-Mode Urban Trail Traffic Using Negative Binomial\n  Regression Models: Data and models of non-motorized traffic on multiuse urban trails are needed\nto improve planning and management of urban transportation systems. Negative\nbinomial regression models are appropriate and useful when dependent variables\nare non-negative integers with over-dispersion like traffic counts. This paper\npresents eight negative binomial models for estimating urban trail traffic\nusing 1,898 daily mixed-mode traffic counts from active infrared monitors at\nsix locations in Minneapolis, MN. Our models include up to 10 independent\nvariables that represent socio-demographic, built environment, weather, and\ntemporal characteristics. A general model can be used to estimate traffic at\nlocations where traffic has not been monitored. A six-location model with dummy\nvariables for each monitoring site rather than neighborhood specific variables\ncan be used to estimate traffic at existing locations when counts from monitors\nare not available. Six trail-specific models are appropriate for estimating\nvariation in traffic in response to variations in weather and day of week.\nValidation results indicate negative binomial models outperform models\nestimated by ordinary least squares regression. These new models estimate\ntraffic within approximately 16.3% error, on average, which is reasonable for\nplanning and management purposes.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Setting the Agenda: Different strategies of a Mass Media in a model of\n  cultural dissemination: Day by day, people exchange opinions about a given new with relatives,\nfriends, and coworkers. In most cases, they get informed about a given issue by\nreading newspapers, listening to the radio, or watching TV, i.e., through a\nMass Media (MM). However, the importance of a given new can be stimulated by\nthe Media by assigning newspaper's pages or time in TV programs. In this sense,\nwe say that the Media has the power to \"set the agenda\", i.e., it decides which\nnew is important and which is not. On the other hand, the Media can know\npeople's concerns through, for instance, websites or blogs where they express\ntheir opinions, and then it can use this information in order to be more\nappealing to an increasing number of people. In this work, we study different\nscenarios in an agent-based model of cultural dissemination, in which a given\nMass Media has a specific purpose: To set a particular topic of discussion and\nimpose its point of view to as many social agents as it can. We model this by\nmaking the Media has a fixed feature, representing its point of view in the\ntopic of discussion, while it tries to attract new consumers, by taking\nadvantage of feedback mechanisms, represented by adaptive features. We explore\ndifferent strategies that the Media can adopt in order to increase the affinity\nwith potential consumers and then the probability to be successful in imposing\nthis particular topic.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Emergence of multilevel selection in the prisoner's dilemma game on\n  coevolving random networks: We study the evolution of cooperation in the prisoner's dilemma game, whereby\na coevolutionary rule is introduced that molds the random topology of the\ninteraction network in two ways. First, existing links are deleted whenever a\nplayer adopts a new strategy or its degree exceeds a threshold value, and\nsecond, new links are added randomly after a given number of game iterations.\nThese coevolutionary processes correspond to the generic formation of new and\ndeletion of existing links that, especially in human societies, appear\nfrequently as a consequence of ongoing socialization, change of lifestyle or\ndeath. Due to the counteraction of deletions and additions of links the initial\nheterogeneity of the interaction network is qualitatively preserved, and thus\ncannot be held responsible for the observed promotion of cooperation. Indeed,\nthe coevolutionary rule evokes the spontaneous emergence of a powerful\nmultilevel selection mechanism, which despite of the sustained random topology\nof the evolving network, maintains cooperation across the whole span of\ndefection temptation values.",
        "category": "physics_soc-ph"
    },
    {
        "text": "How to restart? An agent-based simulation model towards the definition\n  of strategies for COVID-19 \"second phase\" in public buildings: Restarting public buildings activities in the \"second phase\" of COVID-19\nemergency should be supported by operational measures to avoid a second virus\nspreading. Buildings hosting the continuous presence of the same users and\nsignificant overcrowd conditions over space/time (e.g. large offices,\nuniversities) are critical scenarios due to the prolonged contact with\ninfectors. Beside individual's risk-mitigation strategies performed (facial\nmasks), stakeholders should promote additional strategies, i.e. occupants' load\nlimitation (towards \"social distancing\") and access control. Simulators could\nsupport the measures effectiveness evaluation. This work provides an\nAgent-Based Model to estimate the virus spreading in the closed built\nenvironment. The model adopts a probabilistic approach to jointly simulate\noccupants' movement and virus transmission according to proximity-based and\nexposure-time-based rules proposed by international health organizations.\nScenarios can be defined in terms of building occupancy, mitigation strategies\nand virus-related aspects. The model is calibrated on experimental data\n(\"Diamond Princess\" cruise) and then applied to a relevant case-study (a part\nof a university campus). Results demonstrate the model capabilities. Concerning\nthe case-study, adopting facial masks seems to be a paramount strategy to\nreduce virus spreading in each initial condition, by maintaining an acceptable\ninfected people's number. The building capacity limitation could support such\nmeasure by potentially moving from FFPk masks to surgical masks use by\noccupants (thus improving users' comfort issues). A preliminary model to\ncombine acceptable mask filters-occupants' density combination is proposed. The\nmodel could be modified to consider other recurring scenarios in other public\nbuildings (e.g. tourist facilities, cultural buildings).",
        "category": "physics_soc-ph"
    },
    {
        "text": "Leveraging Uncertainty in Collective Opinion Dynamics with Heterogeneity: Natural and artificial collectives exhibit heterogeneities across different\ndimensions, contributing to the complexity of their behavior. We investigate\nthe effect of two such heterogeneities on collective opinion dynamics:\nheterogeneity of the quality of agents' prior information and of centrality in\nthe network, i.e., the number of immediate neighbors. To study these\nheterogeneities, we not only consider them in our model, proposing a novel\nnetwork generator with heterogeneous centrality, but also introduce uncertainty\nas an additional dimension. By quantifying the uncertainty of each agent, we\nprovide a mechanism for agents to adaptively weigh their individual against\nsocial information. As uncertainties develop according to the interactions\nbetween agents, they capture information on heterogeneities. Therefore,\nuncertainty is a relevant additional observable in the study of complex\ncollective opinion dynamics that we use to show the bidirectional relationship\nof heterogeneous centrality and information. Furthermore, we demonstrate that\nuncertainty-driven adaptive weighting leads to increased accuracy and speed of\nconsensus, especially under heterogeneity, and provide guidelines for avoiding\nperformance-decreasing errors in uncertainty modeling. These opportunities for\nimproved performance and observability suggest the importance of uncertainty\nboth for the study of natural and the design of artificial heterogeneous\nsystems.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Double transitions and hysteresis in heterogeneous contagion processes: In many real-world contagion phenomena, the number of contacts to spreading\nentities for adoption varies for different individuals. Therefore, we study a\nmodel of contagion dynamics with heterogeneous adoption thresholds. We derive\nmean-field equations for the fraction of adopted nodes and obtain phase\ndiagrams in terms of the transmission probability and fraction of nodes\nrequiring multiple contacts for adoption. We find a double phase transition\nexhibiting a continuous transition and a subsequent discontinuous jump in the\nfraction of adopted nodes because of the heterogeneity in adoption thresholds.\nAdditionally, we observe hysteresis curves in the fraction of adopted nodes\nowing to adopted nodes in the densely connected core in a network.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Estimating the outcome of spreading processes on networks with\n  incomplete information: a mesoscale approach: Recent advances in data collection have facilitated the access to\ntime-resolved human proximity data that can conveniently be represented as\ntemporal networks of contacts between individuals. While this type of data is\nfundamental to investigate how information or diseases propagate in a\npopulation, it often suffers from incompleteness, which possibly leads to\nbiased conclusions. A major challenge is thus to estimate the outcome of\nspreading processes occurring on temporal networks built from partial\ninformation. To cope with this problem, we devise an approach based on\nNon-negative Tensor Factorization (NTF) -- a dimensionality reduction technique\nfrom multi-linear algebra. The key idea is to learn a low-dimensional\nrepresentation of the temporal network built from partial information, to adapt\nit to take into account temporal and structural heterogeneity properties known\nto be crucial for spreading processes occurring on networks, and to construct\nin this way a surrogate network similar to the complete original network. To\ntest our method, we consider several human-proximity networks, on which we\nsimulate a loss of data. Using our approach on the resulting partial networks,\nwe build a surrogate version of the complete network for each. We then compare\nthe outcome of a spreading process on the complete networks (non altered by a\nloss of data) and on the surrogate networks. We observe that the epidemic sizes\nobtained using the surrogate networks are in good agreement with those measured\non the complete networks. Finally, we propose an extension of our framework\nwhen additional data sources are available to cope with the missing data\nproblem.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Build-Up of Diversity in Complex Ecosystems: Diversity is a fundamental feature of ecosystems, even when the concept of\necosystem is extended to sociology or economics. Diversity can be intended as\nthe count of different items, animals, or, more generally, interactions. There\nare two classes of stylized facts that emerge when diversity is taken into\naccount. The first are Diversity explosions: evolutionary radiations in\nbiology, or the process of escaping 'Poverty Traps' in economics are two well\nknown examples. The second is nestedness: entities with a very diverse set of\ninteractions are the only ones that interact with more specialized ones. In a\nsingle sentence: specialists interact with generalists. Nestedness is observed\nin a variety of bipartite networks of interactions: Biogeographic,\nmacroeconomic and mutualistic to name a few. This indicates that entities\ndiversify following a pattern. Since they appear in such very different\nsystems, these two stylized facts point out that the build up of diversity is\ndriven by a fundamental probabilistic mechanism, and here we sketch its minimal\nfeatures. We show how the contraction of a random tripartite network, which is\nmaximally entropic in all its degree distributions but one, can reproduce\nstylized facts of real data with great accuracy which is qualitatively lost\nwhen that degree distribution is changed. We base our reasoning on the\ncombinatoric picture that the nodes on one layer of these bipartite networks\ncan be described as combinations of a number of fundamental building blocks.\nThe stylized facts of diversity that we observe in real systems can be\nexplained with an extreme heterogeneity (a scale-free distribution) in the\nnumber of meaningful combinations in which each building block is involved. We\nshow that if the usefulness of the building blocks has a scale-free\ndistribution, then maximally entropic baskets of building blocks will give rise\nto very rich behaviors.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Norm breaking in a queue -- athermal phase transition: Standard order-disorder phase transition in the Ising model is described in\nterms of rates of processes of spin flips. This formulation allows to extend\nnumerous results on phase transition for sciences other than physics of\nmagnetism. We apply it to the problem of norm breaking. Two strategies: to\ncooperate or to defect, form an analogy to two spin orientations. An example is\na crowd at an exit, where to defect means to push others, and to cooperate is\nto move slowly together.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Magic of Networks Grown by Redirection: We highlight intriguing features of complex networks that are grown by\n\\emph{redirection}. In this mechanism, a target node is chosen uniformly at\nrandom from the pre-existing network nodes and the new node attaches either to\nthis initial target or to a neighbor of this target. This exceedingly simple\nalgorithm generates preferential attachment networks in an algorithmic time\nthat is linear in the number of network nodes $N$. Even though preferential\nattachment ostensibly requires \\emph{global knowledge} of the network,\nredirection requires only \\emph{local knowledge}. We also show that changing\njust a \\emph{single} attachment rate in linear preferential attachment leads to\na non-universal degree distribution. Finally, we present unexpected\nconsequences of redirection in networks with undirected links, where highly\nmodular and non-sparse networks arise.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Heider Balance under Disordered Triadic Interactions: The Heider balance addresses three-body interactions with the assumption that\ntriads are equally important in the dynamics of the network. In many networks,\nthe relations do not have the same strength so, triads are differently\nweighted. Now, the question is how social networks evolve to reduce the number\nof unbalanced triangles when they are weighted? Are the results foreseeable\nbased on what we have already learned from the unweighted balance? To find the\nsolution, we consider a fully connected network in which triads are assigned\nwith different random weights. Weights are coming from Gaussian probability\ndistribution with mean $\\mu$ and variance $\\sigma$. We study this system in two\nregimes : (\\RN{1}) the ratio of $\\frac{\\mu}{\\sigma} \\ge 1 $ corresponds to weak\ndisorder (small variance) that triads' weight are approximately the same,\n(\\RN{2}) $\\frac{\\mu}{\\sigma} < 1 $ counts for strong disorder (big variance)\nand weights are remarkably diverse. Investigating the structural evolution of\nsuch a network is our intention. We see disorder plays a key role in\ndetermining the critical temperature of the system. Using the mean-field method\nto present an analytic solution for the system represents that the system\nundergoes a first-order phase transition. For weak disorder, our simulation\nresults display the system reaches the global minimum as temperature decreases\nwhereas for high disorder we manage to see a seudo-Kondo effect in triadic\ninteractions. The Kondo effect explains the anomalous increase in electrical\nresistivity of metals due to magnetic impurities at low temperatures. Similar\nbehavior is seen in the energy versus temperature in our work for the second\nregime and the system does not reach a global minimum.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Modeling of flows with the power-law spectral densities and power-law\n  distributions of flow's intensities: We present analytical and numerical results of modeling of flows represented\nas the correlated non-Poissonian point process and as the Poissonian sequence\nof pulses of the different size. Both models may generate signals with the\npower-law distributions of the intensity of the flow and the power-law spectral\ndensity. Furthermore, different distributions of the interevent time of the\npoint process and different statistics of the size of pulses may result in\n$1/f^{\\beta}$ noise (one-over-f noise, 1-f noise) with\n$0.5\\lesssim\\beta\\lesssim2$. Combination of the models is applied for modeling\nof the Internet traffic.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A network-based strategy of price correlations for optimal\n  cryptocurrency portfolios: A cryptocurrency is a digital asset maintained by a decentralised system\nusing cryptography. Investors in this emerging digital market are exploring the\nprofitability potential of portfolios in place of single coins. Portfolios are\nparticularly useful given that price forecasting in such a volatile market is\nchallenging. The crypto market is a self-organised complex system where the\ncomplex inter-dependencies between the cryptocurrencies may be exploited to\nunderstand the market dynamics and build efficient portfolios. In this letter,\nwe use network methods to identify highly decorrelated cryptocurrencies to\ncreate diversified portfolios using the Markowitz Portfolio Theory agnostic to\nfuture market behaviour. The performance of our network-based portfolios is\noptimal with 46 coins and superior to benchmarks up to an investment horizon of\n14 days, reaching up to 1,066% average expected return within 1 day, with\nreasonable associated risks. We also show that popular cryptocurrencies are\ntypically not included in the optimal portfolios. Past price correlations\nreduce risk and may improve the performance of crypto portfolios in comparison\nto methodologies based exclusively on price auto-correlations. Short-term\ncrypto investments may be competitive to traditional high-risk investments such\nas the stock market or commodity market but call for caution given the high\nvariability of prices.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Time to critical condition in emergency services: Providing uninterrupted response service is of paramount importance for\nemergency medical services, regardless of the operating scenario. Thus,\nreliable estimates of the time to the critical condition, under which there\nwill be no available servers to respond the next incoming call, become very\nuseful measures of the system's performance. In this contribution, we develop a\nkey performance indicator by providing an explicit formula for the average time\nto the shortage condition. Our analytical expression for this average time is a\nfunction of the number of parallel servers and the inter-arrival and service\ntimes. We assume exponential distributions of times in our analytical\nexpression but for evaluating the mean first-passage time to the critical\ncondition under more realistic scenarios we validate our result through\nexhaustive simulations with lognormal service time distributions. For this task\nwe have implemented an simulator in $R$. Our results indicate that our\nanalytical formula is an acceptable approximation under any situation of\npractical interest.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Condensation in an Economic Model with Brand Competition: We present a linear agent based model on brand competition. Each agent\nbelongs to one of the two brands and interacts with its nearest neighbors. In\nthe process the agent can decide to change to the other brand if the move is\nbeneficial. The numerical simulations show that the systems always condenses\ninto a state when all agents belong to a single brand. We study the\ncondensation times for different parameters of the model and the influence of\ndifferent mechanisms to avoid condensation, like anti monopoly rules and brand\nfidelity.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Criterions for locally dense subgraphs: Community detection is one of the most investigated problems in the field of\ncomplex networks. Although several methods were proposed, there is still no\nprecise definition of communities. As a step towards a definition, I highlight\ntwo necessary properties of communities, separation and internal cohesion, the\nlatter being a new concept. I propose a local method of community detection\nbased on two-dimensional local optimization, which I tested on common\nbenchmarks and on the word association database.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Long division unites - long union divides, a model for social network\n  evolution: A remarkable phenomenon in the time evolution of many networks such as\ncultural, political, national and economic systems, is the recurrent transition\nbetween the states of union and division of nodes. In this work, we propose a\nphenomenological modeling, inspired by the maxim \"long union divides and long\ndivision unites\", in order to investigate the evolutionary characters of these\nnetworks composed of the entities whose behaviors are dominated by these two\nevents. The nodes are endowed with quantities such as identity, ingredient,\nrichness (power), openness (connections), age, distance, interaction etc. which\ndetermine collectively the evolution in a probabilistic way. Depending on a\ntunable parameter, the time evolution of this model is mainly an alternative\ndomination of union or division state, with a possible state of final union\ndominated by one single node.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Multiplex Markov Chains: Convection Cycles and Optimality: Multiplex networks are a common modeling framework for interconnected systems\nand multimodal data, yet we still lack fundamental insights for how\nmultiplexity affects stochastic processes. We introduce a novel ``Markov chains\nof Markov chains'' model called multiplex Markov chains (MMCs) such that with\nprobably $(1-\\omega)\\in [0,1]$ random walkers remain in the same layer and\nfollow (layer-specific) intralayer Markov chains, whereas with probability\n$\\omega$ they move to different layers following (node-specific) interlayer\nMarkov chains. One main finding is the identification of multiplex convection,\nwhereby a stationary distribution exhibits circulating flows that involve\nmultiple layers. Convection cycles are well understood in fluids, but are\ninsufficiently explored on networks. Our experiments reveal that one mechanism\nfor convection is the existence of imbalances for the (intralayer) degrees of\nnodes in different layers. To gain further insight, we employ spectral\nperturbation theory to characterize the stationary distribution for the limits\nof small and large $\\omega$, and we show that MMCs inherently exhibit\noptimality for intermediate $\\omega$ in terms of their convergence rate and the\nextent of convection. As an application, we conduct an MMC-based analysis of\nbrain-activity data, finding MMCs to differ between healthy persons and those\nwith Alzheimer's disease. Overall, our work suggests MMCs and convection as two\nimportant new directions for network-related research.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Flow of temporal network properties under local aggregation and time\n  shuffling: a tool for characterizing, comparing and classifying temporal\n  networks: Although many tools have been developed and employed to characterize temporal\nnetworks, the issue of how to compare them remains largely open. It depends\nindeed on what features are considered as relevant, and on the way the\ndifferences in these features are quantified. In this paper, we propose to\ncharacterize temporal networks through their behaviour under general\ntransformations that are local in time: (i) a local time shuffling, which\ndestroys correlations at time scales smaller than a given scale $b$, while\npreserving large time scales, and (ii) a local temporal aggregation on time\nwindows of length $n$. By varying $b$ and $n$, we obtain a flow of temporal\nnetworks, and flows of observable values, which encode the phenomenology of the\ntemporal network on multiple time scales. We use a symbolic approach to\nsummarize these flows into labels (strings of characters) describing their\ntrends. These labels can then be used to compare temporal networks, validate\nmodels, or identify groups of networks with similar labels. Our procedure can\nbe applied to any temporal network and with an arbitrary set of observables,\nand we illustrate it on an ensemble of data sets describing face-to-face\ninteractions in various contexts, including both empirical and synthetic data.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Geometrical effects on mobility: In this paper we analyze the effect of randomly deleting streets of a\nsynthetic city on the statistics of displacements. Our city is constituted\ninitially by a set of streets that form a regular tessellation of the euclidean\nplane. Therefore we will have three types of cities, formed by squares,\ntriangles or hexagons. We studied the complementary cumulative distribution\nfunction for displacements (CCDF). For the whole set of streets the CCDF is a\nstretched exponential, and as streets are deleted this function becomes a\nlinear function and then two clear different exponentials. This behavior is\nqualitatively the same for all the tessellations. Most of this functions has\nbeen reported in the literature when studying the displacements of individuals\nbased on cell data trajectories and GPS information. However, in the light of\nthis work, the appearance of different functions for displacements CCDF can be\nattributed to the connectivity of the underlying street network. It is\nremarkably that for some proportion of streets we got a linear function for\nsuch function, and as far as we know this behavior has not been reported nor\nconsidered. Therefore, it is advisable to analyze experimental in the light of\nconnectivity of the street network to make correlations with the present work.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Centrality anomalies in complex networks as a result of model\n  over-simplification: Tremendous advances have been made in our understanding of the properties and\nevolution of complex networks. These advances were initially driven by\ninformation-poor empirical networks and theoretical analysis of unweighted and\nundirected graphs. Recently, information-rich empirical data complex networks\nsupported the development of more sophisticated models that include edge\ndirectionality and weight properties, and multiple layers. Many studies still\nfocus on unweighted undirected description of networks, prompting an essential\nquestion: how to identify when a model is simpler than it must be? Here, we\nargue that the presence of centrality anomalies in complex networks is a result\nof model over-simplification. Specifically, we investigate the well-known\nanomaly in betweenness centrality for transportation networks, according to\nwhich highly connected nodes are not necessarily the most central. Using a\nbroad class of network models with weights and spatial constraints and four\nlarge data sets of transportation networks, we show that the unweighted\nprojection of the structure of these networks can exhibit a significant\nfraction of anomalous nodes compared to a random null model. However, the\nweighted projection of these networks, compared with an appropriated null\nmodel, significantly reduces the fraction of anomalies observed, suggesting\nthat centrality anomalies are a symptom of model over-simplification. Because\nlack of information-rich data is a common challenge when dealing with complex\nnetworks and can cause anomalies that misestimate the role of nodes in the\nsystem, we argue that sufficiently sophisticated models be used when anomalies\nare detected.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Simulating Delay in Seeking Treatment for Stroke due to COVID-19\n  Concerns with a Hybrid Agent-Based and Equation-Based Model: COVID-19 has caused tremendous strain on healthcare systems worldwide. At the\nsame time, concern within the population over this strain and the chances of\nbecoming infected has potentially reduced the likelihood of people seeking\nmedical treatment for other health events. Stroke is a medical emergency and\nswift treatment can make a large difference in patient outcomes. Understanding\nhow concern over the COVID-19 pandemic might impact the time delay in seeking\ntreatment after a stroke can be important in understanding both the long term\ncost implications and how to target individuals during another pandemic\nscenario to remind them of the importance of seeking treatment immediately. We\npresent a hybrid agent-based and equation-based model to simulate the delay in\nseeking treatment for stroke due to concerns over COVID-19 and show that even\nsmall changes in behaviour impact the average delay in seeking treatment for\nthe population. This delay could potentially impact the outcomes for stroke\npatients and future healthcare costs to support them. We find that introducing\ncontrol measures and having multiple smaller peaks of the pandemic results in\nless delay in seeking treatment compared to a scenario with one large peak.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Entropy of labeled versus unlabeled networks: The structure of a network is an unlabeled graph, yet graphs in most models\nof complex networks are labeled by meaningless random integers. Is the\nassociated labeling noise always negligible, or can it overpower the\nnetwork-structural signal? To address this question, we introduce and consider\nthe sparse unlabeled versions of popular network models, and compare their\nentropy against the original labeled versions. We show that labeled and\nunlabeled Erdos-Renyi graphs are entropically equivalent, even though their\ndegree distributions are very different. The labeled and unlabeled versions of\nthe configuration model may have different prefactors in their leading entropy\nterms, although this remains conjectural. Our main results are upper and lower\nbounds for the entropy of labeled and unlabeled one-dimensional random\ngeometric graphs. We show that their unlabeled entropy is negligible in\ncomparison with the labeled entropy. This means that in sparse networks the\nentropy of meaningless labeling may dominate the entropy of the network\nstructure. The main implication of this result is that the common practice of\nusing exchangeable models to reason about real-world networks with\ndistinguishable nodes may introduce uncontrolled aberrations into conclusions\nmade about these networks, suggesting a need for a thorough reexamination of\nthe statistical foundations and key results of network science.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Zipf's law and urban scaling: Hypotheses towards a Unified Urban Theory: We propose hypotheses describing the empirical finding of an association\nbetween the exponents of urban GDP scaling and Zipf's law for cities. These\nhypotheses represent various combinations of directional or reciprocal causal\nlinks between the two phenomena and include inter- and intra-city processes.\nFuture theories and models can be motivated with and categorized according to\nthese hypotheses. This paper intends to stimulate the discussion around the\nprocesses behind these phenomena and pave the way to a Unified Urban Theory.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Blockbusters, Bombs and Sleepers: The income distribution of movies: The distribution of gross earnings of movies released each year show a\ndistribution having a power-law tail with Pareto exponent $\\alpha \\simeq 2$.\nWhile this offers interesting parallels with income distributions of\nindividuals, it is also clear that it cannot be explained by simple asset\nexchange models, as movies do not interact with each other directly. In fact,\nmovies (because of the large quantity of data available on their earnings)\nprovide the best entry-point for studying the dynamics of how ``a hit is born''\nand the resulting distribution of popularity (of products or ideas). In this\npaper, we show evidence of Pareto law for movie income, as well as, an analysis\nof the time-evolution of income.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Activity networks determine project performance: Projects are characterised by activity networks with a critical path, a\nsequence of activities from start to end, that must be finished on time to\ncomplete the project on time. Watching over the critical path is the project\nmanager's strategy to ensure timely project completion. This intense focus on a\nsingle path contrasts the broader complex structure of the activity network,\nand is due to our poor understanding on how that structure influences this\ncritical path. Here, we use a generative model and detailed data from 77 real\nworld projects (plus 10 billion dollars total budget) to demonstrate how this\nnetwork structure forces us to look beyond the critical path. We introduce a\nduplication-split model of project schedules that yields (i) identical\npower-law in- and-out degree distributions and (ii) a vanishing fraction of\ncritical path activities with schedule size. These predictions are corroborated\nin real projects. We demonstrate that the incidence of delayed activities in\nreal projects is consistent with the expectation from percolation theory in\ncomplex networks. We conclude that delay propagation in project schedules is a\nnetwork property and it is not confined to the critical path.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Consequences of nonconformist behaviors in a continuous opinion model: We investigate opinion formation in a kinetic exchange opinion model, where\nopinions are represented by numbers in the real interval $[-1,1]$ and agents\nare typified by the individual degree of conviction about the opinion that they\nsupport. Opinions evolve through pairwise interactions governed by competitive\npositive and negative couplings, that promote imitation and dissent,\nrespectively. The model contemplates also another type of nonconformity such\nthat agents can occasionally choose their opinions independently of the\ninteractions with other agents. The steady states of the model as a function of\nthe parameters that describe conviction, dissent and independence are analyzed,\nwith particular emphasis on the emergence of extreme opinions. Then, we\ncharacterize the possible ordered and disordered phases and the occurrence or\nsuppression of phase transitions that arise spontaneously due to the disorder\nintroduced by the heterogeneity of the agents and/or their interactions.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Pluralistic Modeling of Complex Systems: The modeling of complex systems such as ecological or socio-economic systems\ncan be very challenging. Although various modeling approaches exist, they are\ngenerally not compatible and mutually consistent, and empirical data often do\nnot allow one to decide what model is the right one, the best one, or most\nappropriate one. Moreover, as the recent financial and economic crisis shows,\nrelying on a single, idealized model can be very costly. This contribution\ntries to shed new light on problems that arise when complex systems are\nmodeled. While the arguments can be transferred to many different systems, the\nrelated scientific challenges are illustrated for social, economic, and traffic\nsystems. The contribution discusses issues that are sometimes overlooked and\ntries to overcome some frequent misunderstandings and controversies of the\npast. At the same time, it is highlighted how some long-standing scientific\npuzzles may be solved by considering non-linear models of heterogeneous agents\nwith spatio-temporal interactions. As a result of the analysis, it is concluded\nthat a paradigm shift towards a pluralistic or possibilistic modeling approach,\nwhich integrates multiple world views, is overdue. In this connection, it is\nargued that it can be useful to combine many different approaches to obtain a\ngood picture of reality, even though they may be inconsistent. Finally, it is\nidentified what would be profitable areas of collaboration between the\nsocio-economic, natural, and engineering sciences.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Dimension matters when modeling network communities in hyperbolic spaces: Over the last decade, random hyperbolic graphs have proved successful in\nproviding geometric explanations for many key properties of real-world\nnetworks, including strong clustering, high navigability, and heterogeneous\ndegree distributions. These properties are ubiquitous in systems as varied as\nthe internet, transportation, brain or epidemic networks, which are thus\nunified under the hyperbolic network interpretation on a surface of constant\nnegative curvature. Although a few studies have shown that hyperbolic models\ncan generate community structures, another salient feature observed in real\nnetworks, we argue that the current models are overlooking the choice of the\nlatent space dimensionality that is required to adequately represent clustered\nnetworked data. We show that there is an important qualitative difference\nbetween the lowest-dimensional model and its higher-dimensional counterparts\nwith respect to how similarity between nodes restricts connection\nprobabilities. Since more dimensions also increase the number of nearest\nneighbors for angular clusters representing communities, considering only one\nmore dimension allows us to generate more realistic and diverse community\nstructures.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A proposal for a quantitative indicator of original research output: The use of quantitative indicators of scientific productivity seems now quite\nwidespread for assessing researchers and research institutions. There is a\ngeneral perception, however, that these indicators are not necessarily\nrepresentative of the originality of the research carried out, being primarily\nindicative of a more or less prolific scientific activity and of the size of\nthe targeted scientific subcommunity. We first discuss some of the drawbacks of\nthe broadly adopted $h$-index and of the fact that it represents, in an average\nsense, an indicator derivable from the total number of citations. Then we\npropose an indicator which, although not immune from biases, seems more in line\nwith the general expectations for quantifying what is typically considered\noriginal work. Qualitative arguments on how different indicators may shape the\nfuture of science are finally discussed.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Structure of 311 Service Requests as a Signature of Urban Location: While urban systems demonstrate high spatial heterogeneity, many urban\nplanning, economic and political decisions heavily rely on a deep understanding\nof local neighborhood contexts. We show that the structure of 311 Service\nRequests enables one possible way of building a unique signature of the local\nurban context, thus being able to serve as a low-cost decision support tool for\nurban stakeholders. Considering examples of New York City, Boston and Chicago,\nwe demonstrate how 311 Service Requests recorded and categorized by type in\neach neighborhood can be utilized to generate a meaningful classification of\nlocations across the city, based on distinctive socioeconomic profiles.\nMoreover, the 311-based classification of urban neighborhoods can present\nsufficient information to model various socioeconomic features. Finally, we\nshow that these characteristics are capable of predicting future trends in\ncomparative local real estate prices. We demonstrate 311 Service Requests data\ncan be used to monitor and predict socioeconomic performance of urban\nneighborhoods, allowing urban stakeholders to quantify the impacts of their\ninterventions.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A short review and discussion about the limiting factors, which can hold\n  back wind and photovoltaic power plants from its presently exponential growth: The present nearly exponential growth of the cumulative installation power of\nwind and photovoltaic power plants is very promising in the context of a rapid\nreduction of the emission of CO2, which can lead to a swift mitigation climate\nchange if this growth behavior is maintained in the future. In this review we\nidentify a set of ten limiting factors that can restrain, or halt back, an\nexponential growth of these variable renewable power plants (VREs) in the\nfuture. If the exponential growth slows down to a linear growth, such a case\nwould extent considerably the time to reduce the CO2 emissions and the related\nmitigation of climate change. A scenario that would result to a much higher\nrisk of a future continuation of the economic grow and thrive of the global\neconomy and human society. We argue that if these limiting factors are\nadequately addressed to an exponential expansion VRE power plants is feasible\nand can result in the nearly complete avoidance of CO2 emissions as related to\nenergy generation and consumption in 2030.",
        "category": "physics_soc-ph"
    },
    {
        "text": "How the rich get richer: In our model, $n$ traders interact with each other and with a central bank;\nthey are taxed on the money they make, some of which is dissipated away by\ncorruption. A generic feature of our model is that the richest trader always\nwins by 'consuming' all the others: another is the existence of a threshold\nwealth, below which all traders go bankrupt. The two-trader case is examined in\ndetail,in the socialist and capitalist limits, which generalise easily to\n$n>2$. In its mean-field incarnation, our model exhibits a two-time-scale\nglassy dynamics, as well as an astonishing universality.When preference is\ngiven to local interactions in finite neighbourhoods,a novel feature emerges:\ninstead of at most one overall winner in the system,finite numbers of winners\nemerge, each one the overlord of a particular region.The patterns formed by\nsuch winners (metastable states) are very much a consequence of initial\nconditions, so that the fate of the marketplace is ruled by its past history;\nhysteresis is thus also manifested.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Knowing the past improves cooperation in the future: Cooperation is the cornerstone of human evolutionary success. Like no other\nspecies, we champion the sacrifice of personal benefits for the common good,\nand we work together to achieve what we are unable to achieve alone. Knowledge\nand information from past generations is thereby often instrumental in ensuring\nwe keep cooperating rather than deteriorating to less productive ways of\ncoexistence. Here we present a mathematical model based on evolutionary game\ntheory that shows how using the past as the benchmark for evolutionary success,\nrather than just current performance, significantly improves cooperation in the\nfuture. Interestingly, the details of just how the past is taken into account\nplay only second-order importance, whether it be a weighted average of past\npayoffs or just a single payoff value from the past. Cooperation is promoted\nbecause information from the past disables fast invasions of defectors, thus\nenhancing the long-term benefits of cooperative behavior.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Evolution of Labour Supply in Ridesourcing: Contrary to traditional transit services, supply in ridesourcing systems\nemerges from individual labour decisions of gig workers. The effect of\ndecentralisation in supply on the evolution of on-demand transit services is\nlargely unknown. To this end, we propose a dynamic model comprising of the\nsubsequent supply-side processes: (i) initial exposure to information about the\nplatform, (ii) a long-term registration decision, and (iii) daily participation\ndecisions, subject to day-to-day learning based on within-day matching\noutcomes. We construct a series of experiments to study the effect of supply\nmarket properties and pricing strategies. We find that labour supply in\nridesourcing may be non-linear and undergo several transitions, inducing\nsignificant variations in income levels and level of service over time. Our\nresults provide indications that the ridesourcing market may benefit from a cap\nin supply and regulation of the commission fee.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Workforce Development Through Research-Based, Plasma-Focused Activities: This report is a summary of the mini-conference Workforce Development Through\nResearch-Based, Plasma-Focused Science Education and Public Engagement held\nduring the 2022 American Physical Society Division of Plasma Physics (APS DPP)\nannual meeting. The motivation for organizing this mini-conference originates\nfrom recent studies and community-based reports highlighting important issues\nwith the current state of the plasma workforce. Here we summarize the main\nfindings presented in the two speaker sessions of the mini-conference, the\nchallenges and recommendations identified in the discussion sessions, and the\nresults from a post-conference survey. We further provide information on\ninitiatives and studies presented at the mini-conference, along with references\nto further resources.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Temporal Dynamics of Connectivity and Epidemic Properties of Growing\n  Networks: Traditional mathematical models of epidemic disease had for decades\nconventionally considered static structure for contacts. Recently, an upsurge\nof theoretical inquiry has strived towards rendering the models more realistic\nby incorporating the temporal aspects of networks of contacts, societal and\nonline, that are of interest in the study of epidemics (and other similar\ndiffusion processes). However, temporal dynamics have predominantly focused on\nlink fluctuations and nodal activities, and less attention has been paid to the\ngrowth of the underlying network. Many real networks grow: online networks are\nevidently in constant growth, and societal networks can grow due to migration\nflux and reproduction. The effect of network growth on the epidemic properties\nof networks is hitherto unknown---mainly due to the predominant focus of the\nnetwork growth literature on the so-called steady-state. This paper takes a\nstep towards alleviating this gap. We analytically study the degree dynamics of\na given arbitrary network that is subject to growth. We use the theoretical\nfindings to predict the epidemic properties of the network as a function of\ntime. We observe that the introduction of new individuals into the network can\nenhance or diminish its resilience against endemic outbreaks, and investigate\nhow this regime shift depends upon the connectivity of newcomers and on how\nthey establish connections to existing nodes. Throughout, theoretical findings\nare corroborated with Monte Carlo simulations over synthetic and real networks.\nThe results shed light on the effects of network growth on the future epidemic\nproperties of networks, and offers insights for devising a-priori immunization\nstrategies.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Kinetic models for epidemic dynamics with social heterogeneity: We introduce a mathematical description of the impact of sociality in the\nspread of infectious diseases by integrating an epidemiological dynamics with a\nkinetic modeling of population-based contacts. The kinetic description leads to\nstudy the evolution over time of Boltzmann-type equations describing the number\ndensities of social contacts of susceptible, infected and recovered\nindividuals, whose proportions are driven by a classical SIR-type compartmental\nmodel in epidemiology. Explicit calculations show that the spread of the\ndisease is closely related to moments of the contact distribution. Furthermore,\nthe kinetic model allows to clarify how a selective control can be assumed to\nachieve a minimal lockdown strategy by only reducing individuals undergoing a\nvery large number of daily contacts. We conduct numerical simulations which\nconfirm the ability of the model to describe different phenomena characteristic\nof the rapid spread of an epidemic. Motivated by the COVID-19 pandemic, a last\npart is dedicated to fit numerical solutions of the proposed model with\ninfection data coming from different European countries.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Normalized Mutual Information to evaluate overlapping community finding\n  algorithms: Given the increasing popularity of algorithms for overlapping clustering, in\nparticular in social network analysis, quantitative measures are needed to\nmeasure the accuracy of a method. Given a set of true clusters, and the set of\nclusters found by an algorithm, these sets of clusters must be compared to see\nhow similar or different the sets are. A normalized measure is desirable in\nmany contexts, for example assigning a value of 0 where the two sets are\ntotally dissimilar, and 1 where they are identical. A measure based on\nnormalized mutual information, [1], has recently become popular. We demonstrate\nunintuitive behaviour of this measure, and show how this can be corrected by\nusing a more conventional normalization. We compare the results to that of\nother measures, such as the Omega index [2].",
        "category": "physics_soc-ph"
    },
    {
        "text": "Machine Learning Partners in Criminal Networks: Recent research has shown that criminal networks have complex organizational\nstructures, but whether this can be used to predict static and dynamic\nproperties of criminal networks remains little explored. Here, by combining\ngraph representation learning and machine learning methods, we show that\nstructural properties of political corruption, police intelligence, and money\nlaundering networks can be used to recover missing criminal partnerships,\ndistinguish among different types of criminal and legal associations, as well\nas predict the total amount of money exchanged among criminal agents, all with\noutstanding accuracy. We also show that our approach can anticipate future\ncriminal associations during the dynamic growth of corruption networks with\nsignificant accuracy. Thus, similar to evidence found at crime scenes, we\nconclude that structural patterns of criminal networks carry crucial\ninformation about illegal activities, which allows machine learning methods to\npredict missing information and even anticipate future criminal behavior.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Reduction from non-Markovian to Markovian dynamics: The case of aging in\n  the noisy-voter model: We study memory dependent binary-state dynamics, focusing on the noisy-voter\nmodel. This is a non-Markovian process if we consider the set of binary states\nof the population as the description variables, or Markovian if we incorporate\n\"age\", related to the time one has spent holding the same state, as a part of\nthe description. We show that, in some cases, the model can be reduced to an\neffective Markovian process, where the age distribution of the population\nrapidly equilibrates to a quasi-steady state, while the global state of the\nsystem is out of equilibrium. This effective Markovian process shares the same\nphenomenology of the non-linear noisy-voter model and we establish a clear\nparallelism between these two extensions of the noisy-voter model.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Application of a cognitive-inspired algorithm for detecting communities\n  in mobility networks: The emergence and the global adaptation of mobile devices has influenced\nhuman interactions at the individual, community, and social levels leading to\nthe so called Cyber-Physical World (CPW) convergence scenario [1]. One of the\nmost important features of CPW is the possibility of exploiting information\nabout the structure of the social communities of users, revealed by joint\nmovement patterns and frequency of physical co-location. Mobile devices of\nusers that belong to the same social community are likely to \"see\" each other\n(and thus be able to communicate through ad-hoc networking techniques) more\nfrequently and regularly than devices outside the community. In mobile\nopportunistic networks, this fact can be exploited, for example, to optimize\nnetworking operations such as forwarding and dissemination of messages. In this\npaper we present the application of a cognitive-inspired algorithm [2,3,4] for\nrevealing the structure of these dynamic social networks (simulated by the HCMM\nmodel [5]) using information about physical encounters logged by the users'\nmobile devices. The main features of our algorithm are: (i) the capacity of\ndetecting social communities induced by physical co-location of users through\ndistributed algorithms; (ii) the capacity to detect users belonging to more\ncommunities (thus acting as bridges across them), and (iii) the capacity to\ndetect the time evolution of communities.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Scale-Free Networks beyond Power-Law Degree Distribution: Complex networks across various fields are often considered to be scale free\n-- a statistical property usually solely characterized by a power-law\ndistribution of the nodes' degree $k$. However, this characterization is\nincomplete. In real-world networks, the distribution of the degree-degree\ndistance $\\eta$, a simple link-based metric of network connectivity similar to\n$k$, appears to exhibit a stronger power-law distribution than $k$. While\noffering an alternative characterization of scale-freeness, the discovery of\n$\\eta$ raises a fundamental question: do the power laws of $k$ and $\\eta$\nrepresent the same scale-freeness? To address this question, here we\ninvestigate the exact asymptotic {relationship} between the distributions of\n$k$ and $\\eta$, proving that every network with a power-law distribution of $k$\nalso has a power-law distribution of $\\eta$, but \\emph{not} vice versa. This\nprompts us to introduce two network models as counterexamples that have a\npower-law distribution of $\\eta$ but not $k$, constructed using the\npreferential attachment and fitness mechanisms, respectively. Both models show\npromising accuracy by fitting only one model parameter each when modeling\nreal-world networks. Our findings suggest that $\\eta$ is a more suitable\nindicator of scale-freeness and can provide a deeper understanding of the\nuniversality and underlying mechanisms of scale-free networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Limits of Phenomenology: From Behaviorism to Drug Testing and\n  Engineering Design: It is widely believed that theory is useful in physics because it describes\nsimple systems and that strictly empirical phenomenological approaches are\nnecessary for complex biological and social systems. Here we prove based upon\nan analysis of the information that can be obtained from experimental\nobservations that theory is even more essential in the understanding of complex\nsystems. Implications of this proof revise the general understanding of how we\ncan understand complex systems including the behaviorist approach to human\nbehavior, problems with testing engineered systems, and medical experimentation\nfor evaluating treatments and the FDA approval of medications. Each of these\napproaches are inherently limited in their ability to characterize real world\nsystems due to the large number of conditions that can affect their behavior.\nModels are necessary as they can help to characterize behavior without\nrequiring observations for all possible conditions. The testing of models by\nempirical observations enhances the utility of those observations. For systems\nfor which adequate models have not been developed, or are not practical, the\nlimitations of empirical testing lead to uncertainty in our knowledge and risks\nin individual, organizational and social policy decisions. These risks should\nbe recognized and inform our decisions.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Contraction of online response to major events: Quantifying regularities in behavioral dynamics is of crucial interest for\nunderstanding collective social events such as panics or political revolutions.\nWith the widespread use of digital communication media it has become possible\nto study massive data streams of user-created content in which individuals\nexpress their sentiments, often towards a specific topic. Here we investigate\nmessages from various online media created in response to major, collectively\nfollowed events such as sport tournaments, presidential elections or a large\nsnow storm. We relate content length and message rate, and find a systematic\ncorrelation during events which can be described by a power law relation - the\nhigher the excitation the shorter the messages. We show that on the one hand\nthis effect can be observed in the behavior of most regular users, and on the\nother hand is accentuated by the engagement of additional user demographics who\nonly post during phases of high collective activity. Further, we identify the\ndistributions of content lengths as lognormals in line with statistical\nlinguistics, and suggest a phenomenological law for the systematic dependence\nof the message rate to the lognormal mean parameter. Our measurements have\npractical implications for the design of micro-blogging and messaging services.\nIn the case of the existing service Twitter, we show that the imposed limit of\n140 characters per message currently leads to a substantial fraction of\npossibly dissatisfying to compose tweets that need to be truncated by their\nusers.",
        "category": "physics_soc-ph"
    },
    {
        "text": "How Much is the Whole Really More than the Sum of its Parts? 1 + 1 =\n  2.5: Superlinear Productivity in Collective Group Actions: In a variety of open source software projects, we document a superlinear\ngrowth of production ($R \\sim c^\\beta$) as a function of the number of active\ndevelopers $c$, with $\\beta \\simeq 4/3$ with large dispersions. For a typical\nproject in this class, doubling of the group size multiplies typically the\noutput by a factor $2^\\beta=2.5$, explaining the title. This superlinear law is\nfound to hold for group sizes ranging from 5 to a few hundred developers. We\npropose two classes of mechanisms, {\\it interaction-based} and {\\it large\ndeviation}, along with a cascade model of productive activity, which unifies\nthem. In this common framework, superlinear productivity requires that the\ninvolved social groups function at or close to criticality, in the sense of a\nsubtle balance between order and disorder. We report the first empirical test\nof the renormalization of the exponent of the distribution of the sizes of\nfirst generation events into the renormalized exponent of the distribution of\nclusters resulting from the cascade of triggering over all generation in a\ncritical branching process in the non-meanfield regime. Finally, we document a\nsize effect in the strength and variability of the superlinear effect, with\nsmaller groups exhibiting widely distributed superlinear exponents, some of\nthem characterizing highly productive teams. In contrast, large groups tend to\nhave a smaller superlinearity and less variability.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Complex networks and public funding: the case of the 2007-2013 Italian\n  program: In this paper we apply techniques of complex network analysis to data sources\nrepresenting public funding programs and discuss the importance of the\nconsidered indicators for program evaluation. Starting from the Open Data\nrepository of the 2007-2013 Italian Program Programma Operativo Nazionale\n'Ricerca e Competitivit\\`a' (PON R&C), we build a set of data models and\nperform network analysis over them. We discuss the obtained experimental\nresults outlining interesting new perspectives that emerge from the application\nof the proposed methods to the socio-economical evaluation of funded programs.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Modeling temporal networks with bursty activity patterns of nodes and\n  links: The concept of temporal networks provides a framework to understand how the\ninteraction between system components changes over time. In empirical\ncommunication data, we often detect non-Poissonian, so-called bursty behavior\nin the activity of nodes as well as in the interaction between nodes. However,\nsuch reconciliation between node burstiness and link burstiness cannot be\nexplained if the interaction processes on different links are independent of\neach other. This is because the activity of a node is the superposition of the\ninteraction processes on the links incident to the node and the superposition\nof independent bursty point processes is not bursty in general. Here we\nintroduce a temporal network model based on bursty node activation and show\nthat it leads to heavy-tailed inter-event time distributions for both node\ndynamics and link dynamics. Our analysis indicates that activation processes\nintrinsic to nodes give rise to dynamical correlations across links. Our\nframework offers a way to model competition and correlation between links,\nwhich is key to understanding dynamical processes in various systems.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Network connectivity optimization: An evaluation of heuristics applied\n  to complex networks and a transportation case study: Network optimization has generally been focused on solving network flow\nproblems, but recently there have been investigations into optimizing network\ncharacteristics. Optimizing network connectivity to maximize the number of\nnodes within a given distance to a focal node and then minimizing the number\nand length of additional connections has not been as thoroughly explored, yet\nis important in several domains including transportation planning,\ntelecommunications networks, and geospatial analysis. We compare several\nheuristics to explore this network connectivity optimization problem with the\nuse of random networks, including the introduction of two planar random\nnetworks that are useful for spatial network simulation research, and a\nreal-world case study from urban planning and public health. We observe\nsignificant variation between nodal characteristics and optimal connections\nacross network types. This result along with the computational costs of the\nsearch for optimal solutions highlights the difficulty of finding effective\nheuristics. A novel genetic algorithm is proposed and we find this optimization\nheuristic outperforms existing techniques and describe how it can be applied to\nother combinatorial and dynamic problems.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Investigation of Pedestrian Dynamics in Circle Antipode Experiments: To explore the pedestrian motion navigation and conflict reaction mechanisms\nin practice, we organized a series of circle antipode experiments. In the\nexperiments, pedestrians are uniformly initialized on the circle and required\nto leave for their antipodal positions simultaneously. On the one hand, a\nconflicting area is naturally formulated in the central region due to the\nconverged shortest routes, so the practical conflict avoidance behaviors can be\nfully explored. On the other hand, the symmetric experimental conditions of\npedestrians, e.g., symmetric starting points, symmetric destination points, and\nsymmetric surroundings lay the foundation for further quantitative comparisons\namong participants. The pedestrian trajectories in the experiments are\nrecognized and rotated, and several aspects, e.g., the trajectory space\ndistribution, route length, travel time, velocity distribution, and\ntime-series, are investigated. It is found that: (1) Pedestrians prefer the\nright-hand side during the experiments; (2) The route length is as the law of\nlog-normal distribution, the route potential obeys the exponential\ndistribution, and the travel time is normally distributed as well as the speed;\n(3) Taking the short routes unexpectedly cost pedestrians plenty of travel\ntime, while detour seems to be a time-saving decision. What's more, the series\nof experiments can be regarded as a basis of the model evaluation benefit from\nthe serious conflicts and the symmetric conditions. The evaluation framework\ncontains four distribution indexes and two time series indexes in space and\ntime dimensions, and they are respectively graded A traditional social force\nmodel and a Voronoi diagram based modification are introduced to test the\nevaluation framework. The evaluation results show that the framework is\nbeneficial to evaluate pedestrian models and even reflects the minor\ndifferences between the models.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Epidemic Spreading in Random Rectangular Networks: The use of network theory to model disease propagation on populations\nintroduces important elements of reality to the classical epidemiological\nmodels. The use of random geometric graphs (RGG) is one of such network models\nthat allows for the consideration of spatial properties on disease propagation.\nIn certain real-world scenarios -like in the analysis of a disease propagating\nthrough plants- the shape of the plots and fields where the host of the disease\nis located may play a fundamental role on the propagation dynamics. Here we\nconsider a generalization of the RGG to account for the variation of the shape\nof the plots/fields where the hosts of a disease are allocated. We consider a\ndisease propagation taking place on the nodes of a random rectangular graph\n(RRG) and we consider a lower bound for the epidemic threshold of a\nSusceptible-Infected-Susceptible (SIS) or Susceptible-Infected-Recovered (SIR)\nmodel on these networks. Using extensive numerical simulations and based on our\nanalytical results we conclude that (ceteris paribus) the elongation of the\nplot/field in which the nodes are distributed makes the network more resilient\nto the propagation of a disease due to the fact that the epidemic threshold\nincreases with the elongation of the rectangle. These results agree with\naccumulated empirical evidence and simulation results about the propagation of\ndiseases on plants in plots/fields of the same area and different shapes.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Topology-based Approximations for $\\mathcal{N}-1$ Contingency\n  Constraints in Power Transmission Networks: It is crucial for maintaining the security of supply that transmission\nnetworks continue to operate even if a single line fails. Modeling $\\mathcal{N}\n- 1$ security in power system capacity expansion problems introduces many extra\nconstraints if all possible outages are accounted for, which leads to a high\ncomputational burden. Typical approaches to avoid this burden consider only a\nsubset of possible outages relevant to a given dispatch situation. However,\nthis relies on knowing the dispatch situation beforehand, and it is not\nsuitable for investment optimization problems where the generation fleet is not\nknown in advance. In this paper, we introduce a heuristic approach to model the\nfully secured $\\mathcal{N}-1$ feasible space using a smaller number of\nconstraints in a way that only depends on the topology of transmission\nnetworks. In our proposed approach, the network's security is modelled by\ncomparing the polytope of the feasible space of nodal net power obtained from\nthe security-constrained linearized AC optimal power flow problem. To\napproximate this polytope, a buffer capacity factor is defined for transmission\nlines in the $\\mathcal{N}-0$ secure case, thereby avoiding the introduction of\nmany additional constraints. In this way, three approaches are introduced for\nobtaining a buffer capacity factor consisting of approximate, robust and\nline-specific approaches. Finally, the performance of our proposed approaches\nis assessed in different scales of transmission networks for determining the\nproposed buffer capacity factors, contingency analysis and economic evaluation.\nMoreover, we find that our proposed heuristics provide excellent approximations\nof the fully secured $\\mathcal{N}-1$ solutions with a much lower computational\nburden.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Random matrices and the New York City subway system: We analyze subway arrival times in the New York City subway system. We find\nregimes where the gaps between trains exhibit both (unitarily invariant) random\nmatrix statistics and Poisson statistics. The departure from random matrix\nstatistics is captured by the value of the Coulomb potential along the subway\nroute. This departure becomes more pronounced as trains make more stops.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Instability of oscillations in the Rosenzweig-MacArthur model of one\n  consumer and two resources: The system of two resources $R_1$, $R_2$ and one consumer $C$ is investigated\nwithin the Rosenzweig-MacArthur model with Holling type II functional response.\nThe rates $\\beta_i$ of consumption of resources $i=1,2$ are coupled by the\ncondition $\\beta_1+\\beta_2=1$. The dynamic switching is introduced by a\nmaximization of $C$: $d\\beta_1/dt=(1/\\tau) dC/d\\beta_1$, where the\ncharacteristic time $\\tau$ is large but finite. The space of parameters where\nboth resources coexist is explored numerically. The results indicate that\noscillations of $C$ and mutually synchronized $R_i$ which appear at\n$\\beta_i=0.5$ are destabilized for $\\beta_i$ larger or smaller. Then, the\nsystem is driven to one of fixed points where either $\\beta_1>0.5$ and\n$R_1<R_2$ or the opposite. This behaviour is explained as an inability of the\nconsumer to change the preferred resource, once it is chosen.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Cooperative Dynamics of Neighborhood Economic Status in Cities: We significantly extend our earlier variant of the Schelling model,\nincorporating a neighborhood Potential function as well as an agent wealth gain\nfunction to study the long term evolution of the economic status of\nneighborhoods in cities. We find that the long term patterns of neighborhood\nrelative economic status (RES) simulated by this model reasonably replicate the\nempirically observed patterns from American cities. Specifically, we find that\nlarger fractions of rich and poor neighborhoods tend to, on average, retain\nstatus for longer than lower- and upper-middle wealth neighborhoods. The use of\na Potential function that measures the relative wealth of neighborhoods as the\nbasis for agent wealth gain and agent movement appears critical to explaining\nthese emergent patterns of neighborhood RES. This also suggests that the\nempirically observed RES patterns could indeed be universal and that we would\nexpect to see these patterns repeated for cities around the world. Observing\nRES behavior over even longer periods of time, the model predicts that the\nfraction of poor neighborhoods retaining status remains almost constant over\nextended periods of time, while the fraction of middle-wealth and rich\nneighborhoods retaining status reduces significantly over time, tending to\nzero.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A message-passing approach to epidemic tracing and mitigation with apps: With the hit of new pandemic threats, scientific frameworks are needed to\nunderstand the unfolding of the epidemic. The use of mobile apps that are able\nto trace contacts is of utmost importance in order to control new infected\ncases and contain further propagation. Here we present a theoretical approach\nusing both percolation and message--passing techniques, to the role of contact\ntracing, in mitigating an epidemic wave. We show how the increase of the app\nadoption level raises the value of the epidemic threshold, which is eventually\nmaximized when high-degree nodes are preferentially targeted. Analytical\nresults are compared with extensive Monte Carlo simulations showing good\nagreement for both homogeneous and heterogeneous networks. These results are\nimportant to quantify the level of adoption needed for contact-tracing apps to\nbe effective in mitigating an epidemic.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Tie-decay networks in continuous time and eigenvector-based centralities: Network theory is a useful framework for studying interconnected systems of\ninteracting entities. Many networked systems evolve continuously in time, but\nmost existing methods for the analysis of time-dependent networks rely on\ndiscrete or discretized time. In this paper, we propose an approach for\nstudying networks that evolve in continuous time by distinguishing between\n\\emph{interactions}, which we model as discrete contacts, and \\emph{ties},\nwhich encode the strengths of relationships as functions of time. To illustrate\nour tie-decay network formalism, we adapt the well-known PageRank centrality\nscore to our tie-decay framework in a mathematically tractable and\ncomputationally efficient way. We apply this framework to a synthetic example\nand then use it to study a network of retweets during the 2012 National Health\nService controversy in the United Kingdom. Our work also provides guidance for\nsimilar generalizations of other tools from network theory to continuous-time\nnetworks with tie decay, including for applications to streaming data.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Attention dynamics on the Chinese social media Sina Weibo during the\n  COVID-19 pandemic: Understanding attention dynamics on social media during pandemics could help\ngovernments minimize the effects. We focus on how COVID-19 has influenced the\nattention dynamics on the biggest Chinese microblogging website Sina Weibo\nduring the first four months of the pandemic. We study the real-time Hot Search\nList (HSL), which provides the ranking of the most popular 50 hashtags based on\nthe amount of Sina Weibo searches. We show how the specific events, measures\nand developments during the epidemic affected the emergence of different kinds\nof hashtags and the ranking on the HSL. A significant increase of COVID-19\nrelated hashtags started to occur on HSL around January 20, 2020, when the\ntransmission of the disease between humans was announced. Then very rapidly a\nsituation was reached where COVID-related hashtags occupied 30-70% of the HSL,\nhowever, with changing content. We give an analysis of how the hashtag topics\nchanged during the investigated time span and conclude that there are three\nperiods separated by February 12 and March 12. In period 1, we see strong\ntopical correlations and clustering of hashtags; in period 2, the correlations\nare weakened, without clustering pattern; in period 3, we see a potential of\nclustering while not as strong as in period 1. We further explore the dynamics\nof HSL by measuring the ranking dynamics and the lifetimes of hashtags on the\nlist. This way we can obtain information about the decay of attention, which is\nimportant for decisions about the temporal placement of governmental measures\nto achieve permanent awareness. Furthermore, our observations indicate\nabnormally higher rank diversity in the top 15 ranks on HSL due to the COVID-19\nrelated hashtags, revealing the possibility of algorithmic intervention from\nthe platform provider.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Wealth distribution under the spread of infectious diseases: We develop a mathematical framework to study the economic impact of\ninfectious diseases by integrating epidemiological dynamics with a kinetic\nmodel of wealth exchange. The multi-agent description leads to study the\nevolution over time of a system of kinetic equations for the wealth densities\nof susceptible, infectious and recovered individuals, whose proportions are\ndriven by a classical compartmental model in epidemiology. Explicit\ncalculations show that the spread of the disease seriously affects the\ndistribution of wealth, which, unlike the situation in the absence of\nepidemics, can converge towards a stationary state with a bimodal form.\nFurthermore, simulations confirm the ability of the model to describe different\nphenomena characteristics of economic trends in situations compromised by the\nrapid spread of an epidemic, such as the unequal impact on the various wealth\nclasses and the risk of a shrinking middle class.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Assessing the Bias in Communication Networks Sampled from Twitter: We collect and analyse messages exchanged in Twitter using two of the\nplatform's publicly available APIs (the search and stream specifications). We\nassess the differences between the two samples, and compare the networks of\ncommunication reconstructed from them. The empirical context is given by\npolitical protests taking place in May 2012: we track online communication\naround these protests for the period of one month, and reconstruct the network\nof mentions and re-tweets according to the two samples. We find that the search\nAPI over-represents the more central users and does not offer an accurate\npicture of peripheral activity; we also find that the bias is greater for the\nnetwork of mentions. We discuss the implications of this bias for the study of\ndiffusion dynamics and collective action in the digital era, and advocate the\nneed for more uniform sampling procedures in the study of online communication.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Understanding the onset of hot streaks across artistic, cultural, and\n  scientific careers: Hot streaks dominate the main impact of creative careers. Despite their\nubiquitous nature across a wide range of creative domains, it remains unclear\nif there is any regularity underlying the beginning of hot streaks. Here, we\ndevelop computational methods using deep learning and network science and apply\nthem to novel, large-scale datasets tracing the career outputs of artists, film\ndirectors, and scientists, allowing us to build high-dimensional\nrepresentations of the artworks, films, and scientific publications they\nproduce. By examining individuals' career trajectories within the underlying\ncreative space, we find that across all three domains, individuals tend to\nexplore diverse styles or topics before their hot streak, but become notably\nmore focused in what they work on after the hot streak begins. Crucially, we\nfind that hot streaks are associated with neither exploration nor exploitation\nbehavior in isolation, but a particular sequence of exploration followed by\nexploitation, where the transition from exploration to exploitation closely\ntraces the onset of a hot streak. Overall, these results unveil among the first\nidentifiable regularity underlying the onset of hot streaks, which appears\nuniversal across diverse creative domains, suggesting that a sequential view of\ncreative strategies that balances experimentation and implementation may be\nparticularly powerful for producing long-lasting contributions, which may have\nbroad implications for identifying and nurturing creative talents.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Pareto index induced from the scale of companies: Employing profits data of Japanese companies in 2002 and 2003, we confirm\nthat Pareto's law and the Pareto index are derived from the law of detailed\nbalance and Gibrat's law. The last two laws are observed beyond the region\nwhere Pareto's law holds. By classifying companies into job categories, we find\nthat companies in a small scale job category have more possibilities of growing\nthan those in a large scale job category. This kinematically explains that the\nPareto index for the companies in the small scale job class is larger than that\nfor the companies in the large scale job class.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Early Prediction of Movie Box Office Success based on Wikipedia Activity\n  Big Data: Use of socially generated \"big data\" to access information about collective\nstates of the minds in human societies has become a new paradigm in the\nemerging field of computational social science. A natural application of this\nwould be the prediction of the society's reaction to a new product in the sense\nof popularity and adoption rate. However, bridging the gap between \"real time\nmonitoring\" and \"early predicting\" remains a big challenge. Here we report on\nan endeavor to build a minimalistic predictive model for the financial success\nof movies based on collective activity data of online users. We show that the\npopularity of a movie can be predicted much before its release by measuring and\nanalyzing the activity level of editors and viewers of the corresponding entry\nto the movie in Wikipedia, the well-known online encyclopedia.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A Heart for Interaction: Shared Physiological Dynamics and Behavioral\n  Coordination in a Collective, Creative Construction Task: Interpersonally shared physiological dynamics are increasingly argued to\nunderlie rapport, empathy and even team performance. Inspired by the model of\ninterpersonal synergy, we critically investigate the presence, temporal\ndevelopment, possible mechanisms and impact of shared interpersonal heart rate\ndynamics during individual and collective creative LEGO construction tasks. In\nStudy 1 we show how shared HR dynamics are driven by a plurality of sources\nincluding task constraints and behavioral coordination. Generally, shared HR\ndynamics are more prevalent in individual trials (involving participants doing\nthe same things) than in collective ones (involving participants taking turns\nand performing complementary actions). However, when contrasted against virtual\npairs, collective trials display more stable shared HR dynamics suggesting that\nonline social interaction plays an important role. Furthermore, in contrast to\nindividual trials, shared HR dynamics are found to increase across collective\ntrials. Study 2 investigates which aspects of social interaction might drive\nthese effects. We show that shared HR dynamics are statistically predicted by\ninterpersonal speech and building coordination. In Study 3, we explore the\nrelation between HR dynamics, behavioral coordination, and self-reported\nmeasures of rapport and group competence. While behavioral coordination\npredicts rapport and group competence, shared HR dynamics do not. Although\nshared physiological dynamics were reliably observed in our study, our results\nwarrant not to consider HR dynamics a general driving mechanism of social\ncoordination. Behavioral coordination - on the other hand - seems to be more\ninformative of both shared physiological dynamics and collective experience.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Uncovering collective listening habits and music genres in bipartite\n  networks: In this paper, we analyze web-downloaded data on people sharing their music\nlibrary, that we use as their individual musical signatures (IMS). The system\nis represented by a bipartite network, nodes being the music groups and the\nlisteners. Music groups audience size behaves like a power law, but the\nindividual music library size is an exponential with deviations at small\nvalues. In order to extract structures from the network, we focus on\ncorrelation matrices, that we filter by removing the least correlated links.\nThis percolation idea-based method reveals the emergence of social communities\nand music genres, that are visualised by a branching representation. Evidence\nof collective listening habits that do not fit the neat usual genres defined by\nthe music industry indicates an alternative way of classifying listeners/music\ngroups. The structure of the network is also studied by a more refined method,\nbased upon a random walk exploration of its properties. Finally, a personal\nidentification - community imitation model (PICI) for growing bipartite\nnetworks is outlined, following Potts ingredients. Simulation results do\nreproduce quite well the empirical data.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Analysis and Synthesis of the Distribution of Consonants over Languages:\n  A Complex Network Approach: Cross-linguistic similarities are reflected by the speech sound systems of\nlanguages all over the world. In this work we try to model such similarities\nobserved in the consonant inventories, through a complex bipartite network. We\npresent a systematic study of some of the appealing features of these\ninventories with the help of the bipartite network. An important observation is\nthat the occurrence of consonants follows a two regime power law distribution.\nWe find that the consonant inventory size distribution together with the\nprinciple of preferential attachment are the main reasons behind the emergence\nof such a two regime behavior. In order to further support our explanation we\npresent a synthesis model for this network based on the general theory of\npreferential attachment.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Particle modeling of the spreading of Coronavirus Disease (COVID-19): By the end of July 2020, the COVID-19 pandemic had infected more than\nseventeen million people and had spread to almost all countries worldwide. In\nresponse, many countries all over the world have used different methods to\nreduce the infection rate, such as including case isolation, the closure of\nschools and universities, banning public events, and mostly forcing social\ndistancing, including local and national lockdowns. We use a Monte-Carlo (MC)\nbased algorithm to predict the virus infection rate for different population\ndensities using the most recent epidemic data in our work. We test the spread\nof the Coronavirus using three different lockdown models, and eight various\ncombinations of constraints, which allow us to examine the efficiency of each\nmodel and constraint. In this paper, we have tested three different time-cyclic\npatterns of no-restrictions/lockdown patterns. This model's main prediction is\nthat a cyclic schedule of no-restrictions/lockdown that contains at least ten\ndays of lockdown for each time cycle can help control the virus infection. In\nparticular, this model reduces the infection rate when accompanied by social\ndistancing and complete isolation of symptomatic patients.",
        "category": "physics_soc-ph"
    },
    {
        "text": "How networks shape diversity for better or worse: Socio-diversity, the variety of human opinions, ideas, behaviors and styles,\nhas profound implications for social systems. While it fuels innovation,\nproductivity, and collective intelligence, it can also complicate communication\nand erode trust. So what mechanisms can influence it? This paper studies how\nfundamental characteristics of social networks can support or hinder\nsocio-diversity. It employs models of cultural evolution, mathematical\nanalysis, and numerical simulations. We find that pronounced inequalities in\nthe distribution of connections obstruct socio-diversity. In contrast, the\nprevalence of close-knit communities, a scarcity of long-range connections, and\na significant tie density tend to promote it. These results open new\nperspectives for understanding how to change social networks to sustain more\nsocio-diversity and, thereby, societal innovation, collective intelligence, and\nproductivity.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Dynamics of Social Systems: Cooperation and Free-Riding: We study the mean field dynamics of a model introduced by Phan et al [Wehia,\n2005] of a polymorphic social community. The individuals may choose between\nthree strategies: either not to join the community or, in the case of joining\nit, to cooperate or to behave as a free-rider. Individuals' preferences have an\nidiosyncratic component and a social component. Cooperators bear a fixed cost\nwhereas free-riders support a cost proportional to the number of cooperators.\nWe study the dynamics of this model analytically in the mean field\napproximation for both parallel and sequential updating. As we vary one of the\nparameters while keeping the other parameters fixed, the phase diagram\nexperiences a rich class of bifurcations. Noticeably, a limit cycle is shown to\nexist in both parallel and sequential updating, under certain parameter\nsettings. A comparison of the analytical predictions with computer simulation\nis also included.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Outbreak diversity in epidemic waves propagating through distinct\n  geographical scales: A central feature of an emerging infectious disease in a pandemic scenario is\nthe spread through geographical scales and the impacts on different locations\naccording to the adopted mitigation protocols. We investigated a stochastic\nepidemic model with the metapopulation approach in which patches represent\nmunicipalities. Contagion follows a stochastic compartmental model for\nmunicipalities; the latter, in turn, interact with each other through recurrent\nmobility. As a case of study, we consider the epidemic of COVID-19 in Brazil\nperforming data-driven simulations. Properties of the simulated epidemic curves\nhave very broad distributions across different geographical locations and\nscales, from states, passing through intermediate and immediate regions down to\nmunicipality levels. Correlations between delay of the epidemic outbreak and\ndistance from the respective capital cities were predicted to be strong in\nseveral states and weak in others, signaling influences of multiple epidemic\nfoci propagating towards the inland cities. Responses of different regions to a\nsame mitigation protocol can vary enormously implying that the policies of\ncombating the epidemics must be engineered according to the region' specificity\nbut integrated with the overall situation. Real series of reported cases\nconfirm the qualitative scenarios predicted in simulations. Even though we\nrestricted our study to Brazil, the prospects and model can be extended to\nother geographical organizations with heterogeneous demographic distributions.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Opinion disparity in hypergraphs with community structure: The division of a social group into subgroups with opposing opinions, which\nwe refer to as opinion disparity, is a prevalent phenomenon in society. This\nphenomenon has been modeled by including mechanisms such as opinion homophily,\nbounded confidence interactions, and social reinforcement mechanisms. In this\npaper we study a complementary mechanism for the formation of opinion disparity\nbased on higher-order interactions, i.e., simultaneous interactions between\nmultiple agents. We present an extension of the planted partition model for\nuniform hypergraphs as a simple model of community structure and consider the\nhypergraph SIS model on a hypergraph with two communities where the binary\nideology can spread via links (pairwise interactions) and triangles (three-way\ninteractions). We approximate this contagion process with a mean-field model\nand find that for strong enough community structure, the two communities can\nhold very different average opinions. We determine the regimes of structural\nand infectious parameters for which this opinion disparity can exist and find\nthat the existence of these disparities is much more sensitive to the triangle\ncommunity structure than to the link community structure. We show that the\nexistence and type of opinion disparities are extremely sensitive to\ndifferences in the sizes of the two communities.",
        "category": "physics_soc-ph"
    },
    {
        "text": "In-depth analysis of the Naming Game dynamics: the homogeneous mixing\n  case: Language emergence and evolution has recently gained growing attention\nthrough multi-agent models and mathematical frameworks to study their behavior.\nHere we investigate further the Naming Game, a model able to account for the\nemergence of a shared vocabulary of form-meaning associations through\nsocial/cultural learning. Due to the simplicity of both the structure of the\nagents and their interaction rules, the dynamics of this model can be analyzed\nin great detail using numerical simulations and analytical arguments. This\npaper first reviews some existing results and then presents a new overall\nunderstanding.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Network Horizon Dynamics I: Qualitative Aspects: Mostly acyclic directed networks, treated mathematically as directed graphs,\narise in machine learning, biology, social science, physics, and other\napplications. Newman [1] has noted the mathematical challenges of such\nnetworks. In this series of papers, we study their connectivity properties,\nfocusing on three types of phase transitions that affect horizon sizes for\ntypical nodes. The first two types involve the familiar emergence of giant\ncomponents as average local connectivity increases, while the third type\ninvolves small-world horizon growth at variable distance from a typical node.\nIn this first paper, we focus on qualitative behavior, simulations, and\napplications, leaving formal considerations for subsequent papers. We explain\nhow such phase transitions distinguish deep neural networks from shallow\nmachine learning architectures, and propose hybrid local/random network designs\nwith surprising connectivity advantages. We also propose a small-world approach\nto the horizon problem in the cosmology of the early universe as a novel\nalternative to the inflationary hypothesis of Guth and Linde.",
        "category": "physics_soc-ph"
    },
    {
        "text": "On learning agent-based models from data: Agent-Based Models (ABMs) are used in several fields to study the evolution\nof complex systems from micro-level assumptions. However, ABMs typically can\nnot estimate agent-specific (or \"micro\") variables: this is a major limitation\nwhich prevents ABMs from harnessing micro-level data availability and which\ngreatly limits their predictive power. In this paper, we propose a protocol to\nlearn the latent micro-variables of an ABM from data. The first step of our\nprotocol is to reduce an ABM to a probabilistic model, characterized by a\ncomputationally tractable likelihood. This reduction follows two general design\nprinciples: balance of stochasticity and data availability, and replacement of\nunobservable discrete choices with differentiable approximations. Then, our\nprotocol proceeds by maximizing the likelihood of the latent variables via a\ngradient-based expectation maximization algorithm. We demonstrate our protocol\nby applying it to an ABM of the housing market, in which agents with different\nincomes bid higher prices to live in high-income neighborhoods. We demonstrate\nthat the obtained model allows accurate estimates of the latent variables,\nwhile preserving the general behavior of the ABM. We also show that our\nestimates can be used for out-of-sample forecasting. Our protocol can be seen\nas an alternative to black-box data assimilation methods, that forces the\nmodeler to lay bare the assumptions of the model, to think about the\ninferential process, and to spot potential identification problems.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A sewage management proposal for Luruaco lake, Colombia: This study presents numerical simulations of faecal coliforms dynamics in\nLuruaco lake, located in Atl\\'antico Department, Colombia. The velocity field\nis obtained through a two-dimensional horizontal (2DH) model of Navier-Stokes\nequations system. The transport equation of faecal coliforms concentration is\nprovided from a convective-diffusive-reactive equation. The lake's geometry is\nbuilt through cubic spline and multi-block methods. The discretization method\nby Finite Differences and the First Order Upwind (FOU) are applied to the 2DH\nmodel. The Mark and Cell (MAC) method is used to determine numerically the\nvelocity field of water flow. Numerical simulations are carried out for a\n72-hour period in order to understand the influence of faecal coliforms\ninjections from each tributary. From the qualitative and quantitative analysis\nof the factors that influence faecal coliforms dynamics, proposals are\npresented, which aim to reduce contamination in some regions of Lake Luruaco.\nThe numerical simulations show that the best option to improve water quality in\nthe lake is the implementation of two actions, the diversion of the Lim\\'on\nstream to the Negro stream and the installation of a sewage treatment plant at\nthe mouth of the Negro stream. Other less expensive proposals are also\npresented.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Modeling real spatial networks: We have studied transportation network, namely a road network of the Moscow\nregion and airline network of the Russian Federation. We have constructed\ncorresponding networks and studied degree distribution and length distribution\nfor these networks, as well as the dependences on the average clustering\ncoefficients and the nearest neighbours average degree as a function of the\nvertex degree. In conclusion we discuss degree and length distributions in the\nframework of the nonextensive statistics, using the maximum entropy approach\nand the model with additive and multiplicative noise. We present a procedure of\nfitting the results of the data processing to the q-type distribution which\nallows the fractal dimension definition of the networks under study.",
        "category": "physics_soc-ph"
    },
    {
        "text": "About the impossibility of quantifying the knowledge and of establishing\n  consequently its correlation with money: This article shows how the Universities and higher education and research\ninstitutions obey more and more to criteria of profitability and effectiveness,\nfollowing the example of the industry. The Universities and the academic\ncommunities are the main producers of knowledge, which is not quantifiable and,\ntherefore, cannot find an equivalent in money, which is quantifiable. Many\nscientists intend to establish a connection between knowledge and money, using\nfor that a set of criteria of evaluation. Being useless these criteria, the\nscientists resort to a system of honour, but the science is essentially normal\nknowledge.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Variation of Gini and Kolkata Indices with Saving Propensity in the\n  Kinetic Exchange Model of Wealth Distribution: An Analytical Study: We study analytically the change in the wealth ($x$) distribution $P(x)$\nagainst saving propensity $\\lambda$ in a closed economy, using the Kinetic\ntheory. We estimate the Gini ($g$) and Kolkata ($k)$ indices by deriving (using\n$P(x)$) the Lorenz function $L(f)$, giving the cumulative fraction $L$ of\nwealth possessed by fraction $f$ of the people ordered in ascending order of\nwealth. First, using the exact result for $P(x)$ when $\\lambda = 0$ we derive\n$L(f)$, and from there the index values $g$ and $k$. We then proceed with an\napproximate gamma distribution form of $P(x)$ for non-zero values of $\\lambda$.\nThen we derive the results for $g$ and $k$ at $\\lambda = 0.25$ and as $\\lambda\n\\rightarrow 1$. We note that for $\\lambda \\rightarrow 1$ the wealth\ndistribution $P(x)$ becomes a Dirac $\\delta$-function. Using this and assuming\nthat form for larger values of $\\lambda$ we proceed for an approximate estimate\nfor $P(x)$ centered around the most probable wealth (a function of $\\lambda$).\nWe utilize this approximate form to evaluate $L(f)$, and using this along with\nthe known analytical expression for $g$, we derive an analytical expression for\n$k(\\lambda)$. These analytical results for $g$ and $k$ at different $\\lambda$\nare compared with numerical (Monte Carlo) results from the study of the\nChakraborti-Chakrabarti model. Next we derive analytically a relation between\n$g$ and $k$. From the analytical expressions of $g$ and $k$, we proceed for a\nthermodynamic mapping to show that the former corresponds to entropy and the\nlatter corresponds to the inverse temperature.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Diffusive Epidemic Process on Barabasi-Albert Networks: We present a modified diffusive epidemic process that has a finite threshold\non scale-free graphs. The diffusive epidemic process describes the epidemic\nspreading in a non-sedentary population, and it is a reaction-diffusion\nprocess. In the diffusion stage, the individuals can jump between connected\nnodes, according to different diffusive rates for the infected and susceptible\nindividuals. In the reaction stage, the contagion can happen if there is an\ninfected individual sharing the same node, and infected individuals can\nspontaneously recover. Our main modification is to turn the number of\nindividuals' interactions independent on the population size by using Gillespie\nalgorithm with a reaction time $t_\\mathrm{max}$, exponentially distributed with\nmean inversely proportional to the node concentration. Our simulation results\nof the modified model on Barabasi-Albert networks are compatible with a\ncontinuous phase transition with a finite threshold from an absorbing phase to\nan active phase when increasing the concentration. The transition obeys the\nmean-field critical exponents of the order parameter, its fluctuations and the\nspatial correlation length, whose values are $\\beta=1$, $\\gamma'=0$ and\n$\\nu_\\perp=1/2$, respectively. In addition, the system presents logarithmic\ncorrections with pseudo-exponents $\\widehat{\\beta}=\\widehat{\\gamma}'=-3/2$ on\nthe order parameter and its fluctuations, respectively. The most evident\nimplication of our simulation results is if the individuals avoid social\ninteractions in order to not spread a disease, this leads the system to have a\nfinite threshold in scale-free graphs, allowing for epidemic control.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Functional structure in production networks: Production networks are integral to economic dynamics, yet dis-aggregated\nnetwork data on inter-firm trade is rarely collected and often proprietary.\nHere we situate company-level production networks among networks from other\ndomains according to their local connectivity structure. Through this lens, we\nstudy a regional and a national network of inferred trade relationships\nreconstructed from Dutch national economic statistics and re-interpret prior\nempirical findings. We find that company-level production networks have\nso-called functional structure, as previously identified in protein-protein\ninteraction (PPI) networks. Functional networks are distinctive in their\nover-representation of closed squares, which we quantify using an existing\nmeasure called spectral bipartivity. Shared local connectivity structure lets\nus ferry insights between domains. PPI networks are shaped by complementarity,\nrather than homophily, and we use multi-layer directed configuration models to\nshow that this principle explains the emergence of functional structure in\nproduction networks. Companies are especially similar to their close\ncompetitors, not to their trading partners. Our findings have practical\nimplications for the analysis of production networks and a thorough\nunderstanding of their local connectivity structure will help us better reason\nabout the micro-economic mechanisms behind their routine function, failure, and\ngrowth.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Zipf's law, Hierarchical Structure, and Shuffling-Cards Model for Urban\n  Development: A new angle of view is proposed to find the simple rules dominating complex\nsystems and regular patterns behind random phenomena such as cities. Hierarchy\nof cities reflects the ubiquitous structure frequently observed in the natural\nworld and social institutions. Where there is a hierarchy with cascade\nstructure, there is a rank-size distribution following Zipf's law, and vice\nversa. The hierarchical structure can be described with a set of exponential\nfunctions that are identical in form to Horton-Strahler's laws on rivers and\nGutenberg-Richter's laws on earthquake energy. From the exponential models, we\ncan derive four power laws such as Zipf's law indicative of fractals and\nscaling symmetry. Research on the hierarchy is revealing for us to understand\nhow complex systems are self-organized. A card-shuffling model is built to\ninterpret the relation between Zipf's law and hierarchy of cities. This model\ncan be expanded to explain the general empirical power-law distributions across\nthe individual physical and social sciences, which are hard to be comprehended\nwithin the specific scientific domains.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Influence maximization in multilayer networks based on adaptive coupling\n  degree: Influence Maximization(IM) aims to identify highly influential nodes to\nmaximize influence spread in a network. Previous research on the IM problem has\nmainly concentrated on single-layer networks, disregarding the comprehension of\nthe coupling structure that is inherent in multilayer networks. To solve the IM\nproblem in multilayer networks, we first propose an independent cascade model\n(MIC) in a multilayer network where propagation occurs simultaneously across\ndifferent layers. Consequently, a heuristic algorithm, i.e., Adaptive Coupling\nDegree (ACD), which selects seed nodes with high spread influence and a low\ndegree of overlap of influence, is proposed to identify seed nodes for IM in a\nmultilayer network. By conducting experiments based on MIC, we have\ndemonstrated that our proposed method is superior to the baselines in terms of\ninfluence spread and time cost in 6 synthetic and 4 real-world multilayer\nnetworks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Analysis of the Dynamics and Topology Dependencies of Small\n  Perturbations in Electric Transmission Grids: Through an eigenanalysis of small perturbations, as typically done in\nsmall-signal stability studies, we intend to discover the underlying reasons\nthat make those perturbations propagate in some way or another in the grid. To\nthis end, we establish connections between the perturbations time-scale and\ntopological metrics. Namely, the algebraic connectivity and the Fiedler vector\nof a generalized/weighted Laplacian matrix that depends on the stationary phase\nsolutions of the system and is thereby inherently conditioned by the topology\nand the power distribution. Then, we aim to find out the isolated influence of\ntopology on the perturbations when the network interacting agents have, in\nprinciple, opposite behaviors (i.e. producers and consumers). To do so, we\nstudy three networks: Small-world, Random, German grid. Furthermore, we tackle\nthe effect of machine clustering on small perturbations and the influence of\nthe network's average clustering coefficient on the intensity localization of\nthe generalized Fiedler vector. Finally, we propose ways in which future\n(dynamic topology control) and existing (power system stabilizer) grid control\nstrategies can adapt their response to comprehensively consider the topology\nand remote signals in the system.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Scaling property and opinion model for interevent time of terrorism\n  attack: The interevent time of terrorism attack events is investigated by empirical\ndata and model analysis. Empirical evidence shows it follows a scale-free\nproperty. In order to understand the dynamic mechanism of such statistic\nfeature, an opinion dynamic model with memory effect is proposed on a\ntwo-dimension lattice network. The model mainly highlights the role of\nindividual social conformity and self-affirmation psychology. An attack event\noccurs when the order parameter of the system reaches a critical value.\nUltimately, the model reproduces the same statistical property as the empirical\ndata and gives a good understanding of terrorism attack.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The hidden traits of endemic illiteracy in cities: In spite of the considerable progress towards reducing illiteracy rates, many\ncountries, including developed ones, have encountered difficulty achieving\nfurther reduction in these rates. This is worrying because illiteracy has been\nrelated to numerous health, social, and economic problems. Here, we show that\nthe spatial patterns of illiteracy in urban systems have several features\nanalogous to the spread of diseases such as dengue and obesity. Our results\nreveal that illiteracy rates are spatially long-range correlated, displaying\nnon-trivial clustering structures characterized by percolation-like transitions\nand fractality. These patterns can be described in the context of percolation\ntheory of long-range correlated systems at criticality. Together, these results\nprovide evidence that the illiteracy incidence can be related to a\ntransmissible process, in which the lack of access to minimal education\npropagates in a population in a similar fashion to endemic diseases.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A Collapse of the Eastern Mediterranean? New results and theories on the\n  interplay between climate and societies in Byzantium and the Near East, ca.\n  1000-1200 AD: This paper discusses a recently proposed scenario of a climate-induced\nCollapse of the Eastern Mediterranean in the 11th century AD. It demonstrates\nthat such a scenario cannot be maintained when confronted with proxy data from\nvarious regions. On the other hand, data on the interplay between environment\nand economy in the Komnenian period (1081-1185) and evidence for a change of\nclimatic conditions in the period of the Angeloi (1185-1204) is presented,\narguing that climatic parameters should be taken into consideration when\ncomparing socio-economic dynamics in the Eastern Mediterranean with those in\nWestern Europe. The necessity of further research on the regional as well as\nover-regional level for many aspects of the interaction between human society\nand environment in the medieval Eastern Mediterranean is highlighted.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Analytical solution of stochastic model of risk-spreading with global\n  coupling: We study a stochastic matrix model to understand the mechanics of\nrisk-spreading (or bet-hedging) by dispersion. Such model has been mostly dealt\nnumerically except for well-mixed case, so far. Here, we present an analytical\nresult, which shows that optimal dispersion leads to Zipf's law. Moreover, we\nfound that the arithmetic ensemble average of the total growth rate converges\nto the geometric one, because the sample size is finite.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Unbeatable Tit-for-Tat as a Zero-Determinant Strategy: Tit-for-Tat strategy is a strategy in repeated two-player symmetric games\nwhich imitates the previous action of the opponent. We show that the\nTit-for-Tat strategy is a zero-determinant strategy, which unilaterally\nequalizes the expected payoffs of two players, if and only if the stage game is\na potential game. Because it has been known that this condition is equivalent\nto the condition that the Tit-for-Tat strategy is unbeatable, our results\nsuggest some relation between unbeatable property and the concept of\nzero-determinant strategy.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Homophily and the Speed of Social Mobilization: The Effect of Acquired\n  and Ascribed Traits: Large-scale mobilization of individuals across social networks is becoming\nincreasingly prevalent in society. However, little is known about what affects\nthe speed of social mobilization. Here we use a framed field experiment to\nidentify and measure properties of individuals and their relationships that\npredict mobilization speed. We ran a global social mobilization contest and\nrecorded personal traits of the participants and those they recruited. We\nstudied the effects of ascribed traits (gender, age) and acquired traits\n(geography, and information source) on the speed of mobilization. We found that\nhomophily, a preference for interacting with other individuals with similar\ntraits, had a mixed role in social mobilization. Homophily was present for\nacquired traits, in which mobilization speed was faster when the recuiter and\nrecruit had the same trait compared to different traits. In contrast, we did\nnot find support for homophily for the ascribed traits. Instead, those traits\nhad other, non-homophily effects: Females mobilized other females faster than\nmales mobilized other males. Younger recruiters mobilized others faster, and\nolder recruits mobilized slower. Recruits also mobilized faster when they first\nheard about the contest directly from the contest organization, and decreased\nin speed when hearing from less personal source types (e.g. family vs. media).\nThese findings show that social mobilization includes dynamics that are unlike\nother, more passive forms of social activity propagation. These findings\nsuggest relevant factors for engineering social mobilization tasks for\nincreased speed.",
        "category": "physics_soc-ph"
    },
    {
        "text": "An evolutionary game model for behavioral gambit of loyalists: Global\n  awareness and risk-aversion: We study the phase diagram of a minority game where three classes of agents\nare present. Two types of agents play a risk-loving game that we model by the\nstandard Snowdrift Game. The behaviour of the third type of agents is coded by\n{\\em indifference} w.r.t. the game at all: their dynamics is designed to\naccount for risk-aversion as an innovative behavioral gambit. From this point\nof view, the choice of this solitary strategy is enhanced when innovation\nstarts, while is depressed when it becomes the majority option. This implies\nthat the payoff matrix of the game becomes dependent on the global awareness of\nthe agents measured by the relevance of the population of the indifferent\nplayers. The resulting dynamics is non-trivial with different kinds of phase\ntransition depending on a few model parameters. The phase diagram is studied on\nregular as well as complex networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Analysis of nanosciences and nanotechnology and their applications: The small world of matter is getting smaller and smaller. Nano sciences in\nrecent years had huge developments allowing nanotechnologies to take enormous\nsteps in the development of materials and processes. Numerous applications in a\nwide scope of fields are very beneficial for humans, and many researches in\ndevelopment are very promising. Applications in medicine, industry,\nelectronics, energy, or aeronautics are only some examples of areas where\nenormous benefits exist, and potentialities are clear. Some nanotechnologies\nare already applied and others are in development or testing phases.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A Heterogeneous Out-of-Equilibrium Nonlinear $q$-Voter Model with\n  Zealotry: We study the dynamics of the out-of-equilibrium nonlinear q-voter model with\ntwo types of susceptible voters and zealots, introduced in [EPL 113, 48001\n(2016)]. In this model, each individual supports one of two parties and is\neither a susceptible voter of type $q_1$ or $q_2$, or is an inflexible zealot.\nAt each time step, a $q_i$-susceptible voter ($i = 1,2$) consults a group of\n$q_i$ neighbors and adopts their opinion if all group members agree, while\nzealots are inflexible and never change their opinion. This model violates\ndetailed balance whenever $q_1 \\neq q_2$ and is characterized by two distinct\nregimes of low and high density of zealotry. Here, by combining analytical and\nnumerical methods, we investigate the non-equilibrium stationary state of the\nsystem in terms of its probability distribution, non-vanishing currents and\nunequal-time two-point correlation functions. We also study the switching times\nproperties of the model by exploiting an approximate mapping onto the model of\n[Phys. Rev. E 92, 012803 (2015)] that satisfies the detailed balance, and also\noutline some properties of the model near criticality.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Symmetry Induced Group Consensus: There has been substantial work studying consensus problems for which there\nis a single common final state, although there are many real-world complex\nnetworks for which the complete consensus may be undesirable. More recently,\nthe concept of group consensus whereby subsets of nodes are chosen to reach a\ncommon final state distinct from others has been developed, but the methods\ntend to be independent of the underlying network topology. Here, an alternative\ntype of group consensus is achieved for which nodes that are symmetric achieve\na common final state. The dynamic behavior may be distinct between nodes that\nare not symmetric. We show how group consensus for heterogeneous linear agents\ncan be achieved via a simple coupling protocol that exploits the topology of\nthe network. We see that group consensus is possible on both stable and\nunstable trajectories. We observe and characterize the phenomenon of isolated\ngroup consensus, where one or more clusters may achieve group consensus while\nthe other clusters do not.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Geography of Happiness: Connecting Twitter sentiment and expression,\n  demographics, and objective characteristics of place: We conduct a detailed investigation of correlations between real-time\nexpressions of individuals made across the United States and a wide range of\nemotional, geographic, demographic, and health characteristics. We do so by\ncombining (1) a massive, geo-tagged data set comprising over 80 million words\ngenerated over the course of several recent years on the social network service\nTwitter and (2) annually-surveyed characteristics of all 50 states and close to\n400 urban populations. Among many results, we generate taxonomies of states and\ncities based on their similarities in word use; estimate the happiness levels\nof states and cities; correlate highly-resolved demographic characteristics\nwith happiness levels; and connect word choice and message length with urban\ncharacteristics such as education levels and obesity rates. Our results show\nhow social media may potentially be used to estimate real-time levels and\nchanges in population-level measures such as obesity rates.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Number fluctuations induce persistent congestion: The capacity of a street segment quantifies the maximal density of vehicles\nbefore congestion arises. Here we show in a simple mathematical model that\nfluctuations in the instantaneous number of vehicles entering a street segment\nare sufficient to induce persistent congestion. Congestion emerges even if the\naverage flow is below the segment's capacity where congestion is absent without\nfluctuations. We explain how this fluctuation-induced congestion emerges due to\na self-amplifying reduction of the average vehicle velocities.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Contagion in simplicial complexes: The propagation of information in social, biological and technological\nsystems represents a crucial component in their dynamic behavior. When limited\nto pairwise interactions, a rather firm grip is available on the relevant\nparameters and critical transitions of these spreading processes, most notably\nthe pandemic transition, which indicates the conditions for the spread to cover\na large fraction of the network. The challenge is that, in many relevant\napplications, the spread is driven by higher order relationships, in which\nseveral components undergo a group interaction. To address this, we analyze the\nspreading dynamics in a simplicial complex environment, designed to capture the\ncoexistence of interactions of different orders. We find that, while pairwise\ninteractions play a key role in the initial stages of the spread, once it gains\ncoverage, higher order simplices take over and drive the contagion dynamics.\nThe result is a distinctive spreading phase diagram, exhibiting a discontinuous\npandemic transition, and hence offering a qualitative departure from the\ntraditional network spreading dynamics.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Individual heterogeneity generates explosive system network dynamics: Individual heterogeneity is a key characteristic of many real-world systems,\nfrom organisms to humans. However its role in determining the system's\ncollective dynamics is typically not well understood. Here we study how\nindividual heterogeneity impacts the system network dynamics by comparing\nlinking mechanisms that favor similar or dissimilar individuals. We find that\nthis heterogeneity-based evolution can drive explosive network behavior and\ndictates how a polarized population moves toward consensus. Our model shows\ngood agreement with data from both biological and social science domains. We\nconclude that individual heterogeneity likely plays a key role in the\ncollective development of real-world networks and communities, and cannot be\nignored.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Impacts of Game-Theoretic Activation on Epidemic Spread over Dynamical\n  Networks: We investigate the evolution of epidemics over dynamical networks when nodes\nchoose to interact with others in a selfish and decentralized manner.\nSpecifically, we analyze the susceptible-asymptomatic-infected-recovered (SAIR)\nepidemic in the framework of activity-driven networks with heterogeneous node\ndegrees and time-varying activation rates, and derive both individual and\ndegree-based mean-field approximations of the exact state evolution. We then\npresent a game-theoretic model where nodes choose their activation\nprobabilities in a strategic manner using current state information as\nfeedback, and characterize the quantal response equilibrium (QRE) of the\nproposed setting. We then consider the activity-driven\nsusceptible-infected-susceptible (SIS) epidemic model, characterize equilibrium\nactivation probabilities and analyze epidemic evolution in closed-loop. Our\nnumerical results provide compelling insights into epidemic evolution under\ngame-theoretic activation. Specifically, for the SAIR epidemic, we show that\nunder suitable conditions, the epidemic can persist, as any decrease in\ninfected proportion is counteracted by an increase in activity rates by the\nnodes. For the SIS epidemic, we show that in regimes where there is an endemic\nstate, the infected proportion could be significantly smaller under\ngame-theoretic activation if the loss upon infection is sufficiently high.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Intra-City Urban Network and Traffic Flow Analysis from GPS Mobility\n  Trace: We analyse two large-scale intra-city urban networks and traffic flows\ntherein measured by GPS traces of taxis in San Francisco and Shanghai. Our\nresults coincide with previous findings that, based purely on topological\nmeans, it is often insufficient to characterise traffic flow. Traditional\nshortest-path betweenness analysis, where shortest paths are calculated from\neach pairs of nodes, carries an unrealistic implicit assumption that each node\nor junction in the urban network generates and attracts an equal amount of\ntraffic. We also argue that weighting edges based only on euclidean distance is\ninadequate, as primary roads are commonly favoured over secondary roads due to\nthe perceived and actual travel time required. We show that betweenness traffic\nanalysis can be improved by a simple extended framework which incorporates both\nthe notions of node weights and fastest-path betweenness. We demonstrate that\nthe framework is superior to traditional methods based solely on simple\ntopological perspectives.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Resource heterogeneity leads to unjust effort distribution in climate\n  change mitigation: Fighting against climate change is a global challenge shared by nations with\nheterogeneous economical resources and individuals with diverse propensity for\ncooperation. However, we lack a clear understanding of the role of key factors\nsuch as inequality of means when diverse agents interact together towards a\ncommon goal. Here, we report the results of a collective-risk dilemma\nexperiment in which groups of subjects were initially given either equal or\nunequal endowments. We found that although the collective goal was always\nachieved regardless of the initial capital distribution, the effort\ndistribution was highly inequitable. Specifically, participants with fewer\nresources contributed significantly more to the public goods than the richer\n-sometimes twice as much. An unsupervised learning algorithm clustered the\nsubjects according to their individual behavior. We found that the poorest\nparticipants congregated within the two \"generous clusters\" whereas the richest\nwere mostly classified into a \"greedy cluster\". Our findings suggest that\nfuture policies would benefit both from reinforcing climate justice actions\naddressed to most vulnerable people and educating fairness instead of focusing\non understanding of generic or global climate consequences, as the latter has\nnot proven to drive equitable contributions.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The quoter model: a paradigmatic model of the social flow of written\n  information: We propose a model for the social flow of information in the form of text\ndata, which simulates the posting and sharing of short social media posts.\nNodes in a graph representing a social network take turns generating words,\nleading to a symbolic time series associated with each node. Information\npropagates over the graph via a quoting mechanism, where nodes randomly copy\nshort segments of text from each other. We characterize information flows from\nthese text via information-theoretic estimators, and we derive analytic\nrelationships between model parameters and the values of these estimators. We\nexplore and validate the model with simulations on small network motifs and\nlarger random graphs. Tractable models such as ours that generate symbolic data\nwhile controlling the information flow allow us to test and compare measures of\ninformation flow applicable to real social media data. In particular, by\nchoosing different network structures, we can develop test scenarios to\ndetermine whether or not measures of information flow can distinguish between\ntrue and spurious interactions, and how topological network properties relate\nto information flow.",
        "category": "physics_soc-ph"
    },
    {
        "text": "An Ising Model of Bursts and Fizzles in Intergroup Violence: During intergroup confrontations, agitating stimuli such as opponents'\nthreats and provocations can trigger collective violence. We examine video\nrecordings of street fights between groups of young men. Collective violence\nsometimes breaks out in a burst and at other times in a fizzle wherein only few\ngroup members participate. These two patterns are accurately predicted by an\nIsing model with asymmetric spins.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Ultra accurate personalized recommendation via eliminating redundant\n  correlations: In this paper, based on a weighted projection of bipartite user-object\nnetwork, we introduce a personalized recommendation algorithm, called the\n\\emph{network-based inference} (NBI), which has higher accuracy than the\nclassical algorithm, namely \\emph{collaborative filtering}. In the NBI, the\ncorrelation resulting from a specific attribute may be repeatedly counted in\nthe cumulative recommendations from different objects. By considering the\nhigher order correlations, we design an improved algorithm that can, to some\nextent, eliminate the redundant correlations. We test our algorithm on two\nbenchmark data sets, \\emph{MovieLens} and \\emph{Netflix}. Compared with the\nNBI, the algorithmic accuracy, measured by the ranking score, can be further\nimproved by 23% for \\emph{MovieLens} and 22% for \\emph{Netflix}, respectively.\nThe present algorithm can even outperform the \\emph{Latent Dirichlet\nAllocation} algorithm, which requires much longer computational time.\nFurthermore, most of the previous studies considered the algorithmic accuracy\nonly, in this paper, we argue that the diversity and popularity, as two\nsignificant criteria of algorithmic performance, should also be taken into\naccount. With more or less the same accuracy, an algorithm giving higher\ndiversity and lower popularity is more favorable. Numerical results show that\nthe present algorithm can outperform the standard one simultaneously in all\nfive adopted metrics: lower ranking score and higher precision for accuracy,\nlarger Hamming distance and lower intra-similarity for diversity, as well as\nsmaller average degree for popularity.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Exploring Temporal Networks with Greedy Walks: Temporal networks come with a wide variety of heterogeneities, from\nburstiness of event sequences to correlations between timings of node and link\nactivations. In this paper, we set to explore the latter by using greedy walks\nas probes of temporal network structure. Given a temporal network (a sequence\nof contacts), greedy walks proceed from node to node by always following the\nfirst available contact. Because of this, their structure is particularly\nsensitive to temporal-topological patterns involving repeated contacts between\nsets of nodes. This becomes evident in their small coverage per step as\ncompared to a temporal reference model -- in empirical temporal networks,\ngreedy walks often get stuck within small sets of nodes because of correlated\ncontact patterns. While this may also happen in static networks that have\npronounced community structure, the use of the temporal reference model takes\nthe underlying static network structure out of the equation and indicates that\nthere is a purely temporal reason for the observations. Further analysis of the\nstructure of greedy walks indicates that burst trains, sequences of repeated\ncontacts between node pairs, are the dominant factor. However, there are larger\npatterns too, as shown with non-backtracking greedy walks. We proceed further\nto study the entropy rates of greedy walks, and show that the sequences of\nvisited nodes are more structured and predictable in original data as compared\nto temporally uncorrelated references. Taken together, these results indicate a\nrichness of correlated temporal-topological patterns in temporal networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Time and space generalized diffusion equation on graphs/networks: Normal and anomalous diffusion are ubiquitous in many complex systems [1] .\nHere, we define a time and space generalized diffusion equation (GDE), which\nuses fractional-time derivatives and transformed d-path Laplacian operators on\ngraphs/networks. We find analytically the solution of this equation and prove\nthat it covers the regimes of normal, sub- and superdiffusion as a function of\nthe two parameters of the model. We extend the GDE to consider a system with\ntemporal alternancy of normal and anomalous diffusion which can be observed for\ninstance in the diffusion of proteins along a DNA chain. We perform\ncomputational experiments on a one-dimensional system emulating a linear DNA\nchain. It is shown that a subdiffusive-superdiffusive alternant regime allows\nthe diffusive particle to explore more slowly small regions of the chain with a\nfaster global exploration, than a subdiffusive-subdiffusive regime. Therefore,\nan alternancy of sliding (subdiffusive) with hopping and intersegmental\ntransfer (superdiffusive) mechanisms show important advances for protein-DNA\ninteractions.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Mutual selection in network evolution: the role of the intrinsic fitness: We propose a new mechanism leading to scale-free networks which is based on\nthe presence of an intrinsic character of a vertex called fitness. In our\nmodel, a vertex $i$ is assigned a fitness $x_i$, drawn from a given probability\ndistribution function $f(x)$. During network evolution, with rate $p$ we add a\nvertex $j$ of fitness $x_j$ and connect to an existing vertex $i$ of fitness\n$x_i$ selected preferentially to a linking probability function $g(x_i,x_j)$\nwhich depends on the fitnesses of the two vertices involved and, with rate\n$1-p$ we create an edge between two already existed vertices with fitnesses\n$x_i$ and $x_j$, with a probability also preferential to the connection\nfunction $g(x_i,x_j)$. For the proper choice of $g$, the resulting networks\nhave generalized power laws, irrespective of the fitness distribution of\nvertices.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Synchronization in Scale Free networks with degree correlation: In this paper we study a model of synchronization process on scale free\nnetworks with degree-degree correlations. This model was already studied on\nthis kind of networks without correlations by Pastore y Piontti {\\it et al.},\nPhys. Rev. E {\\bf 76}, 046117 (2007). Here, we study the effects of the\ndegree-degree correlation on the behavior of the load fluctuations $W_s$ in the\nsteady state. We found that for assortative networks there exist a specific\ncorrelation where the system is optimal synchronized. In addition, we found\nthat close to this optimally value the fluctuations does not depend on the\nsystem size and therefore the system becomes fully scalable. This result could\nbe very important for some technological applications. On the other hand, far\nfrom the optimal correlation, $W_s$ scales logarithmically with the system\nsize.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Effective spreading from multiple leaders identified by percolation in\n  social networks: Social networks constitute a new platform for information propagation, but\nits success is crucially dependent on the choice of spreaders who initiate the\nspreading of information. In this paper, we remove edges in a network at random\nand the network segments into isolated clusters. The most important nodes in\neach cluster then form a group of influential spreaders, such that news\npropagating from them would lead to an extensive coverage and minimal\nredundancy. The method well utilizes the similarities between the\npre-percolated state and the coverage of information propagation in each social\ncluster to obtain a set of distributed and coordinated spreaders. Our tests on\nthe Facebook networks show that this method outperforms conventional methods\nbased on centrality. The suggested way of identifying influential spreaders\nthus sheds light on a new paradigm of information propagation on social\nnetworks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Diversity is Key: Fantasy football dream teams under budget constraints: Imagine you are managing a football team and have a fixed budget for\nsalaries. Which players should you draft for your team? We investigated this\nquestion using the wealth of data available from fantasy premier league\nfootball (soccer). Using the players' data from past seasons, for several\nseasons and several different budget constraints, we identified the highest\nscoring fantasy team for each season subject to each budget constraint. We then\ninvestigated quantifiable characteristics of these teams. Interestingly, across\nnearly every variable that is significant to the game of football and the\nbudget, these top teams display diversity across these variables. Our results\nindicate that diversity is a general feature of top performing teams.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A mechanistic model of gossip, reputations, and cooperation: Social reputations facilitate cooperation: those who help others gain a good\nreputation, making them more likely to receive help themselves. But when people\nhold private views of one another, this cycle of indirect reciprocity breaks\ndown, as disagreements lead to the perception of unjustified behavior that\nultimately undermines cooperation. Theoretical studies often assume\npopulation-wide agreement about reputations, invoking rapid gossip as an\nendogenous mechanism for reaching consensus. However, the theory of indirect\nreciprocity lacks a mechanistic description of how gossip actually generates\nconsensus. Here we develop a mechanistic model of gossip-based indirect\nreciprocity that incorporates two alternative forms of gossip: exchanging\ninformation with randomly selected peers or consulting a single gossip source.\nWe show that these two forms of gossip are mathematically equivalent under an\nappropriate transformation of parameters. We derive an analytical expression\nfor the minimum amount of gossip required to reach sufficient consensus and\nstabilize cooperation. We analyze how the amount of gossip necessary for\ncooperation depends on the benefits and costs of cooperation, the assessment\nrule (social norm), and errors in reputation assessment, strategy execution,\nand gossip transmission. Finally, we show that biased gossip can either\nfacilitate or hinder cooperation, depending on the direction and magnitude of\nthe bias. Our results contribute to the growing literature on cooperation\nfacilitated by communication, and they highlight the need to study strategic\ninteractions coupled with the spread of social information.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Effects of heterogeneous social interactions on flocking dynamics: Social relationships characterize the interactions that occur within social\nspecies and may have an important impact on collective animal motion. Here, we\nconsider a variation of the standard Vicsek model for collective motion in\nwhich interactions are mediated by an empirically motivated scale-free topology\nthat represents a heterogeneous pattern of social contacts. We observe that the\ndegree of order of the model is strongly affected by network heterogeneity:\nmore heterogeneous networks show a more resilient ordered state; while less\nheterogeneity leads to a more fragile ordered state that can be destroyed by\nsufficient external noise. Our results challenge the previously accepted\nequivalence between the {\\em static} Vicsek model and the equilibrium XY model\non the network of connections, and point towards a possible equivalence with\nmodels exhibiting a different symmetry.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Socioeconophysics: Opinion Dynamics for number of transactions and\n  price, a trader based model: Involving effects of media, opinion leader and other agents on the opinion of\nindividuals of market society, a trader based model is developed and utilized\nto simulate price via supply and demand. Pronounced effects are considered with\nseveral weights and some personal differences between traders are taken into\naccount. Resulting time series and probabilty distribution function involving a\npower law for price come out similar to the real ones.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Ordering in bidirectional pedestrian flows and its influence on the\n  fundamental diagram: Experiments under laboratory conditions were carried out to study the\nordering in bidirectional pedestrian streams and its influence on the\nfundamental diagram (density-speed-flow relation). The Voronoi method is used\nto resolve the fine structure of the resulting velocity-density relations and\nspatial dependence of the measurements. The data show that the specific flow\nconcept is applicable also for bidirectional streams. For various forms of\nordering in bidirectional streams, no large differences among density-flow\nrelationships are found in the observed density range. The fundamental diagrams\nof bidirectional streams with different forms of ordering are compared with\nthat of unidirectional streams. The result shows differences in the shape of\nthe relation for {\\rho} > 1.0 m-2. The maximum of the specific flow in\nunidirectional streams is significantly larger than that in all bidirectional\nstreams examined.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Effect of Recency to Human Mobility: In recent years, we have seen scientists attempt to model and explain human\ndynamics and, in particular, human movement. Many aspects of our complex life\nare affected by human movements such as disease spread and epidemics modeling,\ncity planning, wireless network development, and disaster relief, to name a\nfew. Given the myriad of applications it is clear that a complete understanding\nof how people move in space can lead to huge benefits to our society. In most\nof the recent works, scientists have focused on the idea that people movements\nare biased towards frequently-visited locations. According to them, human\nmovement is based on an exploration/exploitation dichotomy in which individuals\nchoose new locations (exploration) or return to frequently-visited locations\n(exploitation). In this work, we focus on the concept of recency. We propose a\nmodel in which exploitation in human movement also considers recently-visited\nlocations and not solely frequently-visited locations. We test our hypothesis\nagainst different empirical data of human mobility and show that our proposed\nmodel is able to better explain the human trajectories in these datasets.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Macroscopic and Microscopic Characteristics of Networks with\n  Time-variant Functionality for Evaluating Resilience to External\n  Perturbations: Knowledge of time-variant functionality of real-world physical, social, and\nengineered networks is critical to the understanding of the resilience of\nnetworks facing external perturbations. The majority of existing studies,\nhowever, focus only on the topological properties of networks for resilience\nassessment, which does not fully capture their dynamical resilience. In this\nstudy, we evaluate and quantify network resilience based both on the\nfunctionality states of links and on topology. We propose three independent\nmeasures---the failure scaling index (FSI), the weighted degree scaling index\n(WDSI), and the link functionality irregularity index (LFII)---that capture\nmacroscopic, microscopic, and temporal performance characteristics of networks.\nAccordingly, an integrated general resilience (GR) metric is used to assess\nperformance loss and recovery speed in networks with time-variant\nfunctionality. We test the proposed methods in the study of traffic networks\nunder urban flooding impacts in the context of Harris County, Texas, during\nHurricane Harvey using a high-resolution dataset, which contains temporal speed\nof 20,000 roads every 5 minutes for 5 months. Our results show that link\nweights and node weighted degrees with perturbed functionality in the traffic\nnetwork during flooding follow a scale-free distribution. Hence, three proposed\nmeasures capture clear resilience curves of the network as well as identify the\nirregularity of links. Accordingly, network performance measures and the\nmethodology for resilience quantification reveal insights into the extent of\nnetwork performance loss and recovery speed, suggesting possible improvements\nin network resilience in the face of external perturbations such as urban\nflooding.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Topology Induced Coarsening in Language Games: We investigate how very large populations are able to reach a global\nconsensus, out of local \"microscopic\" interaction rules, in the framework of a\nrecently introduced class of models of semiotic dynamics, the so-called Naming\nGame. We compare in particular the convergence mechanism for interacting agents\nembedded in a low-dimensional lattice with respect to the mean-field case. We\nhighlight that in low-dimensions consensus is reached through a coarsening\nprocess which requires less cognitive effort of the agents, with respect to the\nmean-field case, but takes longer to complete. In 1-d the dynamics of the\nboundaries is mapped onto a truncated Markov process from which we analytically\ncomputed the diffusion coefficient. More generally we show that the convergence\nprocess requires a memory per agent scaling as N and lasts a time N^{1+2/d} in\ndimension d<5 (d=4 being the upper critical dimension), while in mean-field\nboth memory and time scale as N^{3/2}, for a population of N agents. We present\nanalytical and numerical evidences supporting this picture.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Coupling Human Mobility and Social Ties: Studies using massive, passively data collected from communication\ntechnologies have revealed many ubiquitous aspects of social networks, helping\nus understand and model social media, information diffusion, and organizational\ndynamics. More recently, these data have come tagged with geographic\ninformation, enabling studies of human mobility patterns and the science of\ncities. We combine these two pursuits and uncover reproducible mobility\npatterns amongst social contacts. First, we introduce measures of mobility\nsimilarity and predictability and measure them for populations of users in\nthree large urban areas. We find individuals' visitations patterns are far more\nsimilar to and predictable by social contacts than strangers and that these\nmeasures are positively correlated with tie strength. Unsupervised clustering\nof hourly variations in mobility similarity identifies three categories of\nsocial ties and suggests geography is an important feature to contextualize\nsocial relationships. We find that the composition of a user's ego network in\nterms of the type of contacts they keep is correlated with mobility behavior.\nFinally, we extend a popular mobility model to include movement choices based\non social contacts and compare it's ability to reproduce empirical measurements\nwith two additional models of mobility.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Thermal properties of structurally balanced systems on diluted and\n  densified triangulations: The dynamics of social relations and the possibility of reaching the state of\nstructural balance (Heider balance) are discussed for various networks of\ninteracting actors under the influence of the temperature modeling the social\nnoise level. For that purpose, two main types of lattices are considered. The\nfirst is created by removing some links from a regular triangular lattice to\nproduce a diluted triangular lattice, and the second by adding more links to\ncreate an enhanced triangular lattice. In both those cases, the full range of\npossible graph densities is discussed, limited by the extreme cases of networks\nwhich consist of a small number of separated triads and fully connected\nnetworks. It is shown that the existence of the balanced state is not possible\nif the average node degree is too close to the value characterizing the regular\ntriangular lattice. Otherwise, both balanced (or partially balanced) and\nimbalanced states are possible, depending on the temperature. However, only for\ngraphs which are dense enough a phase transition of the first kind is observed,\nwhile less enhanced networks (and all diluted) indicate a smooth cross-over\nbetween the two states. The cross-over temperatures are size-independent only\nfor the diluted triangular lattices and depend on the size of the system for\nthe enhanced triangular lattices, as is the case also for the critical\ntemperatures of the phase transition observed in denser enhanced lattices.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Participatory Patterns in an International Air Quality Monitoring\n  Initiative: The issue of sustainability is at the top of the political and societal\nagenda, being considered of extreme importance and urgency. Human individual\naction impacts the environment both locally (e.g., local air/water quality,\nnoise disturbance) and globally (e.g., climate change, resource use). Urban\nenvironments represent a crucial example, with an increasing realization that\nthe most effective way of producing a change is involving the citizens\nthemselves in monitoring campaigns (a citizen science bottom-up approach). This\nis possible by developing novel technologies and IT infrastructures enabling\nlarge citizen participation. Here, in the wider framework of one of the first\nsuch projects, we show results from an international competition where citizens\nwere involved in mobile air pollution monitoring using low cost sensing\ndevices, combined with a web-based game to monitor perceived levels of\npollution. Measures of shift in perceptions over the course of the campaign are\nprovided, together with insights into participatory patterns emerging from this\nstudy. Interesting effects related to inertia and to direct involvement in\nmeasurement activities rather than indirect information exposure are also\nhighlighted, indicating that direct involvement can enhance learning and\nenvironmental awareness. In the future, this could result in better adoption of\npolicies towards decreasing pollution.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Cliophysics: A scientific analysis of recurrent historical events: Named after Clio, the Greek goddess of history, cliophysics is a daughter\n(and in a sense an extension) of econophysics. Like econophysics it relies on\nthe methodology of experimental physics. Its purpose is to conduct a scientific\nanalysis of historical events. Such events can be of sociological, political or\neconomic nature. In this last case cliophysics would coincide with\neconophysics. The main difference between cliophysics and econophysics is that\nthe description of historical events may be qualitative as well as\nquantitative. For the handling of qualitative accounts cliophysics has\ndeveloped an approach based on the identification of patterns. To detect a\npattern the main challenge is to break the \"noise barrier\". The very existence\nof patterns is what makes cliophysics possible and ensures its success. Briefly\nstated, once a pattern is detected, it allows predictions to be made. As the\ncapacity to make successful predictions is the hallmark of any science, it\nbecomes easy to decide whether or not the claim made in the title of the paper\nis indeed fulfilled. A number of examples of clusters of similar events will be\ngiven which should convince readers that historical events can be simplified\nalmost at will very much as in physics. One should not forget that physical\neffects are also subject to the environment. For instance, if tried at the\nequator, the experiment of the Foucault pendulum will fail. In the last part of\nthe paper, we describe cliophysical investigations conducted over the past\ndecades; they make us confident that cliophysics can be a valuable tool for\ndecision makers.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Renormalization flows in complex networks: Complex networks have acquired a great popularity in recent years, since the\ngraph representation of many natural, social and technological systems is often\nvery helpful to characterize and model their phenomenology. Additionally, the\nmathematical tools of statistical physics have proven to be particularly\nsuitable for studying and understanding complex networks. Nevertheless, an\nimportant obstacle to this theoretical approach is still represented by the\ndifficulties to draw parallelisms between network science and more traditional\naspects of statistical physics. In this paper, we explore the relation between\ncomplex networks and a well known topic of statistical physics:\nrenormalization. A general method to analyze renormalization flows of complex\nnetworks is introduced. The method can be applied to study any suitable\nrenormalization transformation. Finite-size scaling can be performed on\ncomputer-generated networks in order to classify them in universality classes.\nWe also present applications of the method on real networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Paradox of integration -- a computational model: The paradoxical aspect of integration of a social group has been highlighted\nby Peter Blau (Exchange and Power in Social Life, Wiley and Sons, 1964). During\nthe integration process, the group members simultaneously compete for social\nstatus and play the role of the audience. Here we show that when the\ncompetition prevails over the desire of approval, a sharp transition breaks all\nfriendly relations. However, as was described by Blau, people with high status\nare inclined to bother more with acceptance of others; this is achieved by\npraising others and revealing her/his own weak points. In our model, this\naction smooths the transition and improves interpersonal relations.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Detecting Structure of Complex Network by Quantum Bosonic Dynamics: We introduce a non-interacting boson model to investigate topological\nstructure of complex networks in the present paper. By exactly solving this\nmodel, we show that it provides a powerful analytical tool in uncovering the\nimportant properties of real-world networks. We find that the ground state\ndegeneracy of this model is equal to the number of connected components in the\nnetwork and the square of coefficients in the expansion of ground state gives\nthe averaged time for a random walker spending at each node in the infinite\ntime limit. Furthermore, the first excited state appears always on its largest\nconnected component. To show usefulness of this approach in practice, we carry\non also numerical simulations on some concrete complex networks. Our results\nare completely consistent with the previous conclusions derived by graph theory\nmethods.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Derivation of Relations between Scaling Exponents and Standard Deviation\n  Ratios: The law of allometric growth is one of basic rules for understanding urban\nevolution. The general form of this law is allometric scaling law. However, the\ndeep meaning and underlying rationale of the scaling exponents remain to be\nbrought to light. In this paper, the theories of linear algebra and regression\nanalysis are employed to reveal the mathematical and statistic essence of\nallometric scaling exponents. Suppose that the geometric measure relations\nbetween a set of elements in an urban system follow the allometric growth law.\nAn allometric scaling exponent is proved to equal in theory to the ratio of the\nstandard deviation of one logarithmic measure to the standard deviation of\nanother logarithmic measure. In empirical analyses based on observational data,\nthe scaling exponent is equal to the product between the standard deviation\nratio and the corresponding Pearson correlation coefficient. The mathematical\nderivation results can be verified by empirical analysis: the scaling exponent\nvalues based on the standard deviation ratios are completely identical to those\nbased on the conventional method. This finding can be generalized to city\nfractals and city size distribution to explain fractal dimensions of urban\nspace and Zipf scaling exponent of urban hierarchy. A conclusion can be reached\nthat scaling exponents reflect the ratios of characteristic lengths. This study\nmay be helpful for comprehending scaling from a new perspective and the\nconnections and distinctions between scaling and characteristic scales.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Reverse Engineering the Reproduction Number: A Framework for Data-Driven\n  Counterfactual Analysis, Strategy Evaluation, and Feedback Control of\n  Epidemics: During the COVID-19 pandemic, different countries, regions, and communities\nconstructed various epidemic models to evaluate spreading behaviors and assist\nin making mitigation policies. Model uncertainties, introduced by complex\ntransmission behaviors, contact-tracing networks, time-varying spreading\nparameters, and human factors, as well as insufficient data, have posed arduous\nchallenges for model-based approaches. To address these challenges, we propose\na novel framework for data-driven counterfactual analysis, strategy evaluation,\nand feedback control of epidemics, which leverages statistical information from\nepidemic testing data instead of constructing a specific model. Through reverse\nengineering the reproduction number by quantifying the impact of the\nintervention strategy, this framework tackles three primary problems: 1) How\nsevere would an outbreak have been without the implemented intervention\nstrategies? 2) What impact would varying the intervention strength have had on\nan outbreak? 3) How can we adjust the intervention intensity based on the\ncurrent state of an outbreak? Specifically, we consider the epidemic\nintervention policies such as the testing-for-isolation strategy as an example,\nwhich was successfully implemented by the University of Illinois\nUrbana-Champaign (UIUC) and Purdue University (Purdue) during the COVID-19\npandemic. By leveraging data collected by UIUC and Purdue, we validate the\neffectiveness of the proposed data-driven framework.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Effect of Spreading Knowledge Centers -- A Physics-based Approach: We use a simple physics-inspired model to get an idea about how to enhance\nthe speed with which a society becomes educated if we strategically place our\nknowledge spreading centers (teachers or educational institutions). We study\nknowledge spreading using the Ising model, a well-studied model used in\nphysics, specifically statistical mechanics, to describe the phenomenon of\nferromagnetism. In the social context, up and down spins are mapped to\nknowledgeable and ignorant individuals. We introduce some knowledgeable\nindividuals into an otherwise ignorant society and see how their number\nincreases with time, when evolved using the Metropolis algorithm. We find that\nthe knowledge of the society grows faster when the initial group of\nknowledgeable individuals is maximally spread out. We quantify this effect\nusing the doubling time and look at the distribution of the doubling time as a\nfunction of \"temperature\". In the social context, the energy is identified as\nthe (lack of) happiness of neighbours and temperature is a parameter that\nquantifies how important happiness is in the society. We point out several\nlimitations of this study in order to facilitate future research.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Promise and Pitfalls of Extending Google's PageRank Algorithm to\n  Citation Networks: We review our recent work on applying the Google PageRank algorithm to find\nscientific \"gems\" among all Physical Review publications, and its extension to\nCiteRank, to find currently popular research directions. These metrics provide\na meaningful extension to traditionally-used importance measures, such as the\nnumber of citations and journal impact factor. We also point out some pitfalls\nof over-relying on quantitative metrics to evaluate scientific quality.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Looplessness in networks is linked to trophic coherence: Many natural, complex systems are remarkably stable thanks to an absence of\nfeedback acting on their elements. When described as networks, these exhibit\nfew or no cycles, and associated matrices have small leading eigenvalues. It\nhas been suggested that this architecture can confer advantages to the system\nas a whole, such as `qualitative stability', but this observation does not in\nitself explain how a loopless structure might arise. We show here that the\nnumber of feedback loops in a network, as well as the eigenvalues of associated\nmatrices, are determined by a structural property called trophic coherence, a\nmeasure of how neatly nodes fall into distinct levels. Our theory correctly\nclassifies a variety of networks -- including those derived from genes,\nmetabolites, species, neurons, words, computers and trading nations -- into two\ndistinct regimes of high and low feedback, and provides a null model to gauge\nthe significance of related magnitudes. Since trophic coherence suppresses\nfeedback, whereas an absence of feedback alone does not lead to coherence, our\nwork suggests that the reasons for `looplessness' in nature should be sought in\ncoherence-inducing mechanisms.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Spatial regionalization based on optimal information compression: Regionalization, spatially contiguous clustering, provides a means to reduce\nthe effect of noise in sampled data and identify homogeneous areas for policy\ndevelopment among many other applications. Existing regionalization methods\nrequire user input such as the number of regions or a similarity measure\nbetween regions, which does not allow for the extraction of the natural regions\ndefined solely by the data itself. Here we view the problem of regionalization\nas one of data compression and develop an efficient, parameter-free\nregionalization algorithm based on the minimum description length principle. We\ndemonstrate that our method is capable of recovering planted spatial clusters\nin noisy synthetic data, and that it can meaningfully coarse-grain real\ndemographic data. Using our description length formulation, we find that\nspatial ethnoracial data in U.S. metropolitan areas has become less\ncompressible over the period from 1980 to 2010, reflecting the rising\ncomplexity of urban segregation patterns in these metros.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Using network science to analyze football passing networks: dynamics,\n  space, time and the multilayer nature of the game: From the diversity of applications of Network Science, in this Opinion Paper\nwe are concerned about its potential to analyze one of the most extended group\nsports: Football (soccer in U.S. terminology). As we will see, Network Science\nallows addressing different aspects of the team organization and performance\nnot captured by classical analyses based on the performance of individual\nplayers. The reason behind relies on the complex nature of the game, which,\nparaphrasing the foundational paradigm of complexity sciences \"can not be\nanalyzed by looking at its components (i.e., players) individually but, on the\ncontrary, considering the system as a whole\" or, in the classical words of\nafter-match interviews \"it's not just me, it's the team\".",
        "category": "physics_soc-ph"
    },
    {
        "text": "Lowest Degree Decomposition of Complex Networks: The heterogeneous structure implies that a very few nodes may play the\ncritical role in maintaining structural and functional properties of a\nlarge-scale network. Identifying these vital nodes is one of the most important\ntasks in network science, which allow us to better conduct successful social\nadvertisements, immunize a network against epidemics, discover drug target\ncandidates and essential proteins, and prevent cascading breakdowns in power\ngrids, financial markets and ecological systems. Inspired by the nested nature\nof real networks, we propose a decomposition method where at each step the\nnodes with the lowest degree are pruned. We have strictly proved that this\nso-called lowest degree decomposition (LDD) is a subdivision of the famous\nk-core decomposition. Extensive numerical analyses on epidemic spreading,\nsynchronization and nonlinear mutualistic dynamics show that the LDD can more\naccurately find out the most influential spreaders, the most efficient\ncontrollers and the most vulnerable species than k-core decomposition and other\nwell-known indices. The present method only makes use of local topological\ninformation, and thus has high potential to become a powerful tool for network\nanalysis.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Hiding in Temporal Networks: Social network analysis tools can infer various attributes just by\nscrutinizing one's connections. Several researchers have studied the problem\nfaced by an evader whose goal is to strategically rewire their social\nconnections in order to mislead such tools, thereby concealing their private\nattributes. However, to date, this literature has only considered static\nnetworks, while neglecting the more general case of temporal networks, where\nthe structure evolves over time. Driven by this observation, we study how the\nevader can conceal their importance from an adversary armed with temporal\ncentrality measures. We consider computational and structural aspects of this\nproblem: Is it computationally feasible to calculate optimal ways of hiding? If\nit is, what network characteristics facilitate hiding? This topic has been\nstudied in static networks, but in this work, we add realism to the problem by\nconsidering temporal networks of edges changing in time. We find that it is\nusually computationally infeasible to find the optimal way of hiding. On the\nother hand, by manipulating one's contacts, one could add a surprising amount\nof privacy. Compared to static networks, temporal networks offer more\nstrategies for this type of manipulation and are thus, to some extent, easier\nto hide in.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Colombian export capabilities: building the firms-products network: In this paper we analyse the bipartite Colombian firms-products network,\nthroughout a period of five years, from 2010 to 2014. Our analysis depicts a\nstrongly modular system, with several groups of firms specializing in the\nexport of specific categories of products. These clusters have been detected by\nrunning the bipartite variant of the traditional modularity maximization,\nrevealing a bi-modular structure. Interestingly, this finding is refined by\napplying a recently-proposed algorithm for projecting bipartite networks on the\nlayer of interest and, then, running the Louvain algorithm on the resulting\nmonopartite representations. Important structural differences emerge upon\ncomparing the Colombian firms-products network with the World Trade Web, in\nparticular, the bipartite representation of the latter is not characterized by\na similar block-structure, as the modularity maximization fails in revealing\n(bipartite) nodes clusters. This points out that economic systems behave\ndifferently at different scales: while countries tend to diversify their\nproduction --potentially exporting a large number of different products-- firms\nspecialize in exporting (substantially very limited) baskets of basically\nhomogeneous products.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Fundamental Patterns of Signal Propagation in Complex Networks: Various disasters stem from minor perturbations, such as the spread of\ninfectious diseases, cascading failure in power grids, etc. Analyzing\nperturbations is crucial for both theoretical and application fields. Previous\nresearchers have proposed basic propagation patterns for perturbation and\nexplored the impact of basic network motifs on the collective response to these\nperturbations, However, the current framework is limited in its ability to\ndecouple interactions, and therefore cannot analyze more complex structures. In\nthis article, we establish an effective, robust and powerful propagation\nframework under a general dynamic model. This framework reveals common and\ndense network motifs that exert a critical influence on signal propagation,\noften spanning orders of magnitude compared with conclusions generated by\nprevious work. Moreover, our framework provides a new approach to understand\nthe fundamental principles of complex systems and the negative feedback\nmechanism, which is of great significance for research of system controlling\nand network resilience.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A stochastic approach for quantifying immigrant integration: the Spanish\n  test case: We apply stochastic process theory to the analysis of immigrant integration.\nUsing a unique and detailed data set from Spain, we study the relationship\nbetween local immigrant density and two social and two economic immigration\nquantifiers for the period 1999-2010. As opposed to the classic time-series\napproach, by letting immigrant density play the role of \"time\", and the\nquantifier the role of \"space\" it become possible to analyze the behavior of\nthe quantifiers by means of continuous time random walks. Two classes of\nresults are obtained. First we show that social integration quantifiers evolve\nfollowing pure diffusion law, while the evolution of economic quantifiers\nexhibit ballistic dynamics. Second we make predictions of best and worst case\nscenarios taking into account large local fluctuations. Our stochastic process\napproach to integration lends itself to interesting forecasting scenarios\nwhich, in the hands of policy makers, have the potential to improve political\nresponses to integration problems. For instance, estimating the standard\nfirst-passage time and maximum-span walk reveals local differences in\nintegration performance for different immigration scenarios. Thus, by\nrecognizing the importance of local fluctuations around national means, this\nresearch constitutes an important tool to assess the impact of immigration\nphenomena on municipal budgets and to set up solid multi-ethnic plans at the\nmunicipal level as immigration pressure build.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Impact of the Russia-Ukraine conflict on the international staple\n  agrifood trade networks: The Russia-Ukraine conflict is a growing concern worldwide and poses serious\nthreats to regional and global food security. Using monthly trade data for\nmaize, rice, and wheat from 2016/1 to 2022/12, this paper constructs three\ninternational crop trade networks (iCTNs) and an aggregate international food\ntrade network (iFTN). We aim to examine the structural changes following the\noccurrence of the Russia-Ukraine conflict. We find significant shifts in the\nnumber of edges, average degree, density, efficiency, and natural connectivity\nin the third quarter of 2022, particularly in the international wheat trade\nnetwork. Additionally, we have shown that political reasons have caused more\npronounced changes in the trade connections between the economies of the North\nAtlantic Treaty Organization and Russia than with Ukraine. This paper could\nprovide insights into the negative impact of geopolitical conflicts on the\nglobal food system and encourage a series of effective strategies to mitigate\nthe negative impact of the conflict on global food trade.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Alternative Agriculture Land-Use Transformation Pathways by\n  Partial-Equilibrium Agricultural Sector Model: A Mathematical Approach: Humanity's progress in combating hunger, poverty, and child mortality is\nmarred by escalating environmental degradation due to rising greenhouse gas\nemissions and climate change impacts. Despite positive developments, ecosystems\nare suffering globally. Regional strategies for mitigating and adapting to\nclimate change must be viewed from a global perspective. The 2015 UN\nSustainable Development Goals reflect the challenge of balancing social and\nenvironmental aspects for sustainable development. Agriculture, vital for food\nproduction, also threatens Earth systems. A study examines the interplay of\nland-use impacts, modeling crop and livestock trade, and their effects on\nclimate, biodiversity, water, and land using a Partial-Equilibrium Agricultural\nSector Model. Different scenarios involving taxing externalities related to\nEarth processes were tested. Results show synergies in reducing emissions,\nbiodiversity loss, water use, and phosphorus pollution, driven by shifts in\ncrop management. Nitrogen application and deforestation scenarios exhibit\nweaker synergies and more conflicts. The study offers insights into SDG\ninteractions and the potential for sustainable farming.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Benchmark graphs for testing community detection algorithms: Community structure is one of the most important features of real networks\nand reveals the internal organization of the nodes. Many algorithms have been\nproposed but the crucial issue of testing, i.e. the question of how good an\nalgorithm is, with respect to others, is still open. Standard tests include the\nanalysis of simple artificial graphs with a built-in community structure, that\nthe algorithm has to recover. However, the special graphs adopted in actual\ntests have a structure that does not reflect the real properties of nodes and\ncommunities found in real networks. Here we introduce a new class of benchmark\ngraphs, that account for the heterogeneity in the distributions of node degrees\nand of community sizes. We use this new benchmark to test two popular methods\nof community detection, modularity optimization and Potts model clustering. The\nresults show that the new benchmark poses a much more severe test to algorithms\nthan standard benchmarks, revealing limits that may not be apparent at a first\nanalysis.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Bi-layer voter model: Modeling intolerant/tolerant positions and bots in\n  opinion dynamics: The diffusion of opinions in Social Networks is a relevant process for\nadopting positions and attracting potential voters in political campaigns.\nOpinion polarization, bias, targeted diffusion, and the radicalization of\npostures are key elements for understanding voting dynamics. In particular,\nsocial bots are a new element that can have a pronounced effect on the\nformation of opinions during elections by, for instance, creating fake accounts\nin social networks to manipulate elections. Here we propose a voter model\nincorporating bots and radical or intolerant individuals in the decision-making\nprocess. The dynamics of the system occur in a multiplex network of interacting\nagents composed of two layers, one for the dynamics of opinions where agents\nchoose between two possible alternatives, and the other for the tolerance\ndynamics, in which agents adopt one of two tolerance levels. The tolerance\naccounts for the likelihood to change opinion in an interaction, with tolerant\n(intolerant) agents switching opinion with probability $1.0$ ($\\gamma \\le 1$).\nWe find that intolerance leads to a consensus of tolerant agents during an\ninitial stage that scales as $\\tau^+ \\sim \\gamma^{-1} \\ln N$, who then reach an\nopinion consensus during the second stage in a time that scales as $\\tau \\sim\nN$, where $N$ is the number of agents. Therefore, very intolerant agents\n($\\gamma \\ll 1$) could considerably slow down dynamics towards the final\nconsensus state. We also find that the inclusion of a fraction\n$\\sigma_{\\mathbb{B}}^-$ of bots breaks the symmetry between both opinions,\ndriving the system to a consensus of intolerant agents with the bots' opinion.\nThus, bots eventually impose their opinion to the entire population, in a time\nthat scales as $\\tau_B^- \\sim \\gamma^{-1}$ for $\\gamma \\ll\n\\sigma_{\\mathbb{B}}^-$ and $\\tau_B^- \\sim 1/\\sigma_{\\mathbb{B}}^-$ for\n$\\sigma_{\\mathbb{B}}^- \\ll \\gamma$.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Social Value of Dark Energy: Astrophysics is a social enterprise exemplified here by the Dark Energy\nSurvey (DES) which completed its fieldwork in 2019 after 16 years of\npreparation and observation, while data analysis continues. Society funds\nastrophysics on a grand scale. For human capital and for governance the\ndiscipline draws on a self-governing \"republic of science\", while the funds\nwere provided by philanthropists in the past, and by governments today. The\nbenefits accrue initially to scientists themselves, in the form of a rewarding\nvocation. For the social benefit it is tempting to apply formal cost benefit\nanalysis, but that approach ignores the option value of science and imposes\nquestionable assumptions from welfare economics. Astrophysics generates some\nuseful spinoffs, offers attractive careers, appeals to the popular imagination,\nspeaks to metaphysical cravings and constitutes a good in itself. The rise of\nAI also suggests a role in exploring future habitats for intelligence and\ncognition.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Covid-19 Modeling towards socioeconomic and health data from New South\n  Wales (NSW) -- Australia: An approach via Geospatial Analysis and\n  Geographically Weighted Poisson Regression (GWPR): An integrated approach of spatial data analysis and Geographically Weighted\nPoisson Regression (GWPR) along with global regression techniques are used in\nthis study. This approach aims to model relationships between dependent\nvariable Covid-19 and independent variables from socioeconomic and pre-existing\nhealth conditions within the local government area (LGA) in New South Wales\n(NSW)-Australia. Based on geospatial data analysis and a step-by-step procedure\nin building both global and GWPR models, four (4) independent variables are\nfinally selected to investigate relationships between dependent and independent\nvariables at the local scale. The GWPR model's results with the Goodness-of-Fit\n(R2) range between 45-73% exhibit positive relationships between Covid-19 and\nthe total population, the cancers, and the people with ages between 60 and 85\nin most of the NSW state. Meanwhile, a negative relationship is observed\nbetween Covid-19 and the ischaemic heart disease; however, the estimated\ncoefficients for this relationship are very low and close to zero; hence\nfurther investigation, including assessment from a different perspective, is\nnecessary for validation. In conclusion, the model suggests that the\nrelationships between the dependent variable and independent variables are\nnonstationary. Therefore, GWPR model calibration plays a vital role in\ngeographic modelling at the local scale.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Percolate or die: Multi-percolation decides the struggle between\n  competing innovations: Competition is one of the most fundamental phenomena in physics, biology and\neconomics. Recent studies of the competition between innovations have\nhighlighted the influence of switching costs and interaction networks, but the\nproblem is still puzzling. We introduce a model that reveals a novel\nmulti-percolation process, which governs the struggle of innovations trying to\npenetrate a market. We find that innovations thrive as long as they percolate\nin a population, and one becomes dominant when it is the only one that\npercolates. Besides offering a theoretical framework to understand the\ndiffusion of competing innovations in social networks, our results are also\nrelevant to model other problems such as opinion formation, political\npolarization, survival of languages and the spread of health behavior.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A model bridging chimera state and explosive synchronization: Global and partial synchronization are the two distinctive forms of\nsynchronization in coupled oscillators and have been well studied in the past\ndecades. Recent attention on synchronization is focused on the chimera state\n(CS) and explosive synchronization (ES), but little attention has been paid to\ntheir relationship. We here study this topic by presenting a model to bridge\nthese two phenomena, which consists of two groups of coupled oscillators and\nits coupling strength is adaptively controlled by a local order parameter. We\nfind that this model displays either CS or ES in two limits. In between the two\nlimits, this model exhibits both CS and ES, where CS can be observed for a\nfixed coupling strength and ES appears when the coupling is increased\nadiabatically. Moreover, we show both theoretically and numerically that there\nare a variety of CS basin patterns for the case of identical oscillators,\ndepending on the distributions of both the initial order parameters and the\ninitial average phases. This model suggests a way to easily observe CS, in\ncontrast to others models having some (weak or strong) dependence on initial\nconditions.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Message Passing for Integrating and Assessing Renewable Generation in a\n  Redundant Power Grid: A simplified model of a redundant power grid is used to study integration of\nfluctuating renewable generation. The grid consists of large number of\ngenerator and consumer nodes. The net power consumption is determined by the\ndifference between the gross consumption and the level of renewable generation.\nThe gross consumption is drawn from a narrow distribution representing the\npredictability of aggregated loads, and we consider two different distributions\nrepresenting wind and solar resources. Each generator is connected to D\nconsumers, and redundancy is built in by connecting R of these consumers to\nother generators. The lines are switchable so that at any instance each\nconsumer is connected to a single generator. We explore the capacity of the\nrenewable generation by determining the level of \"firm\" generation capacity\nthat can be displaced for different levels of redundancy R. We also develop\nmessage-passing control algorithm for finding switch settings where no\ngenerator is overloaded.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Equitable Transit Network Design Under Uncertainty: This paper proposes a bilevel transit network design problem considering\nsupply side uncertainty. The upper level problem determines frequency settings\nto simultaneously maximize the efficiency and equity measures, which are\ndefined by the reduction in the total effective travel cost and the minimum\nreduction in the effective travel cost of all OD pairs, respectively. The lower\nlevel problem is the reliability based transit assignment problem that captures\nthe effects of supply-side uncertainty on passengers route choice\nbehavior.Numerical studies demonstrate that 1) the Pareto frontier may not be\nconvex; 2) it is possible to improve the efficiency and equity objectives\nsimultaneously; 3) increasing the frequency could worsen the equity measure; 4)\npassengers risk attitude affects the rate of substitution between the two\nobjectives.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Evidence-based policy-making in sports funding using a data-driven\n  optimization approach: Many European countries face rising obesity rates among children, compounded\nby decreased opportunities for sports activities during the COVID-19 pandemic.\nAccess to sports facilities depends on multiple factors, such as geographic\nlocation, proximity to population centers, budgetary constraints, and other\nsocio-economic covariates. Here we show how an optimal allocation of government\nfunds towards sports facilitators (e.g. sports clubs) can be achieved in a\ndata-driven simulation model that maximizes children's access to sports\nfacilities. We compile a dataset for all 1,854 football clubs in Austria,\nincluding estimates for their budget, geolocation, tally, and the age profile\nof their members. We find a characteristic sub-linear relationship between the\nnumber of active club members and the budget, which depends on the\nsocio-economic conditions of the clubs' municipality. In the model, where we\nassume this relationship to be causal, we evaluate different funding\nstrategies. We show that an optimization strategy where funds are distributed\nbased on regional socio-economic characteristics and club budgets outperforms a\nnaive approach by up to 117\\% in attracting children to sports clubs for 5\nmillion Euros of additional funding. Our results suggest that the impact of\npublic funding strategies can be substantially increased by tailoring them to\nregional socio-economic characteristics in an evidence-based and individualized\nway.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Identifying overlapping communities in social networks using multi-scale\n  local information expansion: Most existing approaches for community detection require complete information\nof the graph in a specific scale, which is impractical for many social\nnetworks. We propose a novel algorithm that does not embrace the universal\napproach but instead of trying to focus on local social ties and modeling\nmulti-scales of social interactions occurring in those networks. Our method for\nthe first time optimizes the topological entropy of a network and uncovers\ncommunities through a novel dynamic system converging to a local minimum by\nsimply updating the membership vector with very low computational complexity.\nIt naturally supports overlapping communities through associating each node\nwith a membership vector which describes node's involvement in each community.\nThis way, in addition to uncover overlapping communities, we can also describe\ndifferent multi-scale partitions by tuning the characteristic size of modules\nfrom the optimal partition. Because of the high efficiency and accuracy of the\nalgorithm, it is feasible to be used for the accurate detection of community\nstructure in real networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The adoption of non-pharmaceutical interventions and the role of digital\n  infrastructure during the COVID-19 Pandemic in Colombia, Ecuador, and El\n  Salvador: Adherence to the non-pharmaceutical interventions (NPIs) put in place to\nmitigate the spreading of infectious diseases is a multifaceted problem.\nSocio-demographic, socio-economic, and epidemiological factors can influence\nthe perceived susceptibility and risk which are known to affect behavior.\nFurthermore, the adoption of NPIs is dependent upon the barriers, real or\nperceived, associated with their implementation. We study the determinants of\nNPIs adherence during the first wave of the COVID-19 Pandemic in Colombia,\nEcuador, and El Salvador. Analyses are performed at the level of municipalities\nand include socio-economic, socio-demographic, and epidemiological indicators.\nFurthermore, by leveraging a unique dataset comprising tens of millions of\ninternet Speedtest measurements from Ookla, we investigate the quality of the\ndigital infrastructure as a possible barrier to adoption. We use publicly\navailable data provided by Meta capturing aggregated mobility changes as a\nproxy of adherence to NPIs. Across the three countries considered, we find a\nsignificant correlation between mobility drops and digital infrastructure\nquality. The relationship remains significant after controlling for several\nfactors including socio-economic status, population size, and reported COVID-19\ncases. This finding suggests that municipalities with better connectivity were\nable to afford higher mobility reductions. The link between mobility drops and\ndigital infrastructure quality is stronger at the peak of NPIs stringency. We\nalso find that mobility reductions were more pronounced in larger, denser, and\nwealthier municipalities. Our work provides new insights on the significance of\naccess to digital tools as an additional factor influencing the ability to\nfollow social distancing guidelines during a health emergency",
        "category": "physics_soc-ph"
    },
    {
        "text": "Are Opinions Based on Science: Modelling Social Response to Scientific\n  Facts: As scientists we like to think that modern societies and their members base\ntheir views, opinions and behaviour on scientific facts. This is not\nnecessarily the case, even though we are all (over-) exposed to information\nflow through various channels of media, i.e. newspapers, television, radio,\ninternet, and web. It is thought that this is mainly due to the conflicting\ninformation on the mass media and to the individual attitude (formed by\ncultural, educational and environmental factors), that is, one external factor\nand another personal factor. In this paper we will investigate the dynamical\ndevelopment of opinion in a small population of agents by means of a\ncomputational model of opinion formation in a co-evolving network of socially\nlinked agents. The personal and external factors are taken into account by\nassigning an individual attitude parameter to each agent, and by subjecting all\nto an external but homogeneous field to simulate the effect of the media. We\nthen adjust the field strength in the model by using actual data on scientific\nperception surveys carried out in two different populations, which allow us to\ncompare two different societies. We interpret the model findings with the aid\nof simple mean field calculations. Our results suggest that scientifically\nsound concepts are more difficult to acquire than concepts not validated by\nscience, since opposing individuals organize themselves in close communities\nthat prevent opinion consensus.",
        "category": "physics_soc-ph"
    },
    {
        "text": "First Order Models of Human Crowds with Behavioral-Social Dynamics: This paper presents a new approach to behavioral-social dynamics of human\ncrowds. First order models are derived based on mass conservation at the\nmacroscopic scale, while methods of the kinetic theory are used to model the\ndecisional process by which walking individuals select their velocity\ndirection. Crowd heterogeneity is modeled by dividing the whole system into\nsubsystems identified by different features. The passage from one subsystem to\nthe other is induced by interactions. It is shown how heterogeneous individual\nbehaviors can modify the collective dynamics, as well as how local unusual\nbehaviors can propagate in the crowd. The paper also proposes a system approach\nto the modeling of the dynamics in complex venues, where individuals move\nthrough areas with different features.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Robustness analysis of car-following models for full speed range ACC\n  systems: Adaptive cruise control systems are fundamental components of the automation\nof the driving. At upper control level, ACC systems are based on car-following\nmodels determining the acceleration rate of a vehicle according to the distance\ngap to the predecessor and the speed difference. The pursuit strategy consists\nin keeping a constant time gap with the predecessor, as recommended by\nindustrial norms for ACC systems. The systematic active safety of the systems\nis tackled thanks to local and string stability analysis. Several classical\nconstant time gap linear and non-linear car-following models are compared. We\ncritically evaluate the stability robustness of the models against latency,\nnoise and measurement error, heterogeneity, or kinetic constraints operating in\nthe dynamics at lower control level. The results highlight that many factors\ncan perturb the stability and induce the formation of stop-and-go waves, even\nfor intrinsically stable car-following models.",
        "category": "physics_soc-ph"
    },
    {
        "text": "On the distribution of time-to-proof of mathematical conjectures: What is the productivity of Science? Can we measure an evolution of the\nproduction of mathematicians over history? Can we predict the waiting time till\nthe proof of a challenging conjecture such as the P-versus-NP problem?\nMotivated by these questions, we revisit a suggestion published recently and\ndebated in the \"New Scientist\" that the historical distribution of\ntime-to-proof's, i.e., of waiting times between formulation of a mathematical\nconjecture and its proof, can be quantified and gives meaningful insights in\nthe future development of still open conjectures. We find however evidence that\nthe mathematical process of creation is too much non-stationary, with too\nlittle data and constraints, to allow for a meaningful conclusion. In\nparticular, the approximate unsteady exponential growth of human population,\nand arguably that of mathematicians, essentially hides the true distribution.\nAnother issue is the incompleteness of the dataset available. In conclusion we\ncannot really reject the simplest model of an exponential rate of conjecture\nproof with a rate of 0.01/year for the dataset that we have studied,\ntranslating into an average waiting time to proof of 100 years. We hope that\nthe presented methodology, combining the mathematics of recurrent processes,\nlinking proved and still open conjectures, with different empirical\nconstraints, will be useful for other similar investigations probing the\nproductivity associated with mankind growth and creativity.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Modelling Covid-19 epidemic in Mexico, Finland and Iceland: Over the past two decades there has been a number of global outbreaks of\nviral diseases. This has accelerated the efforts to model and forecast the\ndisease spreading, in order to find ways to confine the spreading regionally\nand between regions. Towards this we have devised a model of geographical\nspreading of viral infections due to human spatial mobility and adapted it to\nthe latest COVID-19 pandemic. In this the region to be modelled is overlaid\nwith a two-dimensional grid weighted with the population density defined cells,\nin each of which a compartmental SEIRS system of delay difference equations\nsimulate the local dynamics (microdynamics) of the disease. The infections\nbetween cells are stochastic and allow for the geographical spreading of the\nvirus over the two-dimensional space (macrodynamics). This approach allows to\nseparate the parameters related to the biological aspects of the disease from\nthe ones that represent the spatial contagious behaviour through different\nkinds of mobility of people acting as virus carriers. These provide sufficient\ninformation to trace the evolution of the pandemic in different situations. In\nparticular we have applied this approach to three in many ways different\ncountries, Mexico, Finland and Iceland and found that the model is capable of\nreproducing and predicting the stochastic global path of the pandemic. This\nstudy sheds light on how the diverse cultural and socioeconomic aspects of a\ncountry influence the evolution of the epidemics and also the efficacy of\nsocial distancing and other confinement measures.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Evolving small-world scale-free networks consist of cliques: We present a family of scale-free network model consisting of cliques, which\nis established by a simple recursive algorithm. We investigate the networks\nboth analytically and numerically. The obtained analytical solutions show that\nthe networks follow a power-law degree distribution, with degree exponent\ncontinuously tuned between 2 and 3. The exact expression of clustering\ncoefficient is also provided for the networks. Furthermore, the investigation\nof the average path length reveals that the networks possess small-world\nfeature. Interestingly, we find that a special case of our model can be mapped\ninto the Yule process.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Group-level selection avoids the tragedy of the commons: The public goods game is a famous example illustrating the tragedy of the\ncommons. In this game cooperating individuals contribute to a pool, which in\nturn is distributed to all members of the group, including defectors who reap\nthe same rewards as cooperators without having made a contribution before. The\nquestion is now, how to incentivize group members to all cooperate as it\nmaximizes the common good. While costly punishment presents one such method,\nthe cost of punishment still reduces the common good. Here we show how\ngroup-level selection can be such an incentive, and specifically how even\nfractions of group-level selection can overcome the benefits defectors receive.\nFurther, we show how punishment and group-level selection interact. This work\nsuggests that a redistribution similar to a basic income that is coupled to the\neconomic success of the entire group could overcome the tragedy of the commons.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Core-periphery organization of complex networks: Networks may, or may not, be wired to have a core that is both itself densely\nconnected and central in terms of graph distance. In this study we propose a\ncoefficient to measure if the network has such a clear-cut core-periphery\ndichotomy. We measure this coefficient for a number of real-world and model\nnetworks and find that different classes of networks have their characteristic\nvalues. For example do geographical networks have a strong core-periphery\nstructure, while the core-periphery structure of social networks (despite their\npositive degree-degree correlations) is rather weak. We proceed to study radial\nstatistics of the core, i.e. properties of the n-neighborhoods of the core\nvertices for increasing n. We find that almost all networks have unexpectedly\nmany edges within n-neighborhoods at a certain distance from the core\nsuggesting an effective radius for non-trivial network processes.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Memory Order Decomposition of Symbolic Sequences: We introduce a general method for the study of memory in symbolic sequences\nbased on higher-order Markov analysis. The Markov process that best represents\na sequence is expressed as a mixture of matrices of minimal orders, enabling\nthe definition of the so-called memory profile, which unambiguously reflects\nthe true order of correlations. The method is validated by recovering the\nmemory profiles of tunable synthetic sequences. Finally, we scan real data and\nshowcase with practical examples how our protocol can be used to extract\nrelevant stochastic properties of symbolic sequences.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Intermittent Control Properties of Car Following: Theory and Driving\n  Simulator Experiments: A rather simple car driving simulator was created based on the available open\nsource engine TORCS and used to analyze the basic features of human behavior in\ncar driving within the car-following setups. Eight subjects with different\nskill in driving real cars participated in these experiments. They were\ninstructed to drive a virtual car without overtaking the lead car driven by\ncomputer at a fixed speed and not to lose sight of it. Moreover, these\nexperiments were conducted with four different speed including 60km/h, 80km/h,\n100km/h, and 120km/h. Based on the collected data the distribution of the\nheadway, velocity, acceleration, and jerk are constructed and compared with\navailable experimental data collected previously by the analysis of the real\ntraffic flow. A new model for car-following is proposed capture the found\nproperties. As the main results we draw a conclusion that the human behavior in\ncar driving should be categorized as a generalized intermittent control with\nnoise-driven activation of the active phase. Besides, we hypothesize that the\nextended phase space required for modeling human actions in car driving has to\ncomprise four phase variables, namely, the headway distance, the velocity of\ncar, its acceleration, and the car jerk, i.e., the time derivative of the car\nacceleration. This time, the time pattern of pedal pushing and the distribution\nof time derivative of pedal was utilized in addition to previous variables.\nMoreover, all subjects' driving data were categorized as some styles with their\nshapes.",
        "category": "physics_soc-ph"
    },
    {
        "text": "COVID-19 propagation by diffusion -- a two-dimensional approach for\n  Germany: Diffusion comes anytime and everywhere. If there is a gradient or a potential\ndifference of a quantity a diffusion process happens and this ends if an\nequilibrium is reached only. The concentration of a species maybe such\nquantity, or the voltage. An electric currant will be driven by a voltage\ndifference for example. In this COVID-19 pandemic one observes both regions\nwith low incidence and other ones with high incidence. The local different\npeople density could be a reason for that. In populous areas like big cities or\ncongested urban areas higher COVID-19 incidences could be observed than in\nrural regions. The aim of this paper consists in the application of a diffusion\nconcept to describe one possible issue of the the COVID-19 propagation. This\nwill be discussed for the German situation based on the quite different\nincidence data for the different federal states of Germany.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Global warming in figures and the question of its treatment: some\n  historical and epistemological views: We first recall fundamentals of elementary climate physics: solar constant,\nradiative balance, greenhouse effect, astronomical parameters of the climate\n(theory of Milankovitch). Without disputing the analyzes of climatologists and\nthe famous Keeling curve revealing in an indisputable way the increase in\nCO$_{2}$ in the atmosphere since the industrial revolution, we nevertheless\ninsist on the main contributor to the greenhouse effect which is, as we know,\nwater vapor. Faced with the difficulties that there will be in imposing\nzero-carbon policies everywhere in the world (and especially in developing\ncountries), we show that it would perhaps be in our interest to act on soil\ndrought, which amounts, in fact, to being interested in the clouds. The\ndecrease in cloud cover, due to a lack of water fixation in the soil, in fact\nincreases the general temperature and therefore the greenhouse effect. Acting\non CO$_{2}$ will always have, in this context, much less effect than acting on\nwater vapor, even indirectly. Despite the difficulty of making this action\nsustainable, due to the balance of atmospheric water vapor and the oceans, it\nwould be in our interest not to neglect this path and also possibly increase\nforest cover for this purpose, given the problems of setting up zero-carbon\npolicy on a global scale. In desperation, one can also consider protecting the\nEarth with an artificial dust cloud.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Jamming transitions induced by an attraction in pedestrian flow: We numerically study jamming transitions in pedestrian flow interacting with\nan attraction, mostly based on the social force model for pedestrians who can\njoin the attraction. We formulate the joining probability as a function of\nsocial influence from others, reflecting that individual choice behavior is\nlikely influenced by others. By controlling pedestrian influx and the social\ninfluence parameter, we identify various pedestrian flow patterns. For the\nbidirectional flow scenario, we observe a transition from the free flow phase\nto the freezing phase, in which oppositely walking pedestrians reach a complete\nstop and block each other. On the other hand, a different transition behavior\nappears in the unidirectional flow scenario, i.e., from the free flow phase to\nthe localized jam phase and then to the extended jam phase. It is also observed\nthat the extended jam phase can end up in freezing phenomena with a certain\nprobability when pedestrian flux is high with strong social influence. This\nstudy highlights that attractive interactions between pedestrians and an\nattraction can trigger jamming transitions by increasing the number of\nconflicts among pedestrians near the attraction. In order to avoid excessive\npedestrian jams, we suggest suppressing the number of conflicts under a certain\nlevel by moderating pedestrian influx especially when the social influence is\nstrong.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Planar growth generates scale free networks: In this paper we introduce a model of spatial network growth in which nodes\nare placed at randomly selected locations on a unit square in $\\mathbb{R}^2$,\nforming new connections to old nodes subject to the constraint that edges do\nnot cross. The resulting network has a power law degree distribution, high\nclustering and the small world property. We argue that these characteristics\nare a consequence of the two defining features of the network formation\nprocedure; growth and planarity conservation. We demonstrate that the model can\nbe understood as a variant of random Apollonian growth and further propose a\none parameter family of models with the Random Apollonian Network and the\nDeterministic Apollonian Network as extreme cases and our model as a midpoint\nbetween them. We then relax the planarity constraint by allowing edge crossings\nwith some probability and find a smooth crossover from power law to exponential\ndegree distributions when this probability is increased.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Contact-based model for epidemic spreading on temporal networks: We present a contact-based model to study the spreading of epidemics by means\nof extending the dynamic message passing approach to temporal networks. The\nshift in perspective from node- to edge-centric quantities enables accurate\nmodelling of Markovian susceptible-infected-recovered outbreaks on time-varying\ntrees, i.e., temporal networks with a loop-free underlying topology. On\narbitrary graphs, the proposed contact-based model incorporates potential\nstructural and temporal heterogeneities of the underlying contact network and\nimproves analytic estimations with respect to the individual-based\n(node-centric) approach at a low computational and conceptual cost. Within this\nnew framework, we derive an analytical expression for the epidemic threshold on\ntemporal networks and demonstrate the feasibility of this method on empirical\ndata.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The statistical relationship between product life cycle and repeat\n  purchase behavior in convenience stores: The density function of product life cycles in convenience stores is found to\nfollow the Weibull distribution. To clarify the parameters that determine these\nlife cycles, we introduce the conditional market share-defined as the\nprobability that a product is selected by customers only if it had been\npreviously purchased-and the market share without any conditions. The product\nlife cycle is more strongly correlated with the conditional market share of the\nproduct than with the latter type of market share.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Assessment of urban rooftop grid connected solar potential in Nepal,a\n  case study of residential buildings in Kathmandu, Pokhara and Biratnagar\n  cities: This paper assesses the technical, financial, and market potential of the\nrooftop Solar Photovoltaic (PV) system on residential buildings in major cities\nnamely Kathmandu valley, Pokhara, and Biratnagar of Nepal. Three sets of\nquestionnaires were prepared each for residential households, PV suppliers, and\nsolar project financing institutions. From the field survey, it is found that\nthe average rooftop area available for PV installation is 14.5 sq.m, 12.45\nsq.m, and 19 sq.m in Kathmandu, Pokhara, and Biratnagar cities respectively.\nConsidering 557,027 residential buildings in Kathmandu; 77,523 in Pokhara and\n33,075 in Biratnagar, total rooftop PV power potential in all three cities are\nfound to be 970 MWp which could generate 1,310 GWh/year that comes out to be\n35% of the electricity sold by Nepal Electricity Authority (NEA) in fiscal year\n2014/15. Based on the 1.5 kWp PV system design per household and market price\nof 2016, the Levelized Cost of Electricity (LCOE) changes from NRs 8/kWh to NRs\n20/kWh for basic lighting to a full load consisting of domestic electrical\nappliances. The technical barriers for the grid connection of rooftop solar in\nNepal are not a major issue now as Nepal Electricity Authority has set clear\nguidelines for it.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Googling Social Interactions: Web Search Engine Based Social Network\n  Construction: Social network analysis has long been an untiring topic of sociology.\nHowever, until the era of information technology, the availability of data,\nmainly collected by the traditional method of personal survey, was highly\nlimited and prevented large-scale analysis. Recently, the exploding amount of\nautomatically generated data has completely changed the pattern of research.\nFor instance, the enormous amount of data from so-called high-throughput\nbiological experiments has introduced a systematic or network viewpoint to\ntraditional biology. Then, is \"high-throughput\" sociological data generation\npossible? Google, which has become one of the most influential symbols of the\nnew Internet paradigm within the last ten years, might provide torrents of data\nsources for such study in this (now and forthcoming) digital era. We\ninvestigate social networks between people by extracting information on the Web\nand introduce new tools of analysis of such networks in the context of\nstatistical physics of complex systems or socio-physics. As a concrete and\nillustrative example, the members of the 109th United States Senate are\nanalyzed and it is demonstrated that the methods of construction and analysis\nare applicable to various other weighted networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Accelerating consensus on co-evolving networks: the effect of committed\n  individuals: Social networks are not static but rather constantly evolve in time. One of\nthe elements thought to drive the evolution of social network structure is\nhomophily - the need for individuals to connect with others who are similar to\nthem. In this paper, we study how the spread of a new opinion, idea, or\nbehavior on such a homophily-driven social network is affected by the changing\nnetwork structure. In particular, using simulations, we study a variant of the\nAxelrod model on a network with a homophilic rewiring rule imposed. First, we\nfind that the presence of homophilic rewiring within the network, in general,\nimpedes the reaching of consensus in opinion, as the time to reach consensus\ndiverges exponentially with network size $N$. We then investigate whether the\nintroduction of committed individuals who are rigid in their opinion on a\nparticular issue, can speed up the convergence to consensus on that issue. We\ndemonstrate that as committed agents are added, beyond a critical value of the\ncommitted fraction, the consensus time growth becomes logarithmic in network\nsize $N$. Furthermore, we show that slight changes in the interaction rule can\nproduce strikingly different results in the scaling behavior of $T_c$. However,\nthe benefit gained by introducing committed agents is qualitatively preserved\nacross all the interaction rules we consider.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Selected Topics of Social Physics: Equilibrium Systems: The present review is based on the lectures that the author had been giving\nduring several years at the Swiss Federal Institute of Technology in Zurich\n(ETH Zurich). Being bounded by lecture frames, the selection of the material,\nby necessity, is limited and is motivated by the author's research interests.\nThe paper gives an introduction to the physics of social systems, providing the\nmain definitions and notions used in the modeling of these systems. The\nbehavior of social systems is illustrated by several simple typical models. The\npresent part considers equilibrium systems. Nonequilibrium systems will be\npresented in the second part of the lectures. The style of the paper combines\nthe features of a tutorial and a survey, which, from one side, makes it easy to\nread for nonspecialists aiming at grasping the basics of social physics, and\nfrom the other side, describes several rather recent original models containing\nnew ideas that could be of interest to experienced researchers in the field.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Nonlinear eco-evolutionary games with global environmental fluctuations\n  and local environmental feedbacks: Environmental changes play a critical role in determining the evolution of\nsocial dilemmas in many natural or social systems. Generally, the environmental\nchanges include two prominent aspects: the global time-dependent fluctuations\nand the local strategy-dependent feedbacks. However, the impacts of these two\ntypes of environmental changes have only been studied separately, a complete\npicture of the environmental effects exerted by the combination of these two\naspects remains unclear. Here we develop a theoretical framework that\nintegrates group strategic behaviors with their general dynamic environments,\nwhere the global environmental fluctuations are associated with a nonlinear\nfactor in public goods game and the local environmental feedbacks are described\nby the `eco-evolutionary game'. We show how the coupled dynamics of local\ngame-environment evolution differs in static and dynamic global environments.\nIn particular, we find the emergence of cyclic evolutions of group cooperation\nand local environment, which forms an interior irregular loop in the phase\nplane, depending on the relative changing speed of both global and local\nenvironments compared to the strategic change. Our results provide important\ninsights toward how diverse evolutionary outcomes could emerge from the\nnonlinear interactions between strategies and the changing environments.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Multicommodity routing optimization for engineering networks: Optimizing passengers routes is crucial to design efficient transportation\nnetworks. Recent results show that optimal transport provides an efficient\nalternative to standard optimization methods. However, it is not yet clear if\nthis formalism has empirical validity on engineering networks. We address this\nissue by considering different response functions -- quantities determining the\ninteraction between passengers -- in the dynamics implementing the optimal\ntransport formulation. Particularly, we couple passengers' fluxes by taking\ntheir sum or the sum of their squares. The first choice naturally reflects\nedges occupancy in transportation networks, however the second guarantees\nconvergence to an optimal configuration of flows. Both modeling choices are\napplied to the Paris metro. We measure the extent of traffic bottlenecks and\ninfrastructure resilience to node removal, showing that the two settings are\nequivalent in the congested transport regime, but different in the branched\none. In the latter, the two formulations differ on how fluxes are distributed,\nwith one function favoring routes consolidation, thus potentially being prone\nto generate traffic overload. Additionally, we compare our method to Dijkstra's\nalgorithm to show its capacity to efficiently recover shortest-path-like\ngraphs. Finally, we observe that optimal transport networks lie in the Pareto\nfront drawn by the energy dissipated by passengers, and the cost to build the\ninfrastructure.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Method for Quantitative Estimation of the Risk Propagation Threshold in\n  Electric Power CPS Based on Seepage Probability: Because of the non-uniformity of the electric power CPS network and the\ndynamic nature of the risk propagation process, it is difficult to quantify the\ncritical point of a cyber risk explosion. From the perspective of the\ndependency network, this paper proposes a method for quantitative evaluation of\nthe risk propagation threshold of power CPS networks based on the percolation\ntheory. First, the power CPS network is abstracted as a dual-layered\nnetwork-directed unweighted graph according to topology correlation and\ncoupling logic, and the asymmetrical balls-into-bins allocation method is used\nto establish a \"one-to-many\" and \"partially coupled\" non-uniform power CPS\ncharacterization model. Then, considering the directionality between the cyber\nlayer and the physical layer link, the probability of percolation flow is\nintroduced to establish the propagation dynamic equations for the internal\ncoupling relationship of each layer. Finally, the risk propagation threshold is\nnumerically quantified by defining the survival function of power CPS network\nnodes, and the validity of the proposed method is verified by the IEEE 30-bus\nsystem and 150-node Barabsi-Albert model.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Hetero-functional Graph Resilience of the Future American Electric Grid: As climate change takes hold in the 21st century, it places an impetus to\ndecarbonize the American electric power system with renewable energy resources.\nThis paper presents a structural resilience analysis of the American electric\npower system that incrementally incorporates architectural changes including\nmeshed distribution lines, distributed generation, and energy storage\nsolutions. A hetero-functional graph analysis confirms our formal graph\nunderstandings from network science in terms of cumulative degree distributions\nand traditional attack vulnerability measures. Additionally, The paper shows\nthat hetero-functional graphs more precisely describe the changes in\nfunctionality associated with the addition of distributed generation and energy\nstorage. Finally, it demonstrates that the addition of all three types of\nmitigation measures enhance the grid's structural resilience; even in the\npresence of disruptive attacks. The paper concludes that there is no structural\ntrade-off between grid sustainability and resilience.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Where Should You Park Your Car? The $\\frac{1}{2}$ Rule: We investigate parking in a one-dimensional lot, where cars enter at a rate\n$\\lambda$ and each attempts to park close to a target at the origin. Parked\ncars also depart at rate 1. An entering driver cannot see beyond the parked\ncars for more desirable open spots. We analyze a class of strategies in which a\ndriver ignores open spots beyond $\\tau L$, where $\\tau$ is a risk threshold and\n$L$ is the location of the most distant parked car, and attempts to park at the\nfirst available spot encountered closer than $\\tau L$. When all drivers use\nthis strategy, the probability to park at the best available spot is maximal\nwhen $\\tau=\\frac{1}{2}$, and parking at the best available spot occurs with\nprobability $\\frac{1}{4}$.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Where to cut to delay a pandemic with minimum disruption? Mathematical\n  analysis based on the SIS model: We consider the problem of modifying a network topology in such a way as to\ndelay the propagation of a disease with minimal disruption of the network\ncapacity to reroute goods/items/passengers. We find an approximate solution to\nthe Susceptible-Infected-Susceptible (SIS) model, which constitutes a tight\nupper bound to its exact solution. This upper bound allows direct\nstructure-epidemic dynamic relations via the total communicability function.\nUsing this approach we propose a strategy to remove edges in a network that\nsignificantly delays the propagation of a disease across the network with\nminimal disruption of its capacity to deliver goods/items/passengers. We apply\nthis strategy to the analysis of the U.K. airport transportation network\nweighted by the number of passengers transported in the year 2003. We find that\nthe removal of all flights connecting four origin-destination pairs in the U.K.\ndelays the propagation of a disease by more than 300\\%, with a minimal\ndeterioration of the transportation capacity of this network. These time delays\nin the propagation of a disease represent an important non-pharmaceutical\nintervention to confront an epidemics, allowing for better preparations of the\nhealth systems, while keeping the economy moving with minimal disruptions.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Comparing electricity generation technologies based on multiple criteria\n  scores from an expert group: Multi criteria decision analysis (MCDA) has been used to provide a holistic\nevaluation of the quality of 13 electricity generation technologies in use\ntoday. A group of 19 energy experts cast scores on a scale of 1 to 10 using 12\nquality criteria, based around the pillars of sustainability (society,\nenvironment and economy), with the aim of quantifying each criterion for each\ntechnology. The total mean score is employed as a holistic measure of system\nquality. The top three technologies to emerge in rank order are nuclear,\ncombined cycle gas and hydroelectric. The bottom three are solar PV, biomass\nand tidal lagoon. All seven new renewable technologies fared badly, perceived\nto be expensive, unreliable, and not as environmentally friendly as is often\nassumed. We validate our approach by 1) comparing scores for pairs of criteria\nwhere we expect a correlation to exist; 2) comparing our qualitative scores\nwith quantitative data; and; 3) comparing our qualitative scores with NEEDS\nproject baseline costs. In many cases, R2>0.8 suggests that the structured\nhierarchy of our approach has led to scores that may be used in a\nsemi-quantitative way. Adopting the results of this survey would lead to a very\ndifferent set of energy policy priorities in the OECD and throughout the world.",
        "category": "physics_soc-ph"
    },
    {
        "text": "On Phenomenology of Complex Scientific Systems: Performance evolution of a number of complex scientific and technical systems\ndemonstrate exponential progress with time exp(+t/C) . The speed of progress C\n- a measure of difficulty and complexity - is analyzed for high energy\nelementary particle colliders, astrophysical searches for galaxies and\nexoplanets, protein structure determination and compared with computers and\nthermonuclear fusion reactors. An explanation of the characteristic exponential\nprogress is offered.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Accounting for spatiality of renewables and storage in transmission\n  planning: The current governance process to plan the German energy system omits two\noptions to substitute grid expansion: First, placing renewables closer to\ndemand instead of where site conditions are best. Second, utilizing storage\ninstead of additional transmission infrastructure to prevent grid congestion.\nIn the paper, we apply a comprehensive capacity expansion model based on the\nAnyMOD modelling framework to compare the status quo to alternative planning\napproaches for a fully renewable energy system. To represent spatiality and\nfluctuations of renewables, the German electricity sector is modelled with\ngreat spatio-temporal detail of 32 NUTS2 regions and hourly time-steps. In\naddition to the German electricity sector, analysis also accounts for exchange\nof energy with the rest of Europe and demand for electricity and\nelectricity-based fuels, like hydrogen or synthetic gases, from the industry,\ntransport, and heat sector.\n  The results reveal that a first-best solution can be well approximated if the\ncurrent planning approach also considered storage for congestion management.\nPlacing renewables different has no significant effect in our case, because the\navailable potential must be exploited almost entirely leaving little room for\noptimization. Furthermore, a sensitivity on the first-best scenario prohibiting\nadditional transmission lines entirely suggests that grid expansion can be\nsubstituted at tolerable costs.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Fragmenting networks by targeting collective influencers at a mesoscopic\n  level: A practical approach to protecting networks against epidemic processes such\nas spreading of infectious diseases, malware, and harmful viral information is\nto remove some influential nodes beforehand to fragment the network into small\ncomponents. Because determining the optimal order to remove nodes is a\ncomputationally hard problem, various approximate algorithms have been proposed\nto efficiently fragment networks by sequential node removal. Morone and Makse\nproposed an algorithm employing the non-backtracking matrix of given networks,\nwhich outperforms various existing algorithms. In fact, many empirical networks\nhave community structure, compromising the assumption of local tree-like\nstructure on which the original algorithm is based. We develop an immunization\nalgorithm by synergistically combining the Morone-Makse algorithm and coarse\ngraining of the network in which we regard a community as a supernode. In this\nway, we aim to identify nodes that connect different communities at a\nreasonable computational cost. The proposed algorithm works more efficiently\nthan the Morone-Makse and other algorithms on networks with community\nstructure.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Social Thermodynamics 2.0: Trailing the modern ideas of sociophysics, a minimalistic thermodynamic model\nof society is proposed, which consists of three social ingredients: people,\neconomy, and entropy. Employing the universal van der Waals equation of state,\nmany important relationships are discovered, including laws of econophysics. A\nparamount finding is that the Second Law of thermodynamics governs irreversible\nsocial evolution, revealing the power of social liberty and economic freedom.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Home-to-school pedestrian mobility GPS data from a citizen science\n  experiment in the Barcelona area: The analysis of pedestrian GPS datasets is fundamental to further advance on\nthe study and the design of walkable cities. The highest resolution GPS data\ncan characterize micro-mobility patterns and pedestrians' micro-motives in\nrelation to a small-scale urban context. Purposed-based recurrent mobility data\ninside people's neighborhoods is an important source in these sorts of studies.\nHowever, micro-mobility around people's homes is generally unavailable, and if\ndata exists, it is generally not shareable often due to privacy issues. Citizen\nscience and its public involvement practices in scientific research are valid\noptions to circumvent these challenges and provide meaningful datasets for\nwalkable cities. The study presents GPS records from single-day home-to-school\npedestrian mobility of 10 schools in the Barcelona Metropolitan area (Spain).\nThe research provides pedestrian mobility from an age-homogeneous group of\npeople. The study shares processed records with specific filtering, cleaning,\nand interpolation procedures that can facilitate and accelerate data usage.\nCitizen science practices during the whole research process are reported to\noffer a complete perspective of the data collected.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Scaling Law of Urban Ride Sharing: Sharing rides could drastically improve the efficiency of car and taxi\ntransportation. Unleashing such potential, however, requires understanding how\nurban parameters affect the fraction of individual trips that can be shared, a\nquantity that we call shareability. Using data on millions of taxi trips in New\nYork City, San Francisco, Singapore, and Vienna, we compute the shareability\ncurves for each city, and find that a natural rescaling collapses them onto a\nsingle, universal curve. We explain this scaling law theoretically with a\nsimple model that predicts the potential for ride sharing in any city, using a\nfew basic urban quantities and no adjustable parameters. Accurate\nextrapolations of this type will help planners, transportation companies, and\nsociety at large to shape a sustainable path for urban growth.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Publish or perish: analysis of scientific productivity using maximum\n  entropy principle and fluctuation-dissipation theorem: Using data retrieved from the INSPEC database we have quantitatively\ndiscussed a few syndromes of the publish-or-perish phenomenon, including\ncontinuous growth of rate of scientific productivity, and continuously\ndecreasing percentage of those scientists who stay in science for a long time.\nMaking use of the maximum entropy principle and fluctuation-dissipation\ntheorem, we have shown that the observed fat-tailed distributions of the total\nnumber of papers x authored by scientists may result from the density of states\nfunction g(x;\\tau) underlying scientific community. Although different\ngenerations of scientists are characterized by different productivity patterns,\nthe function g(x;\\tau) is inherent to researchers of a given seniority \\tau,\nwhereas the publish-or-perish phenomenon is caused only by an external field\n\\theta influencing researchers.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Optimal prevention strategies for chronic diseases in an compartmental\n  disease trajectory model: In countries with growing elderly populations, multimorbidity poses a\nsignificant healthcare challenge. The trajectories along which diseases\naccumulate as patients age and how they can be targeted by prevention efforts\nare still not fully understood. We propose a compartmental model, traditionally\nused in infectious diseases, describing chronic disease trajectories across 132\ndistinct multimorbidity patterns (compartments). Leveraging a comprehensive\ndataset from approximately 45 million hospital stays spanning 17 years in\nAustria, our compartmental disease trajectory model (CDTM) forecasts changes in\nthe incidence of 131 diagnostic groups and their combinations until 2030,\nhighlighting patterns involving hypertensive diseases with cardiovascular\ndiseases and metabolic disorders. We pinpoint specific diagnoses with the\ngreatest potential for preventive interventions to promote healthy aging.\nAccording to our model, a reduction of new onsets by 5% of hypertensive\ndiseases (I10-I15) leads to a reduction in all-cause mortality over a period of\n15 years by 0.57 (0.06)% and for malignant neoplasms (C00-C97) mortality is\nreduced by 0.57 (0.07)%. Furthermore, we use the model to assess the long-term\nconsequences of the Covid-19 pandemic on hospitalizations, revealing earlier\nand more frequent hospitalizations across multiple diagnoses. Our fully\ndata-driven approach identifies leverage points for proactive preparation by\nphysicians and policymakers to reduce the overall disease burden in the\npopulation, emphasizing a shift towards patient-centered care.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Flow stability for dynamic community detection: Many systems exhibit complex temporal dynamics due to the presence of\ndifferent processes taking place simultaneously. An important task in such\nsystems is to extract a simplified view of their time-dependent network of\ninteractions. Community detection in temporal networks usually relies on\naggregation over time windows or consider sequences of different stationary\nepochs. For dynamics-based methods, attempts to generalize static-network\nmethodologies also face the fundamental difficulty that a stationary state of\nthe dynamics does not always exist. Here, we derive a method based on a\ndynamical process evolving on the temporal network. Our method allows dynamics\nthat do not reach a steady state and uncovers two sets of communities for a\ngiven time interval that accounts for the ordering of edges in forward and\nbackward time. We show that our method provides a natural way to disentangle\nthe different dynamical scales present in a system with synthetic and\nreal-world examples.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Crossover transition in the Fluctuation of Internet: Gibrat's law predicts that the standard deviation of the growth rate of a\nnode's degree is constant. On the other hand, the preferential attachment(PA)\nindicates that such standard deviation decreases with initial degree as a power\nlaw of exponent $-0.5$. While both models have been applied to Internet\nmodeling, this inconsistency requires the verification of their validation.\nTherefore we empirically study the fluctuation of Internet of three different\ntime intervals(daily, monthly and yearly). We find a crossover transition from\nPA model to Gibrat's law, which has never been reported. Specifically\nGibrat-law starts from small degree region and extends gradually with the\nincrease of the observed period. We determine the validated periods for both\nmodels and find that the correlation between internal links has large\ncontribution to the emergence of Gibrat law. These findings indicate neither PA\nnor Gibrat law is applicable to the actual Internet, which requires a more\ncomplete model theory.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Wheel graph strategy for PEV localization of networks: Investigation of eigenvector localization properties of complex networks is\nnot only important for gaining insight into fundamental network problems such\nas network centrality measure, spectral partitioning, development of\napproximation algorithms, but also is crucial for understanding many real-world\nphenomena such as disease spreading, criticality in brain network dynamics. For\na network, an eigenvector is said to be localized when most of its components\ntake value near to zero, with a few components taking very high values. In this\narticle, we devise a methodology to construct a principal eigenvector (PEV)\nlocalized network from a given input network. The methodology relies on adding\na small component having a wheel graph to the given input network. By extensive\nnumerical simulation and an analytical formulation based on the largest\neigenvalue of the input network, we compute the size of the wheel graph\nrequired to localize the PEV of the combined network. Using the\nsusceptible-infected-susceptible model, we demonstrate the success of this\nmethod for various models and real-world networks consider as input networks.\nWe show that on such PEV localized networks, the disease gets localized within\na small region of the network structure before the outbreaks. The study is\nrelevant in controlling spreading processes on complex systems represented by\nnetworks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Studies of opinion stability for small dynamic networks with\n  opportunistic agents: There are numerous examples of societies with extremely stable mix of\ncontrasting opinions. We argue that this stability is a result of an interplay\nbetween society network topology adjustment and opinion changing processes. To\nsupport this position we present a computer model of opinion formation based on\nsome novel assumptions, designed to bring the model closer to social reality.\nIn our model, the agents, in addition to changing their opinions due to\ninfluence of the rest of society and external propaganda, have the ability to\nmodify their social network, forming links with agents sharing the same\nopinions and cutting the links with those they disagree with. To improve the\nmodel further we divide the agents into `fanatics' and `opportunists',\ndepending on how easy is to change their opinions. The simulations show\nsignificant differences compared to traditional models, where network links are\nstatic. In particular, for the dynamical model where inter-agent links are\nadjustable, the final network structure and opinion distribution is shown to\nresemble real world observations, such as social structures and persistence of\nminority groups even when most of the society is against them and the\npropaganda is strong.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Entropy of the Nordic electricity market: anomalous scaling, spikes, and\n  mean-reversion: The electricity market is a very peculiar market due to the large variety of\nphenomena that can affect the spot price. However, this market still shows many\ntypical features of other speculative (commodity) markets like, for instance,\ndata clustering and mean reversion. We apply the diffusion entropy analysis\n(DEA) to the Nordic spot electricity market (Nord Pool). We study the waiting\ntime statistics between consecutive spot price spikes and find it to show\nanomalous scaling characterized by a decaying power-law. The exponent observed\nin data follows a quite robust relationship with the one implied by the DEA\nanalysis. We also in terms of the DEA revisit topics like clustering,\nmean-reversion and periodicities. We finally propose a GARCH inspired model but\nfor the price itself. Models in the context of stochastic volatility processes\nappear under this scope to have a feasible description.",
        "category": "physics_soc-ph"
    },
    {
        "text": "How to choose the most appropriate centrality measure? A decision tree\n  approach: Centrality metrics play a crucial role in network analysis, while the choice\nof specific measures significantly influences the accuracy of conclusions as\neach measure represents a unique concept of node importance. Among over 400\nproposed indices, selecting the most suitable ones for specific applications\nremains a challenge. Existing approaches -- model-based, data-driven, and\naxiomatic -- have limitations, requiring association with models, training\ndatasets, or restrictive axioms for each specific application. To address this,\nwe introduce the culling method, which relies on the expert concept of\ncentrality behavior on simple graphs. The culling method involves forming a set\nof candidate measures, generating a list of as small graphs as possible needed\nto distinguish the measures from each other, constructing a decision-tree\nsurvey, and identifying the measure consistent with the expert's concept. We\napply this approach to a diverse set of 40 centralities, including novel\nkernel-based indices, and combine it with the axiomatic approach. Remarkably,\nonly 13 small 1-trees are sufficient to separate all 40 measures, even for\npairs of closely related ones. By adopting simple ordinal axioms like\nSelf-consistency or Bridge axiom, the set of measures can be drastically\nreduced making the culling survey short. Applying the culling method provides\ninsightful findings on some centrality indices, such as PageRank, Bridging, and\ndissimilarity-based Eigencentrality measures, among others. The proposed\napproach offers a cost-effective solution in terms of labor and time,\ncomplementing existing methods for measure selection, and providing deeper\ninsights into the underlying mechanisms of centrality measures.",
        "category": "physics_soc-ph"
    },
    {
        "text": "On Value at Risk for foreign exchange rates - the copula approach: The aim of this paper is to determine the Value at Risk (VaR) of the\nportfolio consisting of long positions in foreign currencies on an emerging\nmarket. Basing on empirical data we restrict ourselves to the case when the\ntail parts of distributions of logarithmic returns of these assets follow the\npower laws and the lower tail of associated copula C follows the power law of\ndegree 1. We will illustrate the practical usefulness of this approach by the\nanalysis of the exchange rates of EUR and CHF at the Polish forex market.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Synchronization Invariance Under Network Structural Transformations: Synchronization processes are ubiquitous despite the many connectivity\npatterns that complex systems can show. Usually, the emergence of synchrony is\na macroscopic observable, however, the microscopic details of the system, as\ne.g. the underlying network of interactions, is many times partially or totally\nunknown. We already know that different interaction structures can give rise to\na common functionality, understood as a common macroscopic observable. Building\nupon this fact, here, we propose network transformations that keep the\ncollective behavior of a large system of Kuramoto oscillators functionally\ninvariant. We derive a method based on information theory principles, that\nallows us to adjust the weights of the structural interactions to map random\nhomogeneous -in degree- networks into random heterogeneous networks and\nvice-versa, keeping synchronization values invariant. The results of the\nproposed transformations reveal an interesting principle; heterogeneous\nnetworks can be mapped to homogeneous ones with local information, but the\nreverse process needs to exploit higher-order information. The formalism\nprovides new analytical insight to tackle real complex scenarios when dealing\nwith uncertainty in the measurements of the underlying connectivity structure.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Optimization of transport protocols with path-length constraints in\n  complex networks: We propose a protocol optimization technique that is applicable to both\nweighted or unweighted graphs. Our aim is to explore by how much a small\nvariation around the Shortest Path or Optimal Path protocols can enhance\nprotocol performance. Such an optimization strategy can be necessary because\neven though some protocols can achieve very high traffic tolerance levels, this\nis commonly done by enlarging the path-lengths, which may jeopardize\nscalability. We use ideas borrowed from Extremal Optimization to guide our\nalgorithm, which proves to be an effective technique. Our method exploits the\ndegeneracy of the paths or their close-weight alternatives, which significantly\nimproves the scalability of the protocols in comparison to Shortest Paths or\nOptimal Paths protocols, keeping at the same time almost intact the length or\nweight of the paths. This characteristic ensures that the optimized routing\nprotocols are composed of paths that are quick to traverse, avoiding negative\neffects in data communication due to path-length increases that can become\nspecially relevant when information losses are present.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Can Partisan Voting Lead to Truth?: We study an extension of the voter model in which each agent is endowed with\nan innate preference for one of two states that we term as \"truth\" or\n\"falsehood\". Due to interactions with neighbors, an agent that innately prefers\ntruth can be persuaded to adopt a false opinion (and thus be discordant with\nits innate preference) or the agent can possess an internally concordant \"true\"\nopinion. Parallel states exist for agents that inherently prefer falsehood. We\ndetermine the conditions under which a population of such agents can ultimately\nreach a consensus for the truth, a consensus for falsehood, or reach an impasse\nwhere an agent tends to adopt the opinion that is in internal concordance with\nits innate preference so that consensus is never achieved.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Stochastic differential theory of cricket: A new formalism for analyzing the progression of cricket game using\nStochastic differential equation (SDE) is introduced. This theory enables a\nquantitative way of representing every team using three key variables which\nhave physical meaning associated with them. This is in contrast with the\ntraditional system of rating/ranking teams based on combination of different\nstatical cumulants. Further more, using this formalism, a new method to\ncalculate the winning probability as a progression of number of balls is given.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Impact of network structure on a model of diffusion and competitive\n  interaction: We consider a model in which agents of different species move over a complex\nnetwork, are subject to reproduction and compete for resources. The\ncomplementary roles of competition and diffusion produce a variety of fixed\npoints, whose stability depends on the structure of the underlying complex\nnetwork. The survival and death of species is influenced by the network degree\ndistribution, clustering, degree-degree correlations and community structures.\nWe found that the invasion of all the nodes by just one species is possible\nonly in Erdos-Renyi and regular graphs, while networks with scale--free degree\ndistribution, as those observed in real social, biological and technological\nsystems, guarantee the co-existence of different species and therefore help\nenhancing species diversity.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Scaling Phenomena in the Growth Dynamics of Scientific Output: We analyze a set of three databases at different levels of aggregation (i) a\ndatabase of approximately $10^6$ publications of 247 countries in the period\nbetween 1980--2001. (ii) A database of 508 academic institutions from European\nUnion (EU) and 408 institutes from USA in the 11 year period between during\n1991--2001. (iii) A database comprising of 2330 Flemish authors in the period\n1980--2000. At all levels of aggregation we find that the mean annual growth\nrates of publications is independent of the number of publications of the\nvarious units involved. We also find that the standard deviation of the\ndistribution of annual growth rates decays with the number of publications as a\npower law with exponent $\\approx 0.3$. These findings are consistent with those\nof recent studies of systems such as the size of R&D funding budgets of\ncountries, the research publication volumes of US universities, and the size of\nbusiness firms.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Is crime concentrated or are we simply using the wrong metrics?: Crime is highly concentrated in a few places, is committed by a few offenders\nand is suffered by a few victims. In recent decades, the concentration of crime\nhas become an accepted fact, yet, little is known in terms of how to measure\nthis concentration of crime such that the metric takes into account the fact\nthat crime has, in general, a low frequency, it fluctuates, it is highly\nconcentrated and has a certain degree of randomness. Here, the most frequently\nused metrics for concentration of crimes are reviewed. A null model with\ncomplete randomness is used for comparing between different concentration\nmetrics, which allows constructing a sensitivity analysis for every metric\nagainst varying crime rates. Results show that most ways of measuring the\nconcentration of crime are in fact, showing only that crime is rare or that it\nhas fluctuations, but fail to work as a method to compare the concentration of\ncrime between different regions, types of crime or across time.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Power-to-X in Energy Hubs: Operations and Policies Supporting the\n  Scale-Up of Renewable Fuel Production: Power-to-X (P2X) needs to scale up rapidly to provide the fuels required in\nthe hard-to-decarbonize industrial and heavy transport sector. Only recently,\nthe European Commission proposed requirements for \\textit{renewable} fuels. P2X\nenergy hubs enable efficient synergies between energy infrastructures,\nproduction facilities, and storage options. In this study, we explore the\noptimal operation of an energy hub by leveraging the flexibility of P2X\nincluding hydrogen, methanol, and ammonia synthesizers, and analyze potential\nrevenue streams such as the day-ahead and ancillary service markets. We propose\nEnerHub2X, a mixed-integer linear program that maximizes the hub's profit based\non current market prices, considering technical constraints of P2X such as unit\ncommitment and non-linear efficiencies. We model a representative Danish energy\nhub and find that without price incentives, it mainly produces liquid hydrogen\nand sells renewable electricity. Only by adding a price premium of about 50\\%\n(0.16 \\euro{}/kg) to the conventional fuel prices, sufficient amounts of\nrenewable ammonia and methanol are produced. To utilize production efficiently,\non-site renewable capacity and P2X must be carefully aligned. We show that\nrenewable power purchase agreements can provide flexibility while complying\nwith the rules set by the European Commission.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Robustness and fragility of the susceptible-infected-susceptible\n  epidemic models on complex networks: We analyze two alterations of the standard susceptible-infected-susceptible\n(SIS) dynamics that preserve the central properties of spontaneous healing and\ninfection capacity of a vertex increasing unlimitedly with its degree. All\nmodels have the same epidemic thresholds in mean-field theories but depending\non the network properties, simulations yield a dual scenario, in which the\nepidemic thresholds of the modified SIS models can be either dramatically\naltered or remain unchanged in comparison with the standard dynamics. For\nuncorrelated synthetic networks having a power-law degree distribution with\nexponent $\\gamma<5/2$, the SIS dynamics are robust exhibiting essentially the\nsame outcomes for all investigated models. A threshold in better agreement with\nthe heterogeneous rather than quenched mean-field theory is observed in the\nmodified dynamics for exponent $\\gamma>5/2$. Differences are more remarkable\nfor $\\gamma>3$ where a finite threshold is found in the modified models in\ncontrast with the vanishing threshold of the original one. This duality is\nelucidated in terms of epidemic lifespan on star graphs. We verify that the\nactivation of the modified SIS models is triggered in the innermost component\nof the network given by a $k$-core decomposition for $\\gamma<3$ while it\nhappens only for $\\gamma<5/2$ in the standard model. For $\\gamma>3$, the\nactivation in the modified dynamics is collective involving essentially the\nwhole network while it is triggered by hubs in the standard SIS. The duality\nalso appears in the finite-size scaling of the critical quantities where\nmean-field behaviors are observed for the modified, but not for the original\ndynamics. Our results feed the discussions about the most proper conceptions of\nepidemic models to describe real systems and the choices of the most suitable\ntheoretical approaches to deal with these models.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Sociophysics and the Forming of Public Opinion: Threshold versus Non\n  Threshold Dynamics: A two state model for opinion forming, which has proven heuristic power, is\nreviewed with a novel emphasis on the existence or absence of a threshold for\nthe dynamics. Monitored by repeated small groups discussions floater agents\nupdate their opinion according to a local majority rule. A threshold makes the\ninitial supports to flow towards eitheir one of two opposite attractors with\nonly one single opinion. While odd sizes yield a threshold at fifty percent\neven sizes, which allow the inclusion of doubt at an opinion tie, produces a\nthreshold shift toward either 0 or 1 giving rise to minority opinion spreading.\nConsidering heterogeneous agents like contrarians and inflexibles turn the\ndynamics threshold less beyond sone critical values. One unique attractor at\nfifty-fifty drives then the dynamics. In addition inflexibles can generate\nasymmetry and if one sided they erase the threshold ensuring the associated\nopinion to eventually gain the all population support. It may shed a new and\ncounter intuitive light on some social aspect of the global warming phenomenon.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Revealing the structure of the world airline network: Resilience of most critical infrastructures against failure of elements that\nappear insignificant is usually taken for granted. The World Airline Network\n(WAN) is an infrastructure that reduces the geographical gap between societies,\nboth small and large, and brings forth economic gains. With the extensive use\nof a publicly maintained data set that contains information about airports and\nalternative connections between these airports, we empirically reveal that the\nWAN is a redundant and resilient network for long distance air travel, but\notherwise breaks down completely due to removal of short and apparently\ninsignificant connections. These short range connections with moderate number\nof passengers and alternate flights are the connections that keep remote parts\nof the world accessible. It is surprising, insofar as there exists a highly\nresilient and strongly connected core consisting of a small fraction of\nairports (around 2.3%) together with an extremely fragile star-like periphery.\nYet, in spite of their relevance, more than 90% of the world airports are still\ninterconnected upon removal of this core. With standard and unconventional\nremoval measures we compare both empirical and topological perceptions for the\nfragmentation of the world. We identify how the WAN is organized into different\nclasses of clusters based on the physical proximity of airports and analyze the\nconsequence of this fragmentation.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Spectral solution of urn models for interacting particle systems: Using generating function methods for diagonalizing the transition matrix in\n2-Urn models, we provide a complete classification into solvable and unsolvable\nsubclasses, with further division of the solvable models into the Martingale\nand non-Martingale subcategories, and prove that the stationary distribution is\na Gaussian function in the latter. We also give a natural condition related to\nthe symmetry of the random walk in which the non-Martingale Urn models lead to\nan increase in entropy from Gaussian states. The condition also shows that\nuniversal symmetry in the macro-state is equivalent to increasing entropy.\nCertain models of social opinion dynamics, treated as Urn models, do not\nincrease in entropy, unlike isolated mechanical systems.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Finite-size scaling of human-population distributions over fixed-size\n  cells and its relation to fractal spatial structure: Using demographic data of high spatial resolution for a region in the south\nof Europe, we study the population over fixed-size spatial cells. We find that,\ncounterintuitively, the distribution of the number of inhabitants per cell\nincreases its variability when the size of the cells is increased.\nNevertheless, the shape of the distributions is kept constant, which allows us\nto introduce a scaling law, analogous to finite-size scaling, with a scaling\nfunction reasonably well fitted by a gamma distribution. This means that the\ndistribution of the number of inhabitants per cell is stable or invariant under\naddition with neighboring cells (plus rescaling), defying the central-limit\ntheorem, due to the obvious dependence of the random variables. The finite-size\nscaling implies a power-law relations between the moments of the distribution\nand its scale parameter, which are found to be related with the fractal\nproperties of the spatial pattern formed by the population. The match between\ntheoretical predictions and empirical results is reasonably good.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Lindy Effect: The Lindy effect is a statistical tendency for things with longer pasts\nbehind them to have longer futures ahead. It has been experimentally confirmed\nto apply to some categories, but not others, raising questions about when it is\napplicable and why. I shed some light on these questions by examining the\nmathematical properties required for the effect and generating mechanisms that\ncan produce them. While the Lindy effect is often thought to require a\ndeclining hazard rate, I show that it arises very naturally even in cases with\nconstant (or increasing) hazard rates -- so long as there is a probability\ndistribution over the size of that rate. One implication is that even things\nwhich are becoming less robust over time can display the Lindy effect.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Role of information in decision making of social agents: The influence of additional information on the decision making of agents, who\nare interacting members of a society, is analyzed within the mathematical\nframework based on the use of quantum probabilities. The introduction of social\ninteractions, which influence the decisions of individual agents, leads to a\ngeneralization of the quantum decision theory developed earlier by the authors\nfor separate individuals. The generalized approach is free of the standard\nparadoxes of classical decision theory. This approach also explains the\nerror-attenuation effects observed for the paradoxes occurring when decision\nmakers, who are members of a society, consult with each other, increasing in\nthis way the available mutual information. A precise correspondence between\nquantum decision theory and classical utility theory is formulated via the\nintroduction of an intermediate probabilistic version of utility theory of a\nnovel form, which obeys the requirement that zero-utility prospects should have\nzero probability weights.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Pushing the complexity barrier: diminishing returns in the sciences: Are the sciences not advancing at an ever increasing speed? We contrast this\npopular perspective with the view that science funding may actually see\ndiminishing returns, at least regarding established fields. In order to\nstimulate a larger discussion, we investigate two exemplary cases, the linear\nincrease in human life expectancy over the last 170 years and the advances in\nthe reliability of numerical short and medium term weather predictions during\nthe last 50 years. We argue that the outcome of science and technology (S&T)\nfunding in terms of measurable results is a highly sub-linear function of the\namount of resources committed. Supporting a range of small to medium size\nresearch projects, instead of a few large ones, will be, as a corollary, a more\nefficient use of resources for science funding agencies.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Friendship Paradox and Systematic Biases in Perceptions and Social\n  Norms: The \"friendship paradox\" (Feld1991) refers to the fact that, on average,\npeople have strictly fewer friends than their friends have. I show that this\nover-sampling of the most popular people amplifies behaviors that involve\ncomplementarities. People with more friends experience greater interactive\neffects and hence engage more in socially influenced activities. Given the\nfriendship paradox, people then perceive more engagement when sampling their\nfriends than exists in the overall population. Given the complementarities,\nthis feeds back to amplify average engagement. In addition, people with the\ngreatest innate benefits from a behavior also tend to be the ones who choose to\ninteract the most, leading to further feedback and amplification. These results\nare consistent with studies finding that people consistently overestimate peer\nconsumption of alcohol, cigarettes, and drugs; and, can help explain problems\nwith adolescent abuse of drugs and binge-drinking, as well as other behaviors.\nI also discuss how these results change in cases of strategic substitutes,\nwhere individuals overestimate free-riding by peers.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Building connections: How scientists meet each other during a conference: We present the results of two studies on how individuals interact with each\nother during a international, interdisciplinary scientific conference. We first\nshow that contact activity is highly variable across the two conferences and\nbetween different socio-demographic groups. However, we found one consistent\nphenomenon: Professors connect and interact significantly less than the other\nparticipants. We interpret this effect as non-tenured researchers using\nconferences to accumulate social capital, while established researchers already\nhave such capital. We then show that groups mix well during conferences, but\nnote that a language-based homophily is always present. Finally, we show that\nthe dynamics of the contacts across days is also similar between conferences.\nFirst day connections are established, then filtering occurs during the\nfollowing days. The connection turnover between consecutive days proves to be\nlarge ($\\sim 50 \\%$), and related to the intensity of interactions.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Agent-based Dynamics of a SPAHR Opioid Model on Social Network\n  Structures: Addiction epidemiology has been an active area of mathematical research in\nrecent years. However, the social and mental processes involved in substance\nuse disorders versus contraction of a pathogenic disease have presented\nchallenges to advancing the epidemiological theory of substance abuse,\nespecially within the context of the opioids where both prescriptions and\nsocial contagion have played a major role. In this paper, we utilize an\nagent-based modeling approach on social networks to further explore these\ndynamics. Using parameter estimation approaches, we compare our results to that\nof the Phillips et al. SPAHR model which was previously fit to data from the\nstate of Tennessee. Our results show that the average path length of a social\nnetwork has a strong relationship to social contagion dynamics for drug use\ninitiation, while other pathways to substance use disorder should not be\nconstrained to social network interactions that predate the individual's drug\nuse.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Stochastic dynamics and the predictability of big hits in online videos: The competition for the attention of users is a central element of the\nInternet. Crucial issues are the origin and predictability of big hits, the few\nitems that capture a big portion of the total attention. We address these\nissues analyzing 10 million time series of videos' views from YouTube. We find\nthat the average gain of views is linearly proportional to the number of views\na video already has, in agreement with usual rich-get-richer mechanisms and\nGibrat's law, but this fails to explain the prevalence of big hits. The reason\nis that the fluctuations around the average views are themselves heavy tailed.\nBased on these empirical observations, we propose a stochastic differential\nequation with L\\'evy noise as a model of the dynamics of videos. We show how\nthis model is substantially better in estimating the probability of an ordinary\nitem becoming a big hit, which is considerably underestimated in the\ntraditional proportional-growth models.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Triple Helix Indicators of Knowledge-Based Innovation Systems: When two selection environments operate upon each other, mutual shaping in a\nco-evolution along a particular trajectory is one possible outcome. When three\nselection environments are involved, more complex dynamics can be expected as a\nresult of interactions involving bi-lateral and tri-lateral relations. Three\nselection environments are specified in the Triple Helix model: (1) wealth\ngeneration (industry), (2) novelty production (academia), and (3) public\ncontrol (government). Furthermore, this model somewhat reduces the complexity\nby using university-industry-government relations for the specification of the\nhistorical conditions of the non-linear dynamics. One can no longer expect a\none-to-one correspondence between institutions and functions; a statistics is\nneeded for the evaluation of how, for how long, and to what extent\ninstitutional arrangements enhance synergies among different selection\nenvironments. The empirical contributions to this Triple Helix issue point in\nthe direction of \"rich ecologies\": the construction of careful balances between\ndifferentiation and integration among the three functions.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Local Symmetry and Global Structure in Adaptive Voter Models: Adaptive voter models (AVMs) are simple mechanistic systems that model the\nemergence of mesoscopic structure from local networked processes driven by\nconflict and homophily. AVMs display rich behavior, including a phase\ntransition from a fully-fragmented regime of \"echo-chambers\" to a regime of\npersistent disagreement governed by low-dimensional quasistable manifolds. Many\nextant methods for approximating the behavior of AVMs are either restricted in\nscope, expensive in computation, or inaccurate in predicting important\nstatistics. In this work, we develop a novel, second-order moment closure\napproximation method for binary-state rewire-to-random and rewire-to-same model\nvariants. We incorporate a small amount of noise via a random mutation term,\nwhich renders the system ergodic. Using ergodicity, we then approximate the\nvoting process, which is non-Markovian in the second moments of the system,\nwith a Markovian term near the phase transition. This approximation exploits an\nasymmetry between different classes of voting events. The resulting scheme\nenables us to predict the location of the phase transition and the active edge\ndensity in the regime of persistent disagreement, across the entire space of\nparameters and opinion densities. Numerically, our results are nearly exact for\nthe rewire-to-random model, and competitive with other current approaches for\nthe rewire-to-same model. Moreover, our computations display constant scaling\nin the mean degree, enabling approximations for denser systems than previously\npossible. We conclude with suggestions for model refinements and extensions.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Corporate competition: A self-organized network: A substantial number of studies have extended the work on universal\nproperties in physical systems to complex networks in social, biological, and\ntechnological systems. In this paper, we present a complex networks perspective\non interfirm organizational networks by mapping, analyzing and modeling the\nspatial structure of a large interfirm competition network across a variety of\nsectors and industries within the United States. We propose two micro-dynamic\nmodels that are able to reproduce empirically observed characteristics of\ncompetition networks as a natural outcome of a minimal set of general\nmechanisms governing the formation of competition networks. Both models, which\nutilize different approaches yet apply common principles to network formation\ngive comparable results. There is an asymmetry between companies that are\nconsidered competitors, and companies that consider others as their\ncompetitors. All companies only consider a small number of other companies as\ncompetitors; however, there are a few companies that are considered as\ncompetitors by many others. Geographically, the density of corporate\nheadquarters strongly correlates with local population density, and the\nprobability two firms are competitors declines with geographic distance. We\nconstruct these properties by growing a corporate network with competitive\nlinks using random incorporations modulated by population density and\ngeographic distance. Our new analysis, methodology and empirical results are\nrelevant to various phenomena of social and market behavior, and have\nimplications to research fields such as economic geography, economic sociology,\nand regional economic development.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Weighted Hypernetworks: Complex network theory has been used to study complex systems. However, many\nreal-life systems involve multiple kinds of objects . They can't be described\nby simple graphs. In order to provide complete information of these systems, we\nextend the concept of evolving models of complex networks to hypernetworks. In\nthis work, we firstly propose a non-uniform hypernetwork model with\nattractiveness, and obtain the stationary average hyperdegree distribution of\nthe non-uniform hypernetwork. Furthermore, we develop a model for weighted\nhypernetworks that couples the establishment of new hyperedges and nodes and\nthe weights' dynamical evolution. We obtain the stationary average hyperdegree\nand hyperstrength distribution by using the hyperdegree distribution of the\nhypernetwork model with attractiveness, respectively. In particular, the model\nyields a nontrivial time evolution of nodes' properties and scale-free behavior\nfor the hyperdegree and hyperstrength distribution. It is expected that our\nwork may give help to the study of the hypernetworks in real-world systems.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Adult-child pairs walking down stairs: Empirical analysis and\n  optimal-step-based modeling of a complex pedestrian flow, with an exploration\n  of flow-improvement strategies: Pedestrian egress from training schools in the after-class period (especially\nin China, as children walk down stairs together with their parents) raises\npractical concerns related to degraded flow conditions and possible safety\nhazards, but also represents a challenge to mainstream modeling approaches for\nseveral reasons: they involve heterogeneous groups (adult-child pairs), which\nare hardly studied compared to their homogeneous counterparts, in a complex\ngeometry, made of staircases connected by a platform where pedestrians rotate,\nand over a wide range of densities. In light of our field observations at a\ntraining school in China, we develop a semi-continuous model which\nquantitatively reproduces the collective dynamics observed empirically and\nenables us to assess some guidance strategies to improve egress efficiency. In\nthis model, which extends the optimal step approach, adults and children are\ndescribed as ellipses with prescribed relative positions that evolve by\nsuccessive steps handled by an event-driven algorithm, along a spontaneous\nsemi-circular path on the platform, but may deviate from it in crowded\nconditions by selecting their preferred next positions. In line with the\nobservations, most pairs walk side by side overall and only a small fraction\nsometimes make a detour to avoid queuing.Turning to the guidance strategies, we\nfind that promoting front-back pairing may increase the flow, while urging\npeople to make more detours may be counterproductive. Perhaps even more\nrelevantly, the intuitive measure consisting in desynchronizing the flows from\nthe different floors by shifting the ends of classes succeeds in reducing the\negress time by at least 10%.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Co-occurrence simplicial complexes in mathematics: identifying the holes\n  of knowledge: In the last years complex networks tools contributed to provide insights on\nthe structure of research, through the study of collaboration, citation and\nco-occurrence networks. The network approach focuses on pairwise relationships,\noften compressing multidimensional data structures and inevitably losing\ninformation. In this paper we propose for the first time a simplicial complex\napproach to word co-occurrences, providing a natural framework for the study of\nhigher-order relations in the space of scientific knowledge. Using topological\nmethods we explore the conceptual landscape of mathematical research, focusing\non homological holes, regions with low connectivity in the simplicial\nstructure. We find that homological holes are ubiquitous, which suggests that\nthey capture some essential feature of research practice in mathematics. Holes\ndie when a subset of their concepts appear in the same article, hence their\ndeath may be a sign of the creation of new knowledge, as we show with some\nexamples. We find a positive relation between the dimension of a hole and the\ntime it takes to be closed: larger holes may represent potential for important\nadvances in the field because they separate conceptually distant areas. We also\nshow that authors' conceptual entropy is positively related with their\ncontribution to homological holes, suggesting that polymaths tend to be on the\nfrontier of research.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Small world yields the most effective information spreading: Spreading dynamics of information and diseases are usually analyzed by using\na unified framework and analogous models. In this paper, we propose a model to\nemphasize the essential difference between information spreading and epidemic\nspreading, where the memory effects, the social reinforcement and the\nnon-redundancy of contacts are taken into account. Under certain conditions,\nthe information spreads faster and broader in regular networks than in random\nnetworks, which to some extent supports the recent experimental observation of\nspreading in online society [D. Centola, Science {\\bf 329}, 1194 (2010)]. At\nthe same time, simulation result indicates that the random networks tend to be\nfavorable for effective spreading when the network size increases. This\nchallenges the validity of the above-mentioned experiment for large-scale\nsystems. More significantly, we show that the spreading effectiveness can be\nsharply enhanced by introducing a little randomness into the regular structure,\nnamely the small-world networks yield the most effective information spreading.\nOur work provides insights to the understanding of the role of local clustering\nin information spreading.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Double Modelling of the Dynamic of Activities in Rural Municipalities: Land use choices and activity prevalence in a selected territory are\ndetermined by individual preferences constrained by the characteristic of the\nanalysed zone: population density, soil properties, urbanization level and\nother similar factors can drive individuals to make different kind of decisions\nabout their occupations. Different approaches can be used to describe land use\nchange, occupation prevalence and their reciprocal inter-relation. In this\npaper we describe two different kinds of approaches: an agent based model,\ncentred on individual choices and an aggregated model describing the evolution\nof activity prevalence in terms of coupled differential equation. We use and we\ncompare the two models to analyse the effect of territorial constraints, like\nthe lack of employment in determined sectors, on the possible activity\nprevalence scenarios.",
        "category": "physics_soc-ph"
    },
    {
        "text": "An edge density definition of overlapping and weighted graph communities: Community detection in networks refers to the process of seeking strongly\ninternally connected groups of nodes which are weakly externally connected. In\nthis work, we introduce and study a community definition based on internal edge\ndensity. Beginning with the simple concept that edge density equals number of\nedges divided by maximal number of edges, we apply this definition to a variety\nof node and community arrangements to show that our definition yields sensible\nresults. Our community definition is equivalent to that of the Absolute Potts\nModel community detection method (Phys. Rev. E 81, 046114 (2010)), and the\nperformance of that method validates the usefulness of our definition across a\nwide variety of network types. We discuss how this definition can be extended\nto weighted, and multigraphs, and how the definition is capable of handling\noverlapping communities and local algorithms. We further validate our\ndefinition against the recently proposed Affiliation Graph Model\n(arXiv:1205.6228 [cs.SI]) and show that we can precisely solve these\nbenchmarks. More than proposing an end-all community definition, we explain how\nstudying the detailed properties of community definitions is important in order\nto validate that definitions do not have negative analytic properties. We urge\nthat community definitions be separated from community detection algorithms and\npropose that community definitions be further evaluated by criteria such as\nthese.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Spontaneous wave formation in stochastic self-driven particle systems: Waves and oscillations are commonly observed in the dynamics of self-driven\nagents such as pedestrians or vehicles. Interestingly, many factors may perturb\nthe stability of space homogeneous streaming, leading to the spontaneous\nformation of collective oscillations of the agents related to stop-and-go\nwaves, jamiton, or phantom jam in the literature. In this article, we\ndemonstrate that even a minimal additive stochastic noise in stable first-order\ndynamics can initiate stop-and-go phenomena. The noise is not a classic white\none, but a colored noise described by a Gaussian Ornstein-Uhlenbeck process. It\nturns out that the joint dynamics of particles and noises forms again a\n(Gaussian) Ornstein-Uhlenbeck process whose characteristics can be explicitly\nexpressed in terms of parameters of the model. We analyze its stability and\ncharacterize the presence of waves through oscillation patterns in the\ncorrelation and autocorrelation of the distance spacing between the particles.\nWe determine exact solutions for the correlation functions for the finite\nsystem with periodic boundaries and in the continuum limit when the system size\nis infinite. Finally, we compare experimental trajectories of single-file\npedestrian motions to simulation results.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Non-Poisson donation behaviors in virtual worlds: Similar to charitable giving in real world, donation behaviors play an\nimportant role in the complex interactions among individuals in virtual worlds.\nHowever, it is not clear if the donation process is random or not. We\ninvestigate this problem using detailed data from parallel virtual worlds\nadhered to a massively multiplayer online role-playing game. We find that the\ninter-donation durations follow power-law-tailed distributions distributed with\nan average tail exponent close to 1.91, have strong long-range correlations,\nand possess multifractal features. These findings indicate that the donation\nprocess is non-Poissonian, which has potential worth in modeling the\ncomplicated individuals behaviors in virtual worlds.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Exploring the Relation between NPP-VIIRS Nighttime Lights and Carbon\n  Footprint, Population Growth, and Energy Consumption in the UAE: Due to global warming and its detrimental effect, every country is\nresponsible to join the global effort to reduce carbon emissions. In order to\nimprove the mitigation plan of climate change, accurate es-timates of carbon\nemissions, population, and electricity consumption are critical. Carbon\nfootprint is significantly linked to the socioeconomic development of the\ncountry which can be reflected in the city's infrastructure and urbanization.\nWe may be able to estimate the carbon footprint, population growth, and\nelectricity consumption of a city by observing the nighttime light reflecting\nits urbanization. This is more challenging in oil-producing countries where\nurbanization can be more complicated. In this study, we are therefore\ninvestigating the possibility of correlating the remotely sensed NPP-VIIRS\nNighttime light (NTL) estimation with the aforementioned socioeconomic\nindicators. Daily NPP-VIIRS NTL were obtained for the period between 2012 to\n2021 for the United Arab Emirates (UAE) which is one of the top oil producing\ncountries. The socioeconomic indicators of the UAE, including the population,\nelectricity consumption, and carbon dioxide emissions, have been obtained for\nthe same period. The analysis of the correlation between the NTLs and the\npopulation indicates that there is a high correlation of more than 0.9. There\nis also a very good correlation of 0.7 between NTLs and carbon emissions and\nelectricity consumption. However, these correlations differ from one city to\nanother. For example, Dubai has shown the highest correlation between\npopulation and NTLs (R2 > 0.8). However, the correlation was the lowest in\nAl-Ain, a rural city (R2 < 0.4) with maximum electricity consumption of 1.1E04\nGWh. These results demonstrate that NTLs can be considered as a promising proxy\nfor carbon footprint and urbanization in oil-producing regions.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Temporal Trends of Intraurban Commuting in Baton Rouge 1990-2010: Based on the 1990-2010 CTPP data in Baton Rouge, this research analyzes the\ntemporal trends of commuting patterns in both time and distance. In comparison\nto previous work, commuting length is calibrated more accurately by Monte Carlo\nbased simulation of individual journey-to-work trips to mitigate the zonal\neffect. First, average commute distance kept climbing in 1990-2010 while\naverage commute time increased in 1990-2000 but then slightly dropped toward\n2010. Secondly, urban land use remained a good predictor of commuting pattern\nover time (e.g., explaining up to 90% of mean commute distance and about 30% of\nmean commute time). Finally, the percentage of excess commuting increased\nsignificantly in 1990-2000 and stabilized afterwards.",
        "category": "physics_soc-ph"
    },
    {
        "text": "God ($\\equiv Elohim$), the first small world network: In this paper, the approach of network mapping of words in literary texts is\nextended to ''textual factors'': the network nodes are defined as ''concepts'';\nthe links are ''community connexions''. Thereafter, the text network properties\nare investigated along modern statistical physics approaches of networks,\nthereby relating network topology and algebraic properties, to literary texts\ncontents. As a practical illustration, the first chapter of the Genesis in the\nBible is mapped into a 10 node network, as in the Kabbalah approach, mentioning\nGod ($\\equiv Elohim$). The characteristics of the network are studied starting\nfrom its adjacency matrix, and the corresponding Laplacian matrix. Triplets of\nnodes are particularly examined in order to emphasize the ''textual (community)\nconnexions'' of each agent \"emanation\", through the so called clustering\ncoefficients and the overlap index, whence measuring the ''semantic flow''\nbetween the different nodes. It is concluded that this graph is a small-world\nnetwork, weakly dis-assortative, because its average local clustering\ncoefficient is significantly higher than a random graph constructed on the same\nvertex set.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Best reply structure and equilibrium convergence in generic games: Game theory is widely used as a behavioral model for strategic interactions\nin biology and social science. It is common practice to assume that players\nquickly converge to an equilibrium, e.g. a Nash equilibrium. This can be\nstudied in terms of best reply dynamics, in which each player myopically uses\nthe best response to her opponent's last move. Existing research shows that\nconvergence can be problematic when there are best reply cycles. Here we\ncalculate how typical this is by studying the space of all possible two-player\nnormal form games and counting the frequency of best reply cycles. The two key\nparameters are the number of moves, which defines how complicated the game is,\nand the anti-correlation of the payoffs, which determines how competitive it\nis. We find that as games get more complicated and more competitive, best reply\ncycles become dominant. The existence of best reply cycles predicts\nnon-convergence of six different learning algorithms that have support from\nhuman experiments. Our results imply that for complicated and competitive games\nequilibrium is typically an unrealistic assumption. Alternatively, if for some\nreason \"real\" games are special and do not possess cycles, we raise the\ninteresting question of why this should be so.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A generalised model for asymptotically-scale-free geographical networks: We consider a generalised d-dimensional model for asymptotically-scale-free\ngeographical networks. Central to many networks of this kind, when considering\ntheir growth in time, is the attachment rule, i.e. the probability that a new\nnode is attached to one (or more) preexistent nodes. In order to be more\nrealistic, a fitness parameter $\\eta_i \\in [0,1]$ for each node $i$ of the\nnetwork is also taken into account to reflect the ability of the nodes to\nattract new ones. Our d-dimensional model takes into account the geographical\ndistances between nodes, with different probability distribution for $\\eta$\nwhich sensibly modifies the growth dynamics. The preferential attachment rule\nis assumed to be $\\Pi_i\\propto k_i \\eta_i r_{ij}^{-\\alpha_A} $ where $k_i$ is\nthe connectivity of the $i$th pre-existing site and $\\alpha_A$ characterizes\nthe importance of the euclidean distance r for the network growth. For special\nvalues of the parameters, this model recovers respectively the\nBianconi-Barab\\'{a}si and the Barab\\'{a}si-Albert ones. The present generalised\nmodel is asymptotically scale-free in all cases, and its degree distribution is\nvery well fitted with q-exponential distributions, which optimise the\nnonadditive entropy $S_q$, given by $p(k) \\propto e_q^{-k/\\kappa} \\equiv\n1/[1+(q-1)k/\\kappa]^{1/(q-1)}$, with $(q,\\kappa)$ depending uniquely only on\nthe ratio $\\alpha_A/d$ and the fitness distribution. Hence this model\nconstitutes a realization of asymptotically-scale-free geographical networks\nwithin nonextensive statistical mechanics, where $k$ plays the role of energy\nand $\\kappa$ plays the role of temperature. General scaling laws are also found\nfor q as a function of the parameters of the model.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Stochastic Nonlinear Dynamics of Interpersonal and Romantic\n  Relationships: Current theories from biosocial (e.g.: the role of neurotransmitters in\nbehavioral features), ecological (e.g.: cultural, political, and institutional\nconditions), and interpersonal (e.g.: attachment) perspectives have grounded\ninterpersonal and romantic relationships in normative social experiences.\nHowever, these theories have not been developed to the point of providing a\nsolid theoretical understanding of the dynamics present in interpersonal and\nromantic relationships, and integrative theories are still lacking. In this\npaper, mathematical models are use to investigate the dynamics of interpersonal\nand romantic relationships, which are examined via ordinary and stochastic\ndifferential equations, in order to provide insight into the behaviors of love.\nThe analysis starts with a deterministic model and progresses to nonlinear\nstochastic models capturing the stochastic rates and factors (e.g.: ecological\nfactors, such as historical, cultural and community conditions) that affect\nproximal experiences and shape the patterns of relationship. Numerical examples\nare given to illustrate various dynamics of interpersonal and romantic\nbehaviors (with emphasis placed on sustained oscillations, and transitions\nbetween locally stable equilibria) that are observable in stochastic models\n(closely related to real interpersonal dynamics), but absent in deterministic\nmodels.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A weighted network evolution model based on passenger behavior: This paper presents an evolution model of weighted networks in which the\nstructural growth and weight dynamics are driven by human behavior, i.e.\npassenger route choice behavior. Transportation networks grow due to people's\nincreasing travel demand and the pattern of growth is determined by their route\nchoice behavior. In airline networks passengers often transfer from a third\nairport instead of flying directly to the destination, which contributes to the\nhubs formation and finally the scale-free statistical property. In this model\nwe assume at each time step there emerges a new node with m travel\ndestinations. Then the new node either connects destination directly with the\nprobability p or transfers from a third node with the probability 1-p. The\nanalytical result shows degree and strength both obey power-law distribution\nwith the exponent between 2.33 and 3 depending on p. The weights also obey\npower-law distribution. The clustering coefficient, degree assortatively\ncoefficient and degree-strength correlation are all dependent on the\nprobability p. This model can also be used in social networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Measuring Political Polarization: Twitter shows the two sides of\n  Venezuela: We say that a population is perfectly polarized when divided in two groups of\nthe same size and opposite opinions. In this paper, we propose a methodology to\nstudy and measure the emergence of polarization from social interactions. We\nbegin by proposing a model to estimate opinions in which a minority of\ninfluential individuals propagate their opinions through a social network. The\nresult of the model is an opinion probability density function. Next, we\npropose an index to quantify the extent to which the resulting distribution is\npolarized. Finally, we apply the proposed methodology to a Twitter conversation\nabout the late Venezuelan president, Hugo Ch\\'avez, finding a good agreement\nbetween our results and offline data. Hence, we show that our methodology can\ndetect different degrees of polarization, depending on the structure of the\nnetwork.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Metrics for Quantifying Shareability in Transportation Networks: The\n  Maximum Network Flow Overlap Problem: Cities around the world vary in terms of their transportation networks and\ntravel demand patterns; these variations affect the viability of shared\nmobility services. This study proposes metrics to quantify the shareability of\nperson-trips in a city, as a function of two inputs--the road network structure\nand origin-destination (OD) travel demand. The study first conceptualizes a\nfundamental shareability unit, 'flow overlap'. Flow overlap denotes, for a\nperson-trip traversing a given path, the weighted (by link distance) average\nnumber of other trips sharing the links along the original person's path. The\nstudy extends this concept to the network level and formulates the Maximum\nNetwork Flow Overlap Problem (MNFLOP) to assign all OD trips to paths that\nmaximize network-wide flow overlap. The study utilizes the MNFLOP output to\ncalculate metrics of shareability at various levels of aggregation: person-trip\nlevel, OD level, origin or destination level, network level, and link level.\nThe study applies the MNFLOP and associated shareability metrics to different\nOD demand scenarios in the Sioux Falls network. The computational results\nverify that (i) MNFLOP assigns person-trips to paths such that flow overlaps\nsignificantly increase relative to shortest path assignment, (ii) MNFLOP and\nits associated shareability metrics can meaningfully differentiate between\ndifferent OD trip matrices in terms of shareability, and (iii) an MNFLOP-based\nmetric can quantify demand dispersion--a metric of the directionality of\ndemand--in addition to the magnitude of demand, for trips originating or\nterminating from a single node in the network. The paper also includes an\nextensive discussion of potential future uses of the MNFLOP and its associated\nshareability metrics.",
        "category": "physics_soc-ph"
    },
    {
        "text": "$p$-th Clustering coefficients and $q$-$th$ degrees of separation based\n  on String-Adjacent Formulation: The phenomenon of six degrees of separation is an old but attractive subject.\nThe deep understanding has been uncovered yet, especially how closed paths\nincluded in a network affect six degrees of separation are an important subject\nleft yet. For it, some researches have been made\\cite{Newm21}, \\cite{Aoyama}.\n  Recently we have develop a formalism \\cite{Toyota3},\\cite{Toyota4} to explore\nthe subject based on the string formalism developed by Aoyama\\cite{Aoyama}. The\nformalism can systematically investigate the effect of closed paths, especially\ngeneralized clustering coefficient $C_{(p)}$ introduced in \\cite{Toyota4}, on\nsix degrees of separation. In this article, we analyze general $q$-th degrees\nof separation by using the formalism developed by us. So we find that the scale\nfree network with exponent $\\gamma=3$ just display six degrees of separation.\nFurthermore we drive a phenomenological relation between the separation number\n$q$ and $C_{(p)}$ that has crucial information on circle structures in\nnetworks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "How relevant is the predictive power of the h-index? A case study of the\n  time-dependent Hirsch index: The h-index has been shown to have predictive power. Here I report results of\nan empirical study showing that the increase of the h-index with time often\ndepends for a long time on citations to rather old publications. This inert\nbehavior of the h-index means that it is difficult to use it as a measure for\npredicting future scientific output.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Random acyclic networks: Directed acyclic graphs are a fundamental class of networks that includes\ncitation networks, food webs, and family trees, among others. Here we define a\nrandom graph model for directed acyclic graphs and give solutions for a number\nof the model's properties, including connection probabilities and component\nsizes, as well as a fast algorithm for simulating the model on a computer. We\ncompare the predictions of the model to a real-world network of citations\nbetween physics papers and find surprisingly good agreement, suggesting that\nthe structure of the real network may be quite well described by the random\ngraph.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The fundamental Diagram of Pedestrian Model with Slow Reaction: The slow-to-start models are a classical cellular automata model in\nsimulating vehicle traffic. However, to our knowledge, the slow-to-start effect\nhas not considered in modeling pedestrian dynamic. We verify the similar\nbehavior between pedestrian and vehicle, and propose an new lattice gas (LG)\nmodel called the slow reaction (SR) model to describe the pedestrian's delayed\nreaction in single-file movement. We simulate and reproduce the Seyfried's\nfield experiments at the research centre Julich, and use its empirical data to\nvalidate our SR model. We compare the SR model with the standard LG model. We\ntest different probability of slow reaction ps in SR model and found the\nsimulation data of ps=0.3 fit the empirical data best. The RMS error of mean\nvelocity of SR model is smaller than that of standard LG model. In the range of\nps=0.1~0.3, our fundamental diagram between velocity and density by simulation\ncoincides with field experiments. The distribution of individual velocity in\nfundamental diagram in SR model agrees with the empirical data better than that\nof standard LG model. In addition, we observe the stop-and-go waves and phase\nseparation in pedestrian flow by simulation. We reproduced the phenomena of\nuneven distribution of interspaces by SR model while the standard LG model did\nnot implement. The SR model can reproduce the evolution of spatio-temporal\nstructures of pedestrian flow with higher fidelity to Seyfried's experiments\nthan the standard LG model.",
        "category": "physics_soc-ph"
    },
    {
        "text": "An Analytical Framework for Understanding the Intensity of Religious\n  Fundamentalism: This paper examines the process of emergence of religious fundamentalism\nthrough development parameters. Therefore this research work reflects an\nanalytical discussion on how the level of religious fundamentalism can be\nexplained by the economic, political administrative and legal parameters such\nas GDP, Employment to Population ratio, Government Effectiveness, Voice &\nAccountability, Rule of Law (World Justice Project Report) and Rule of law\n(Governance Indicators).",
        "category": "physics_soc-ph"
    },
    {
        "text": "An informational approach to uncover the age group interactions in\n  epidemic spreading from macro analysis: We investigate the use of transfer entropy (TE) as a proxy to detect the\ncontact patterns of the population in epidemic processes. We first apply the\nmeasure to a classical age-stratified SIR model and observe that the recovered\npatterns are consistent with the age-mixing matrix that encodes the interaction\nof the population. We then apply the TE analysis to real data from the COVID-19\npandemic in Spain and show that it can provide information on how the behavior\nof individuals changed through time. We also demonstrate how the underlying\ndynamics of the process allow us to build a coarse-grained representation of\nthe time series that provides more information than raw time series. The\nmacro-level representation is a more effective scale for analysis, which is an\ninteresting result within the context of causal analysis across different\nscales. These results open the path for more research on the potential use of\ninformational approaches to extract retrospective information on how\nindividuals change and adapt their behavior during a pandemic, which is\nessential for devising adequate strategies for an efficient control of the\nspreading.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Growing multiplex networks: We propose a modeling framework for growing multiplexes where a node can\nbelong to different networks. We define new measures for multiplexes and we\nidentify a number of relevant ingredients for modeling their evolution such as\nthe coupling between the different layers and the arrival time distribution of\nnodes. The topology of the multiplex changes significantly in the different\ncases under consideration, with effects of the arrival time of nodes on the\ndegree distribution, average shortest paths and interdependence.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Mitigating Emergency Department Crowding With Stochastic Population\n  Models: Environments such as shopping malls, airports, or hospital emergency\ndepartments often experience crowding, with many people simultaneously\nrequesting service. Crowding is highly noisy, with sudden overcrowding\n\"spikes\". Past research has either focused on average behavior or used\ncontext-specific non-generalizable models. Here we show that a stochastic\npopulation model, previously applied to a broad range of natural phenomena, can\naptly describe hospital emergency-department crowding, using data from\nfive-year minute-by-minute emergency-department records. The model provides\nreliable forecasting of the crowding distribution. Overcrowding is highly\nsensitive to the patient arrival-flux and length-of-stay: a 10% increase in\narrivals triples the probability of overcrowding events. Expediting patient\nexit-rate to shorten the typical length-of-stay by just 20 minutes (8.5%)\nreduces severe overcrowding events by 50%. Such forecasting is crucial in\nprevention and mitigation of breakdown events. Our results demonstrate that\ndespite its high volatility, crowding follows a dynamic behavior common to many\nnatural systems.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Citations and the Zipf-Mandelbrot's law: A curious observation was made that the rank statistics of scientific\ncitation numbers follows Zipf-Mandelbrot's law. The same pow-like behavior is\nexhibited by some simple random citation models. The observed regularity\nindicates not so much the peculiar character of the underlying (complex)\nprocess, but more likely, than it is usually assumed, its more stochastic\nnature.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Two Faces of American Power: Military and Political Communication\n  during the Cuban Missile Crisis: Purpose: The mismatches between political discourse and military momentum in\nthe American handling of the Cuban missile crisis are explained by using the\nmodel of the potential autopoiesis of subsystems. Under wartime conditions, the\ncodes of political and military communications can increasingly be\ndifferentiated. Design/methodology/approach: The model of a further\ndifferentiation between political and military power is developed on the basis\nof a detailed description of the Cuban missile crisis. We introduce the concept\nof a \"semi-dormant autopoiesis\" for the difference in the dynamics between\npeacetime and wartime conditions. Findings: Several dangerous incidents during\nthe crisis can be explained by a sociocybernetic model focusing on\ncommunication and control, but not by using an organization-theoretical\napproach. The further differentiation of the military as a subsystem became\npossible in the course of the twentieth century because of ongoing learning\nprocesses about previous wars.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Zipf's law and maximum sustainable growth: Zipf's law states that the number of firms with size greater than S is\ninversely proportional to S. Most explanations start with Gibrat's rule of\nproportional growth but require additional constraints. We show that Gibrat's\nrule, at all firm levels, yields Zipf's law under a balance condition between\nthe effective growth rate of incumbent firms (which includes their possible\ndemise) and the growth rate of investments in entrant firms. Remarkably, Zipf's\nlaw is the signature of the long-term optimal allocation of resources that\nensures the maximum sustainable growth rate of an economy.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Quantifying information loss on chaotic attractors through recurrence\n  networks: We propose an entropy measure for the analysis of chaotic attractors through\nrecurrence networks which are un-weighted and un-directed complex networks\nconstructed from time series of dynamical systems using specific criteria. We\nshow that the proposed measure converges to a constant value with increase in\nthe number of data points on the attractor (or the number of nodes on the\nnetwork) and the embedding dimension used for the construction of the network,\nand clearly distinguishes between the recurrence network from chaotic time\nseries and white noise. Since the measure is characteristic to the network\ntopology, it can be used to quantify the information loss associated with the\nstructural change of a chaotic attractor in terms of the difference in the link\ndensity of the corresponding recurrence networks. We also indicate some\npractical applications of the proposed measure in the recurrence analysis of\nchaotic attractors as well as the relevance of the proposed measure in the\ncontext of the general theory of complex networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Inequality leads to the evolution of intolerance in reputation-based\n  populations: This work studies the impact of economic inequality on the evolution of\nintolerance through a reputation-based model of indirect reciprocity. Results\nshow that economic inequality is a powerful enhancer of intolerance, inducing\nthe escalation of out-group discrimination even without the presence of new\nintolerant mutants. It also generates behavior modifications within tolerant\ndisfavored minorities: their members either relax punishments against the\nuncooperative or prioritize helping the wealthy, even suffering discrimination\nin return. On the other hand, the redistribution of wealth is proved as a\nviable solution to avoid the spread of intolerance as long as it increases\nequality and is implemented before intolerance permeates part of the\npopulation.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Urban Geography and Scaling of Contemporary Indian Cities: This paper attempts to create a first comprehensive analysis of the\nintegrated characteristics of contemporary Indian cities, using scaling and\ngeographic analysis over a set of diverse indicators. We use data at the level\nof Urban Agglomerations in India from the Census 2011 and from a few other\nsources to characterize patterns of urban population density, infrastructure,\nurban services, economic performance, crime and innovation. Many of the results\nare in line with expectations from urban theory and with the behaviour of\nanalogous quantities in other urban systems in both high and middle-income\nnations. India is a continental scale, fast developing urban system, and\nconsequently there are also a number of interesting exceptions and surprises\nrelated to both specific quantities and strong regional patterns of variation.\nWe characterize these patterns in detail for crime and innovation and connect\nthem to the existing literature on their determinants in a specifically Indian\ncontext. The paucity of data at the urban level and the absence of official\ndefinitions for functional cities in India create a number of limitations and\ncaveats to any present analysis. We discuss these shortcomings and spell out\nthe challenge for a systematic statistical data collection relevant to cities\nand urban development in India.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Quantifying the sensing power of crowd-sourced vehicle fleets: Sensors can measure air quality, traffic congestion, and other aspects of\nurban environments. The fine-grained diagnostic information they provide could\nhelp urban managers to monitor a city's health. Recently, a `drive-by' paradigm\nhas been proposed in which sensors are deployed on third-party vehicles,\nenabling wide coverage at low cost} Research on drive-by sensing has mostly\nfocused on sensor engineering, but a key question remains unexplored: How many\nvehicles would be required to adequately scan a city? Here, we address this\nquestion by analyzing the sensing power of a taxi fleet. Taxis, being numerous\nin cities and typically equipped with some sensing technology (e.g. GPS), are\nnatural hosts for the sensors. Our strategy is to view drive-by sensing as a\nspreading process, in which the area of sensed terrain expands as\nsensor-equipped taxis diffuse through a city's streets. In tandem with a simple\nmodel for the movements of the taxis, this analogy lets us analytically\ndetermine the fraction of a city's street network sensed by a fleet of taxis\nduring a day. Our results agree with taxi data obtained from nine major cities,\nand reveal that a remarkably small number of taxis can scan a large number of\nstreets. This finding appears to be universal, indicating its applicability to\ncities beyond those analyzed here. Moreover, because taxi motions combine\nrandomness and regularity (passengers' destinations being random, but the\nroutes to them being deterministic), the spreading properties of taxi fleets\nare unusual; in stark contrast to random walks, the stationary densities of our\ntaxi model obey Zipf's law, consistent with the empirical taxi data. Our\nresults have direct utility for town councilors, smart-city designers, and\nother urban decision makers.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The physics of (ir)rational choice: Even though classic theories and models of discrete choice pose man as a\nrational being, it has been shown extensively that people persistently violate\nrationality in their actual choices. Recent models of decision-making take\nthese violations often (partially) into account, however, a unified framework\nhas not been established. Here we propose such a framework, inspired by the\nIsing model from statistical physics, and show that representing choice\nproblems as a graph, together with a simple choice process, allows us to\nexplain both rational decisions as well as violations of rationality.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Joint Estimation of the Non-parametric Transitivity and Preferential\n  Attachment Functions in Scientific Co-authorship Networks: We propose a statistical method to estimate simultaneously the non-parametric\ntransitivity and preferential attachment functions in a growing network, in\ncontrast to conventional methods that either estimate each function in\nisolation or assume some functional form for them. Our model is shown to be a\ngood fit to two real-world co-authorship networks and be able to bring to light\nintriguing details of the preferential attachment and transitivity phenomena\nthat would be unavailable under traditional methods. We also introduce a method\nto quantify the amount of contributions of those phenomena in the growth\nprocess of a network based on the probabilistic dynamic process induced by the\nmodel formula. Applying this method, we found that transitivity dominated PA in\nboth co-authorship networks. This suggests the importance of indirect relations\nin scientific creative processes. The proposed methods are implemented in the R\npackage FoFaF.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Collapse of cooperation and corruption in a mathematical model within\n  game theory including Moldovan case study (Homo Sociologicus vs Homo\n  Economicus): A simple model from game theory, which can imitate a mechanism of corruption\nand cooperation patterns, is proposed. The settings are divided into two\nstudies with examples related to safety of Moldovan economy. In Homo Economicus\nworld, players seem to act in rational way and decisions are driven by payouts.\nIn Homo Sociologicus world decisions are described by players acquiring\nreputation and evolving altruism, which in turn determine their choice of\nstrategy (evolution of cooperation). I shall discuss both modeling approaches\nof Prisoner Dilemma, to understand collapse of cooperation and increase of\ncorruption in postcommunistic countries as Republic of Moldova. Possible more\nefficient for society outcomes (fairness equilibrium) are also discussed.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Entropic Spatial Auto-correlation of Voter Uncertainty and Voter\n  Transitions in Parliamentary Elections: This paper studies a novel spatial auto-correlation model of voter\nuncertainty across districts. We use the Moran $I$ index to measure the\nauto-correlation of Shannon, relative Shannon, Tsallis and relative Tsallis\nentropies of regional electoral outcomes with respect to geographic adjacency,\nproximity and sectarian adjacency. Using data from the Lebanese parliamentary\nelections, we find strong geographic and gravitational adjacency correlations.\nMore importantly, there is a notably strong correlation in sectarian adjacency\nin both $2018$ and $2022$ elections, with a very high level of confidence. This\nresult asserts the dominance of the sectarian factor in Lebanese politics. We\nalso introduce the method of maximized general entropy estimation that allows\nus to determine the Markov transition matrix of voters between consecutive\nelections.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Collective emotion dynamics in chats with agents, moderators and Bots: Using agent-directed simulations, we investigate fluctuations in the\ncollective emotional states on a chat network where agents interchange messages\nwith a fixed number of moderators and emotional Bot. To design a realistic chat\nsystem, the interaction rules and some statistical parameters, as well as the\nagent's attributes, are inferred from the empirical chat channel\n\\texttt{Ubuntu}. In the simulations, the Bot's emotion is fixed; the moderators\ntune the level of its activity by passing a fraction $\\epsilon$ of messages to\nthe Bot. At $\\epsilon \\gtrsim 0$, the collective emotional state matching the\nBot's emotion polarity gradually arises; the average growth rate of the\ndominant emotional charge serves as an order parameter. Due to self-organizing\neffects, the collective dynamics is more explosive when positive emotions arise\nby positive Bot than the onset of negative emotions in the presence of negative\nBot at the same $\\epsilon$. Furthermore, when the emotions matching the Bot's\nemotion polarity are spread over the system, the underlying fractal processes\nexhibit higher persistence and stronger clustering of events than the processes\nspreading of emotion polarity opposite to the Bot's emotion. On the other hand,\nthe relaxation dynamics is controlled by the external noise; the related\nnonextensive parameter, estimated from the statistics of returns, is virtually\nindependent of the Bot's activity level and emotion contents.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Completing $h$: Nearly a decade ago, the science community was introduced to the $h$-index, a\nproposed statistical measure of the collective impact of the publications of\nany individual researcher. It is of course undeniable that any method of\nreducing a complex data set to a single number will necessarily have certain\nlimitations and introduce certain biases. However, in this paper we point out\nthat the definition of the $h$-index actually suffers from something far\ndeeper: a hidden mathematical incompleteness intrinsic to its definition. In\nparticular, we point out that one critical step within the definition of $h$\nhas been missed until now, resulting in an index which only achieves its stated\nobjectives under certain rather limited circumstances. For example, this\nincompleteness explains why the $h$-index ultimately has more utility in\ncertain scientific subfields than others. In this paper, we expose the origin\nof this incompleteness and then also propose a method of completing the\ndefinition of $h$ in a way which remains close to its original guiding\nprinciple. As a result, this \"completed\" $h$ not only reduces to the usual $h$\nin cases where the $h$-index already achieves its objectives, but also extends\nthe validity of the $h$-index into situations where it currently does not.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Comparison of different methods of spatial disaggregation of electricity: Energy system models involve various input data sets representing the\ngeneration, consumption and transport infrastructure of electricity. Especially\nenergy system models with a focus on the transmission grid require time series\nof electricity feed-in and consumption in a high spatial resolution. In\ngeneral, there are two approaches to obtain regionalized time series: top-down\nand bottom-up. In many cases, both methodologies may be combined to aggregate\nor disaggregate input data. Furthermore, there exist various approaches to\nassign regionalized feed-in of renewable energy sources and electrical load to\nthe model's grid connection points. The variety in the regionalization process\nleads to significant differences on a regional scope, even if global values are\nthe same. We develop a methodology to compare regionalization techniques of\ninput data for photovoltaics, wind and electrical load between various models\nas well as data assignment techniques to the power grid nodes. We further\ndefine two invariants to evaluate the outcome of the regionalization process at\nthe NUTS 3 level, one invariant for the annual profiles and one for the\ninstalled capacities. This methodology enabled us to compare different\nregionalization and assignment workflows using simple parameters, without\nexplicit knowledge of grid topology. Our results show that the resolution of\nthe input data and the use of a top-down or a bottom-up approach are the most\ndeterminant factors in the regionalization process.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Electric Vehicles and their Effect on Network Load: The composition of the fleet of road cars is changing from fuel powered cars\nto plug-in hybrid (PHEV) and electrical vehicles (EV). The (electrical) range\nof these vehicles is limited, leading to so called `range anxiety'. Firstly,\nthis leads to a preference of shorter routes. Secondly, (PH)EVs have the\nability to regenerate energy from braking, which make routes with many\naccerelations and decelerations not as unattractive for drivers of (PH)EVs as\nfor drivers of vehicles with an internal combustion engine (ICE). This paper\ncombines this, and adds drivers might prefer a lower speed limit due to a lower\nenergy consumption. These elements are compared to with road characteristics.\nIt is found, motorways are usually less favourable for drivers of (PH)EVs\ncompared to drivers of ICE vehicles, and they will prefer the shorter routes on\nsecondary roads or through towns. This will influence the use of the underlying\nroad network, and thereby affecting congestion, emissions and safety. This\nshift towards the underlying road network needs to be taken into account in\ndesigns for the network.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Symmetry breaking by heating in a continuous opinion model: We study the critical behavior of a continuous opinion model, driven by\nkinetic exchanges in a fully-connected population. Opinions range in the real\ninterval $[-1,1]$, representing the different shades of opinions against and\nfor an issue under debate. Individual's opinions evolve through pairwise\ninteractions, with couplings that are typically positive, but a fraction $p$ of\nnegative ones is allowed. Moreover, a social temperature parameter $T$ controls\nthe tendency of the individual responses towards neutrality. Depending on $p$\nand $T$, different collective states emerge: symmetry broken (one side wins),\nsymmetric (tie of opposite sides) and absorbing neutral (indecision wins). We\nfind the critical points and exponents that characterize the phase transitions\nbetween them. The symmetry breaking transition belongs to the usual Ising\nmean-field universality class, but the absorbing-phase transitions, with\n$\\beta=0.5$, are out of the paradigmatic directed percolation class. Moreover,\nordered phases can emerge by increasing social temperature.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Impact of CO2 prices on the design of a highly decarbonised coupled\n  electricity and heating system in Europe: Ambitious targets for renewable energy and CO2 taxation both represent\npolitical instruments for decarbonisation of the energy system. We model a high\nnumber of coupled electricity and heating systems, where the primary sources of\nCO2 neutral energy are from variable renewable energy sources (VRES), i.e.,\nwind and solar generators. The model includes hourly dispatch of all\ntechnologies for a full year for every country in Europe. In each model run,\nthe amount of renewable energy and the level of CO2 tax are fixed exogenously,\nwhile the cost-optimal composition of energy generation, conversion,\ntransmission and storage technologies and the corresponding CO2 emissions are\ncalculated. We show that even for high penetrations of VRES, a significant CO2\ntax of more than 100 euro/tCO2 is required to limit the combined CO2 emissions\nfrom the sectors to less than 5% of 1990 levels, because curtailment of VRES,\ncombustion of fossil fuels and inefficient conversion technologies are\neconomically favoured despite the presence of abundant VRES. A sufficiently\nhigh CO2 tax results in the more efficient use of VRES by means of heat pumps\nand hot water storage, in particular. We conclude that a renewable energy\ntarget on its own is not sufficient; in addition, a CO2 tax is required to\ndecarbonise the electricity and heating sectors and incentivise the least cost\ncombination of flexible and efficient energy conversion and storage.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Stochastic graph Voronoi tessellation reveals community structure: Given a network, the statistical ensemble of its graph-Voronoi diagrams with\nrandomly chosen cell centers exhibits properties convertible into information\non the network's large scale structures. We define a node-pair level measure\ncalled {\\it Voronoi cohesion} which describes the probability for sharing the\nsame Voronoi cell, when randomly choosing $g$ centers in the network. This\nmeasure provides information based on the global context (the network in its\nentirety) a type of information that is not carried by other similarity\nmeasures. We explore the mathematical background of this phenomenon and several\nof its potential applications. A special focus is laid on the possibilities and\nlimitations pertaining to the exploitation of the phenomenon for community\ndetection purposes.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Measuring Scientific Broadness: Who has not read letters of recommendations that comment on a student's\n`broadness' and wondered what to make of it? We here propose a way to quantify\nscientific broadness by a semantic analysis of researchers' publications. We\napply our methods to papers on the open-access server arXiv.org and report our\nfindings.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Testbed for Wireless Vehicle Communication: a Simulation Approach based\n  on Three-Phase Traffic Theory: A testbed for wireless vehicle communication based on a microscopic model in\nthe framework of three-phase traffic theory is presented. In this testbed,\nvehicle motion in traffic flow and analyses of a vehicle communication channel\naccess based on IEEE 802.11e mechanisms, radio propagation modeling, message\nreception characteristics as well as all other effects associated with ad-hoc\nnetworks are integrated into a three-phase traffic flow model. Based on\nsimulations of this testbed, some statistical features of ad-hoc vehicle\nnetworks as well as the effect of C2C communication on increase in the\nefficiency and safety of traffic are studied.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Control centrality and hierarchical structure in complex networks: We introduce the concept of control centrality to quantify the ability of a\nsingle node to control a directed weighted network. We calculate the\ndistribution of control centrality for several real networks and find that it\nis mainly determined by the network's degree distribution. We rigorously prove\nthat in a directed network without loops the control centrality of a node is\nuniquely determined by its layer index or topological position in the\nunderlying hierarchical structure of the network. Inspired by the deep relation\nbetween control centrality and hierarchical structure in a general directed\nnetwork, we design an efficient attack strategy against the controllability of\nmalicious networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Disentangling collective trends from local dynamics: A single social phenomenon (such as crime, unemployment or birth rate) can be\nobserved through temporal series corresponding to units at different levels\n(cities, regions, countries...). Units at a given local level may follow a\ncollective trend imposed by external conditions, but also may display\nfluctuations of purely local origin. The local behavior is usually computed as\nthe difference between the local data and a global average (e.g. a national\naverage), a view point which can be very misleading. We propose here a method\nfor separating the local dynamics from the global trend in a collection of\ncorrelated time series. We take an independent component analysis approach in\nwhich we do not assume a small unbiased local contribution in contrast with\npreviously proposed methods. We first test our method on synthetic series\ngenerated by correlated random walkers. We then consider crime rate series (in\nthe US and France) and the evolution of obesity rate in the US, which are two\nimportant examples of societal measures. For crime rates, the separation\nbetween global and local policies is a major subject of debate. For the US, we\nobserve large fluctuations in the transition period of mid-70's during which\ncrime rates increased significantly, whereas since the 80's, the state crime\nrates are governed by external factors and the importance of local\nspecificities being decreasing. In the case of obesity, our method shows that\nexternal factors dominate the evolution of obesity since 2000, and that\ndifferent states can have different dynamical behavior even if their obesity\nprevalence is similar.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Leaving bads provides better outcome than approaching goods in a social\n  dilemma: Individual migration has been regarded as an important factor for the\nevolution of cooperation in mobile populations. Motivations of migration,\nhowever, can be largely divergent: one is highly frustrated by the vicinity of\nan exploiter or defector, while other enthusiastically searches cooperator\nmates. Albeit both extreme attitudes are observed in human behavior, but their\nspecific impacts on wellbeing remained unexplored. In this work, we propose an\norientation-driven migration approach for mobile individuals in combination\nwith the mentioned migration preferences and study their roles in the\ncooperation level in a two-dimensional public goods game. We find that\ncooperation can be greatly promoted when individuals are more inclined to\nescape away from their defective neighbors. On the contrary, cooperation cannot\nbe effectively maintained when individuals are more motivated to approach their\ncooperative neighbors. In addition, compared with random migration, movement by\nleaving defectors can promote cooperation more effectively. By means of\ntheoretical analysis and numerical calculations, we further find that when\nindividuals only choose to escape away from their defective neighbors, the\naverage distance between cooperators and defectors can be enlarged, hence the\nnatural invasion of defection can be efficiently blocked. Our work, thus,\nprovides further insight on how different migration preferences influence the\nevolution of cooperation in the unified framework of spatially social games.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Estimating the number of COVID-19 cases from population isolation level: We use the logistic function to estimate the number of individuals infected\nby a virus in a period of time as a function of social isolation level in the\nprevious period of the infection occurrences. Each period is composed by a\nfixed date range in days which the social isolation is supposed to take effect\nover the virus spread in the next date range. The sample is the COVID-19 cases\nand social isolation level data from S\\~ao Paulo State, Brazil. The proposed\nmethod is divided into two stages: 1) The logistic function is fitted against\nCOVID-19 empirical data to obtain the function parameters; 2) the function\nparameters, except for the overall growth rate, and the mean of social\nisolation level for all periods of time are used to calculate a constant called\n$\\lambda$. The logistic growth rate for each period of time is calculated using\n$\\lambda$ and the isolation level for that period. The number of cases in a\nperiod is estimated using the logistic function and the growth rate from\nprevious period of time to obtain the effect of social isolation during the\nelapsed time. The period of time that produces a better correlation between\nempirical and estimated data was 5 days. We conclude the method performs a data\nestimation with high correlation with the empirical data.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Phase transitions in random Potts systems and the community detection\n  problem: spin-glass type and dynamic perspectives: Phase transitions in spin glass type systems and, more recently, in related\ncomputational problems have gained broad interest in disparate arenas. In the\ncurrent work, we focus on the \"community detection\" problem when cast in terms\nof a general Potts spin glass type problem. As such, our results apply to\nrather broad Potts spin glass type systems. Community detection describes the\ngeneral problem of partitioning a complex system involving many elements into\noptimally decoupled \"communities\" of such elements. We report on phase\ntransitions between solvable and unsolvable regimes. Solvable region may\nfurther split into \"easy\" and \"hard\" phases. Spin glass type phase transitions\nappear at both low and high temperatures (or noise). Low temperature\ntransitions correspond to an \"order by disorder\" type effect wherein\nfluctuations render the system ordered or solvable. Separate transitions appear\nat higher temperatures into a disordered (or an unsolvable) phase. Different\nsorts of randomness lead to disparate behaviors. We illustrate the spin glass\ncharacter of both transitions and report on memory effects. We further relate\nPotts type spin systems to mechanical analogs and suggest how chaotic-type\nbehavior in general thermodynamic systems can indeed naturally arise in\nhard-computational problems and spin-glasses. The correspondence between the\ntwo types of transitions (spin glass and dynamic) is likely to extend across a\nlarger spectrum of spin glass type systems and hard computational problems. We\nbriefly discuss potential implications of these transitions in complex many\nbody physical systems.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Different Ways to Deal with Subway Accidents: With the development of the subway and the pressing demand of environmentally\nfriendly transportation, more and more people travel by subway. In recent\ndecades, the issues about passenger passive safety on the train have received\nextensive attention. In this research, the head injury of a standing passenger\nin the subway is investigated. Three MADYMO models of the different standing\npassenger postures, defined as baseline scenarios, are numerically set up.\nHIC15 values of passengers with different postures are gained by systematic\nparametric studies. The injury numerical simulation results of various\nscenarios with different friction coefficients, collision acceleration,\nstanding angle, horizontal handrail height, and ring handrail height are\nanalyzed. Results show that the horizontal handrail provides better protection\nin the three different standing passenger postures. Different friction\ncoefficients and the standing angle have great impact on the head injuries of\npassengers in three different scenarios. The handrail height also has some\neffects on head injury of passengers with different standing postures, so it is\nnecessary to be considered when designing the interior layout of the subway.\nThis study may provide guidance for the safety design of the subway and some\nadvices for standing subway passengers.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A multilayer perspective for the analysis of urban transportation\n  systems: Public urban mobility systems are composed by several transportation modes\nconnected together. Most studies in urban mobility and planning often ignore\nthe multi-layer nature of transportation systems considering only aggregated\nversions of this complex scenario. In this work we present a model for the\nrepresentation of the transportation system of an entire city as a multiplex\nnetwork. Using two different perspectives, one in which each line is a layer\nand one in which lines of the same transportation mode are grouped together, we\nstudy the interconnected structure of 9 different cities in Europe raging from\nsmall towns to mega-cities like London and Berlin highlighting their\nvulnerabilities and possible improvements. Finally, for the city of Zaragoza in\nSpain, we also consider data about service schedule and waiting times, which\nallow us to create a simple yet realistic model for urban mobility able to\nreproduce real-world facts and to test for network improvements.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Critical density of urban traffic: A modified version of the Intelligent Driver Model was used to simulate\ntraffic in the district of Afogados, in the city of Recife, Brazil, with the\nobjective to verify whether the complexity of the underlying street grid, with\nmultiple lane streets, crossings, and semaphores, is capable of exhibiting the\neffect of critical density: appearance of a maximum in the vehicle flux versus\ndensity curve. Numerical simulations demonstrate that this effect indeed is\nobserved on individual avenues, while the phase offset among the avenues\nresults in damping of this effect for the region as a whole.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Unfolding accessibility provides a macroscopic approach to temporal\n  networks: An accessibility graph of a network contains a link, wherever there is a path\nof arbitrary length between two nodes. We generalize the concept of\naccessibility to temporal networks. Building an accessibility graph by\nconsecutively adding paths of growing length (unfolding), we obtain information\nabout the distribution of shortest path durations and characteristic\ntime-scales in temporal networks. Moreover, we define causal fidelity to\nmeasure the goodness of their static representation. The practicability of our\nproposed methods is demonstrated for three examples: networks of social\ncontacts, livestock trade and sexual contacts.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Networking - A Statistical Physics Perspective: Efficient networking has a substantial economic and societal impact in a\nbroad range of areas including transportation systems, wired and wireless\ncommunications and a range of Internet applications. As transportation and\ncommunication networks become increasingly more complex, the ever increasing\ndemand for congestion control, higher traffic capacity, quality of service,\nrobustness and reduced energy consumption require new tools and methods to meet\nthese conflicting requirements. The new methodology should serve for gaining\nbetter understanding of the properties of networking systems at the macroscopic\nlevel, as well as for the development of new principled optimization and\nmanagement algorithms at the microscopic level. Methods of statistical physics\nseem best placed to provide new approaches as they have been developed\nspecifically to deal with non-linear large scale systems. This paper aims at\npresenting an overview of tools and methods that have been developed within the\nstatistical physics community and that can be readily applied to address the\nemerging problems in networking. These include diffusion processes, methods\nfrom disordered systems and polymer physics, probabilistic inference, which\nhave direct relevance to network routing, file and frequency distribution, the\nexploration of network structures and vulnerability, and various other\npractical networking applications.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A tale of many cities: universal patterns in human urban mobility: The advent of geographic online social networks such as Foursquare, where\nusers voluntarily signal their current location, opens the door to powerful\nstudies on human movement. In particular the fine granularity of the location\ndata, with GPS accuracy down to 10 meters, and the worldwide scale of\nFoursquare adoption are unprecedented. In this paper we study urban mobility\npatterns of people in several metropolitan cities around the globe by analyzing\na large set of Foursquare users. Surprisingly, while there are variations in\nhuman movement in different cities, our analysis shows that those are\npredominantly due to different distributions of places across different urban\nenvironments. Moreover, a universal law for human mobility is identified, which\nisolates as a key component the rank-distance, factoring in the number of\nplaces between origin and destination, rather than pure physical distance, as\nconsidered in some previous works. Building on our findings, we also show how a\nrank-based movement model accurately captures real human movements in different\ncities. Our results shed new light on the driving factors of urban human\nmobility, with potential applications for urban planning, location-based\nadvertisement and even social studies.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Clustering algorithm for formations in football games: This paper develops a clustering algorithm for formations in team sports,\nwith a focus on football games. Our method first clusters formations into\nseveral average formations: `442,' `4141,' `433,' `541,' and `343.' Then, each\naverage formation is further divided into more specific patterns in which the\nconfigurations of players are slightly different. The latter step is based on\nhierarchical clustering and the Delaunay method, which defines the formation of\na team as an adjacency matrix of Delaunay triangulation. A formation clustered\nusing our method is expressed in a form such as `442-C1'.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Skepticism and rumor spreading: the role of spatial correlations: Critical thinking and skepticism are fundamental mechanisms that one may use\nto prevent the spreading of rumors, fake-news and misinformation. We consider a\nsimple model in which agents without previous contact with the rumor, being\nskeptically oriented, may convince spreaders to stop their activity or, once\nexposed to the rumor, decide not to propagate it as a consequence, for example,\nof fact-checking. We extend a previous, mean-field analysis of the combined\neffect of these two mechanisms, active and passive skepticism, to include\nspatial correlations. This can be done either analytically, through the pair\napproximation, or simulating an agent-based version on diverse networks. Our\nresults show that while in mean-field there is no coexistence between spreaders\nand susceptibles (although, depending on the parameters, there may be\nbistability depending on the initial conditions), when spatial correlations are\nincluded, because of the protective effect of the isolation provided by removed\nagents, coexistence is possible.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A Model of Collaboration Network Formation with Heterogenous Skills: Collaboration networks provide a method for examining the highly\nheterogeneous structure of collaborative communities. However, we still have\nlimited theoretical understanding of how individual heterogeneity relates to\nnetwork heterogeneity. The model presented here provides a framework linking an\nindividual's skill set to her position in the collaboration network, and the\ndistribution of skills in the population to the structure of the collaboration\nnetwork as a whole. This model suggests that there is a non-trivial\nrelationship between skills and network position: individuals with a useful\ncombination of skills will have a disproportionate number of links in the\nnetwork. Indeed, in some cases, an individual's degree is non-monotonic in the\nnumber of skills she has--an individual with very few skills may outperform an\nindividual with many. Special cases of the model suggest that the degree\ndistribution of the network will be skewed, even when the distribution of\nskills is uniform in the population. The degree distribution becomes more\nskewed as problems become more difficult, leading to a community dominated by a\nfew high-degree superstars. This has striking implications for labor market\noutcomes in industries where production is largely the result of collaborative\neffort.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Price Trends in a Simplified Model of the Wealth Game: We consider a simplified version of the Wealth Game, which is an agent-based\nfinancial market model with many interesting features resembling the real stock\nmarket. Market makers are not present in the game so that the majority traders\nare forced to reduce the amount of stocks they trade, in order to have a\nbalance in the supply and demand. The strategy space is also simplified so that\nthe market is only left with strategies resembling the decisions of optimistic\nor pessimistic fundamentalists and trend-followers in the real stock market. A\ndynamical phase transition between a trendsetters' phase and a bouncing phase\nis discovered in the space of price sensitivity and market impact. Analysis\nbased on a semi-empirical approach explains the phase transition and locates\nthe phase boundary. A phase transition is also observed when the fraction of\ntrend-following strategies increases, which can be explained macroscopically by\nmatching the supply and demand of stocks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The physics of custody: Divorced individuals face complex situations when they have children with\ndifferent ex-partners, or even more, when their new partners have children of\ntheir own. In such cases, and when kids spend every other weekend with each\nparent, a practical problem emerges: Is it possible to have such a custody\narrangement that every couple has either all of the kids together or no kids at\nall? We show that in general, it is not possible, but that the number of\ncouples that do can be maximized. The problem turns out to be equivalent to\nfinding the ground state of a spin glass system, which is known to be\nequivalent to what is called a weighted max-cut problem in graph theory, and\nhence it is NP-Complete.",
        "category": "physics_soc-ph"
    },
    {
        "text": "How many people can simultaneously move through a pedestrian space? The\n  impact of complex flow situations on the shape of the fundamental diagram: Pedestrian crowding occurs more frequent. As a result of the increasing\npedestrian demand in public space, the limits of pedestrian spaces are of\nincreasing interest. Some research on the maximum demand that can flow through\na cross-section has been presented, which mainly features simple movement base\ncases, low-density situations and/or a homogeneous crowd. Consequently, it is\ncurrently unclear to what extent their findings apply to heterogeneous\nhigh-density crowds, which are often encountered during real-world scenarios.\nThe CrowdLimits experiment attempted to reproduce crowd movement dynamics of\nheterogeneous crowds experiencing higher densities than have been recorded up\nto this moment. Here, the aim was to study the impact of the three important\ndifferences between the current laboratory studies and real-world crowd\ndynamics in crowded pedestrian spaces simultaneously, namely crowd\nheterogeneity, high densities movements, and (more) complex movement base\ncases. This study shows that there are substantial differences in the maximum\nsustainable flow rate, and the maximum local and global sustainable density for\ndistinct movement base cases and flow ratios. Moreover, the results provide\nevidence of that continuation of flow under very high densities can be\nrecreated under laboratory conditions using a heterogeneous population of\npedestrians. Besides, the experimental results indicate that the maximum global\nflow rate decreases when the scenario becomes more difficult (i.e.\nbidirectional to intersecting) and the collision avoidance opportunities\ndecrease (i.e. 80-20 to 50-50 flow ratio). Thus, this paper concludes that the\nmaximum flow rate of pedestrian infrastructures decreases with increasing\ncomplexity.",
        "category": "physics_soc-ph"
    },
    {
        "text": "On the Empirical Relevance of the Transient in Opinion Models: While the number and variety of models to explain opinion exchange dynamics\nis huge, attempts to justify the model results using empirical data are\nrelatively rare. As linking to real data is essential for establishing model\ncredibility, this Letter develops a empirical confirmation experiment by which\nan opinion model is related to real election data. The model is based on a\nrepresentation of opinions as a vector of $k$ bits. Individuals interact\naccording to the principle that similarity leads to interaction and interaction\nleads to still more similarity. In the comparison to real data we concentrate\non the transient opinion profiles that form during the dynamic process. An\nartificial election procedure is introduced which allows to relate transient\nopinion configurations to the electoral performance of candidates for which\ndata is available. The election procedure based on the well--established\nprinciple of proximity voting is repeatedly performed during the transient\nperiod and remarkable statistical agreement with the empirical data is\nobserved.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Efficiency and irreversibility of movements in a city: We know that maximal efficiency in physical systems is attained by reversible\nprocesses. It is then interesting to see how irreversibility affects efficiency\nin other systems, e.g., in a city. In this study, we focus on a cyclic process\nof movements (home to workplace and back to home) in a city to investigate the\nabove question. To this end, we present a minimal model of the movements, along\nwith plausible definitions for the efficiency and irreversibility of the\nprocess; more precisely, we take the inverse of the total travel time per\nnumber of trips for efficiency and the relative entropy of the forward and\nbackward flow distributions for the process irreversibility. We perform\nnumerical simulations of the model for reasonable choices of the population\ndistribution, the mobility law, and the movement strategy. The results show\nthat the efficiency of movements is indeed negatively correlated with the above\nmeasure of irreversibility. The structure of the network and the impact of the\nflows on the travel times are the main factors here that affect the time\nintervals of arriving to destinations and returning to origins, which are\nusually larger than the time interval of the departures. This in turn gives\nrise to diverging of the backward flows from the forward ones and results to\nentropy (disorder or uncertainty) production in the system. The findings of\nthis study might be helpful in characterizing more accurately the city\nefficiency and in better understanding of the main working principles of these\ncomplex systems.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A large-scale study of the World Wide Web: network correlation functions\n  with scale-invariant boundaries: We performed a large-scale crawl of the World Wide Web, covering 6.9 Million\ndomains and 57 Million subdomains, including all high-traffic sites of the\nInternet. We present a study of the correlations found between quantities\nmeasuring the structural relevance of each node in the network (the in- and\nout-degree, the local clustering coefficient, the first-neighbor in-degree and\nthe Alexa rank). We find that some of these properties show strong correlation\neffects and that the dependencies occurring out of these correlations follow\npower laws not only for the averages, but also for the boundaries of the\nrespective density distributions. In addition, these scale-free limits do not\nfollow the same exponents as the corresponding averages. In our study we retain\nthe directionality of the hyperlinks and develop a statistical estimate for the\nclustering coefficient of directed graphs.\n  We include in our study the correlations between the in-degree and the Alexa\ntraffic rank, a popular index for the traffic volume, finding non-trivial\npower-law correlations. We find that sites with more/less than about one\nThousand links from different domains have remarkably different statistical\nproperties, for all correlation functions studied, indicating towards an\nunderlying hierarchical structure of the World Wide Web.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Propagation of wind-power-induced fluctuations in power grids: Renewable generators perturb the electric power grid with heavily\nnon-Gaussian and time correlated fluctuations. While changes in generated power\non timescales of minutes and hours are compensated by frequency control\nmeasures, we report subsecond distribution grid frequency measurements with\nlocal non-Gaussian fluctuations which depend on the magnitude of wind power\ngeneration in the grid. Motivated by such experimental findings, we simulate\nthe sub-second grid frequency dynamics by perturbing the power grid, as modeled\nby a network of phase coupled nonlinear oscillators, with synthetically\ngenerated wind power feed-in time series. We derive a linear response theory\nand obtain analytical results for the variance of frequency increment\ndistributions. We find that the variance of short-term fluctuations decays, for\nlarge inertia, exponentially with distance to the feed-in node, in agreement\nwith numerical results both for a linear chain of nodes and the German\ntransmission grid topology. In sharp contrast, the kurtosis of frequency\nincrements is numerically found to decay only slowly, not exponentially, in\nboth systems, indicating that the non-Gaussian shape of frequency fluctuations\npersists over long ranges.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Predicting link directions via a recursive subgraph-based ranking: Link directions are essential to the functionality of networks and their\nprediction is helpful towards a better knowledge of directed networks from\nincomplete real-world data. We study the problem of predicting the directions\nof some links by using the existence and directions of the rest of links. We\npropose a solution by first ranking nodes in a specific order and then\npredicting each link as stemming from a lower-ranked node towards a\nhigher-ranked one. The proposed ranking method works recursively by utilizing\nlocal indicators on multiple scales, each corresponding to a subgraph extracted\nfrom the original network. Experiments on real networks show that the\ndirections of a substantial fraction of links can be correctly recovered by our\nmethod, which outperforms either purely local or global methods.",
        "category": "physics_soc-ph"
    },
    {
        "text": "VaR-Efficient Portfolios for a Class of Super- and Sub-Exponentially\n  Decaying Assets Return Distributions: Using a family of modified Weibull distributions, encompassing both\nsub-exponentials and super-exponentials, to parameterize the marginal\ndistributions of asset returns and their multivariate generalizations with\nGaussian copulas, we offer exact formulas for the tails of the distribution\n$P(S)$ of returns $S$ of a portfolio of arbitrary composition of these assets.\nWe find that the tail of $P(S)$ is also asymptotically a modified Weibull\ndistribution with a characteristic scale $\\chi$ function of the asset weights\nwith different functional forms depending on the super- or sub-exponential\nbehavior of the marginals and on the strength of the dependence between the\nassets. We then treat in details the problem of risk minimization using the\nValue-at-Risk and Expected-Shortfall which are shown to be (asymptotically)\nequivalent in this framework.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Equalitarian Societies are Economically Impossible: The inequality of wealth distribution is a universal phenomenon in the\ncivilized nations, and it is often imputed to the Matthew effect, that is, the\nrich get richer and the poor get poorer. Some philosophers unjustified this\nphenomenon and tried to put the human civilization upon the evenness of wealth.\nNoticing the facts that 1) the emergence of the centralism is the starting\npoint of human civilization, i.e., people in a society were organized\nhierarchically, 2) the inequality of wealth emerges simultaneously, this paper\nproposes a wealth distribution model based on the hidden tree structure from\nthe viewpoint of complex network. This model considers the organized structure\nof people in a society as a hidden tree, and the cooperations among human\nbeings as the transactions on the hidden tree, thereby explains the\ndistribution of wealth. This model shows that the scale-free phenomenon of\nwealth distribution can be produced by the cascade controlling of human\nsociety, that is, the inequality of wealth can parasitize in the social\norganizations, such that any actions in eliminating the unequal wealth\ndistribution would lead to the destroy of social or economic structures,\nresulting in the collapse of the economic system, therefore, would fail in\nvain.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Social and Natural Sciences Differ in Their Research Strategies, Adapted\n  to Work for Different Knowledge Landscapes: Do different fields of knowledge require different research strategies? A\nnumerical model exploring different virtual knowledge landscapes, revealed two\ndiverging optimal search strategies. Trend following is maximized when the\npopularity of new discoveries determine the number of individuals researching\nit. This strategy works best when many researchers explore few large areas of\nknowledge. In contrast, individuals or small groups of researchers are better\nin discovering small bits of information in dispersed knowledge landscapes.\nBibliometric data of scientific publications showed a continuous bipolar\ndistribution of these strategies, ranging from natural sciences, with highly\ncited publications in journals containing a large number of articles, to the\nsocial sciences, with rarely cited publications in many journals containing a\nsmall number of articles. The natural sciences seem to adapt their research\nstrategies to landscapes with large concentrated knowledge clusters, whereas\nsocial sciences seem to have adapted to search in landscapes with many small\nisolated knowledge clusters. Similar bipolar distributions were obtained when\ncomparing levels of insularity estimated by indicators of international\ncollaboration and levels of country-self citations: researchers in academic\nareas with many journals such as social sciences, arts and humanities, were the\nmost isolated, and that was true in different regions of the world. The work\nshows that quantitative measures estimating differences between academic\ndisciplines improve our understanding of different research strategies,\neventually helping interdisciplinary research and may be also help improve\nscience policies worldwide.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Metapopulation epidemic models with heterogeneous mixing and travel\n  behaviour: The complex interplay between population movements in space and\nnon-homogeneous mixing patterns have so far hindered the fundamental\nunderstanding of the conditions for spatial invasion through a general\ntheoretical framework. To address this issue, we present an analytical\nmodelling approach taking into account such interplay under general conditions\nof mobility and interactions, in the simplifying assumption of two population\nclasses. We describe a spatially structured population with non-homogeneous\nmixing and travel behaviour through a multi-host stochastic epidemic\nmetapopulation model. Different population partitions, mixing patterns and\nmobility structures are considered, along with a specific application for the\nstudy of the role of age partition in the early spread of the 2009 H1N1\npandemic influenza.\n  We provide a complete mathematical formulation of the model and derive a\nsemi-analytical expression of the threshold condition for global invasion of an\nemerging infectious disease in the metapopulation system. A rich solution space\nis found that depends on the social partition of the population, the pattern of\ncontacts across groups and their relative social activity, the travel attitude\nof each class, and the topological and traffic features of the mobility\nnetwork. Reducing the activity of the less social group and reducing the\ncross-group mixing are predicted to be the most efficient strategies for\ncontrolling the pandemic potential in the case the less active group\nconstitutes the majority of travellers. If instead traveling is dominated by\nthe more social class, our model predicts the existence of an optimal\nacross-groups mixing that maximises the pandemic potential of the disease,\nwhereas the impact of variations in the activity of each group is less\nimportant.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Parrondo's Paradox: some new results and new ideas: Parrondo's paradox is about a paradoxical game and gambling where two\nprobabilistic losing games can be combined to form a winning game. While the\ncounter intuitive game is interesting in itself, it can be thought of a\ndiscrete version of Brownian flashing ratchet which are employed to understand\nnoise induced order. There are plenty of examples from physics to biology and\nsocial sciences where the stochastic thermal fluctuations actually help\nachieving positive movements. In our study, we study various combinations of\nlosing games in order to understand how and how far the losing combinations\nresult in winning. Further we devise an alternative model to study the similar\nparadox. The work reported here is mostly done through computer simulations.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The World-Trade Web: Topological Properties, Dynamics, and Evolution: This paper studies the statistical properties of the web of import-export\nrelationships among world countries using a weighted-network approach. We\nanalyze how the distributions of the most important network statistics\nmeasuring connectivity, assortativity, clustering and centrality have\nco-evolved over time. We show that all node-statistic distributions and their\ncorrelation structure have remained surprisingly stable in the last 20 years --\nand are likely to do so in the future. Conversely, the distribution of\n(positive) link weights is slowly moving from a log-normal density towards a\npower law. We also characterize the autoregressive properties of\nnetwork-statistics dynamics. We find that network-statistics growth rates are\nwell-proxied by fat-tailed densities like the Laplace or the asymmetric\nexponential-power. Finally, we find that all our results are reasonably robust\nto a few alternative, economically-meaningful, weighting schemes.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Modelling Complex Networks: Cameo Graphs And Transport Processes: We discuss a model accounting for the creation and development of transport\nnetworks based on the Cameo principle which refers to the idea of distribution\nof resources, including land, water, minerals, fuel and wealth. We also give an\noutlook of the use of random walks as an effective tool for the investigation\nof network structures and its functional segmentation. In particular, we have\nstudied the complex transport network of Venetian canals by means of random\nwalks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Do language change rates depend on population size?: An earlier study (Nettle 1999b) concluded, based on computer simulations and\nsome inferences from empirical data, that languages will change the more slowly\nthe larger the population gets. We replicate this study using a more complete\nlanguage model for simulations (the Schulze model combined with a\nBarabasi-Albert net- work) and a richer empirical dataset (the World Atlas of\nLanguage Structures edited by Haspelmath et al. 2005). Our simulations show\neither a weak or stronger dependence of language change on population sizes\ndepending on the parameter settings, and empirical data, like some of the\nsimulations, show a weak dependence.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Effect of network clustering on mutually cooperative coinfections: The spread of an infectious disease can be promoted by previous infections\nwith other pathogens. This cooperative effect can give rise to violent\noutbreaks, reflecting the presence of an abrupt epidemic transition. As for\nother diffusive dynamics, the topology of the interaction pattern of the host\npopulation plays a crucial role. It was conjectured that a discontinuous\ntransition arises when there are relatively few short loops and many long loops\nin the contact network. Here we focus on the role of local clustering in\ndetermining the nature of the transition. We consider two mutually cooperative\npathogens diffusing in the same population: an individual already infected with\none disease has an increased probability of getting infected by the other. We\nlook at how a disease obeying the susceptible-infected-removed dynamics spreads\non contact networks with tunable clustering. Using numerical simulations we\nshow that for large cooperativity the epidemic transition is always abrupt,\nwith the discontinuity decreasing as clustering is increased. For large\nclustering strong finite size effects are present and the discontinuous nature\nof the transition is manifest only in large networks. We also investigate the\nproblem of influential spreaders for cooperative infections, revealing that\nboth cooperativity and clustering strongly enhance the dependence of the\nspreading influence on the degree of the initial seed.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Collective behavior in the spatial spreading of obesity: Non-communicable diseases like diabetes, obesity and certain forms of cancer\nhave been increasing in many countries at alarming levels. A difficulty in the\nconception of policies to reverse these trends is the identification of the\ndrivers behind the global epidemics. Here, we implement a spatial spreading\nanalysis to investigate whether diabetes, obesity and cancer show spatial\ncorrelations revealing the effect of collective and global factors acting above\nindividual choices. We adapt a theoretical framework for critical physical\nsystems displaying collective behavior to decipher the laws of spatial\nspreading of diseases. We find a regularity in the spatial fluctuations of\ntheir prevalence revealed by a pattern of scale-free long-range correlations.\nThe fluctuations are anomalous, deviating in a fundamental way from the weaker\ncorrelations found in the underlying population distribution. This collective\nbehavior indicates that the spreading dynamics of obesity, diabetes and some\nforms of cancer like lung cancer are analogous to a critical point of\nfluctuations, just as a physical system in a second-order phase transition.\nAccording to this notion, individual interactions and habits may have\nnegligible influence in shaping the global patterns of spreading. Thus, obesity\nturns out to be a global problem where local details are of little importance.\nInterestingly, we find the same critical fluctuations in obesity and diabetes,\nand in the activities of economic sectors associated with food production such\nas supermarkets, food and beverage stores--- which cluster in a different\nuniversality class than other generic sectors of the economy. These results\nmotivate future interventions to investigate the causality of this relation\nproviding guidance for the implementation of preventive health policies.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Can complex networks describe the urban and rural tropospheric O3\n  dynamics?: Tropospheric ozone (O3) time series have been converted into complex networks\nthrough the recent so-called Visibility Graph (VG), using the data from air\nquality stations located in the western part of Andalusia (Spain). The aim is\nto apply this novel method to differentiate the behavior between rural and\nurban regions when it comes to the ozone dynamics. To do so, some centrality\nparameters of the resulting complex networks have been investigated: the\ndegree, betweenness and shortest path. Some of them are expected to corroborate\nprevious works in order to support the use of this technique; while others to\nsupply new information. Results coincide when describing the difference that\ntropospheric ozone exhibits seasonally and geographically. It is seen that\nozone behavior is fractal, in accordance to previous works. Also, it has been\ndemonstrated that this methodology is able to characterize the divergence\nencountered between measurements in urban environments and countryside. In\naddition to that, the promising outcomes of this technique support the use of\ncomplex networks for the study of air pollutants dynamics. Particularly, new\nnuances are offered such as the identification and description of singularities\nin the signal.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Investment horizons : A time-dependent measure of asset performance: We review a resent {\\em time-dependent} performance measure for economical\ntime series -- the (optimal) investment horizon approach. For stock indices,\nthe approach shows a pronounced gain-loss asymmetry that is {\\em not} observed\nfor the individual stocks that comprise the index. This difference may hint\ntowards an synchronize of the draw downs of the stocks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Modelling Cooperation and Competition in Urban Retail Ecosystems with\n  Complex Network Metrics: Understanding the impact that a new business has on the local market\necosystem is a challenging task as it is multifaceted in nature. Past work in\nthis space has examined the collaborative or competitive role of homogeneous\nvenue types (i.e. the impact of a new bookstore on existing bookstores).\nHowever, these prior works have been limited in their scope and explanatory\npower. To better measure retail performance in a modern city, a model should\nconsider a number of factors that interact synchronously. This paper is the\nfirst which considers the multifaceted types of interactions that occur in\nurban cities when examining the impact of new businesses. We first present a\nmodeling framework which examines the role of new businesses in their\nrespective local areas. Using a longitudinal dataset from location technology\nplatform Foursquare, we model new venue impact across 26 major cities\nworldwide. Representing cities as connected networks of venues, we quantify\ntheir structure and characterise their dynamics over time. We note a strong\ncommunity structure emerging in these retail networks, an observation that\nhighlights the interplay of cooperative and competitive forces that emerge in\nlocal ecosystems of retail establishments. We next devise a data-driven metric\nthat captures the first-order correlation on the impact of a new venue on\nretailers within its vicinity accounting for both homogeneous and heterogeneous\ninteractions between venue types. Lastly, we build a supervised machine\nlearning model to predict the impact of a given new venue on its local retail\necosystem. Our approach highlights the power of complex network measures in\nbuilding machine learning prediction models. These models have numerous\napplications within the retail sector and can support policymakers, business\nowners, and urban planners in the development of models to characterize and\npredict changes in urban settings.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The role of gender in scholarly authorship: Gender disparities appear to be decreasing in academia according to a number\nof metrics, such as grant funding, hiring, acceptance at scholarly journals,\nand productivity, and it might be tempting to think that gender inequity will\nsoon be a problem of the past. However, a large-scale analysis based on over\neight million papers across the natural sciences, social sciences, and\nhumanities re- reveals a number of understated and persistent ways in which\ngender inequities remain. For instance, even where raw publication counts seem\nto be equal between genders, close inspection reveals that, in certain fields,\nmen predominate in the prestigious first and last author positions. Moreover,\nwomen are significantly underrepresented as authors of single-authored papers.\nAcademics should be aware of the subtle ways that gender disparities can appear\nin scholarly authorship.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The different structure of economic ecosystems at the scales of\n  companies and countries: A key element to understand complex systems is the relationship between the\nspatial scale of investigation and the structure of the interrelation among its\nelements. When it comes to economic systems, it is now well-known that the\ncountry-product bipartite network exhibits a nested structure, which is the\nfoundation of different algorithms that have been used to scientifically\ninvestigate countries' development and forecast national economic growth.\nChanging the subject from countries to companies, a significantly different\nscenario emerges. Through the analysis of a unique dataset of Italian firms'\nexports and a worldwide dataset comprising countries' exports, here we find\nthat, while a globally nested structure is observed at the country level, a\nlocal, in-block nested structure emerges at the level of firms. Remarkably,\nthis in-block nestedness is statistically significant with respect to suitable\nnull models and the algorithmic partitions of products into blocks have a high\ncorrespondence with exogenous product classifications. These findings lay a\nsolid foundation for developing a scientific approach based on the physics of\ncomplex systems to the analysis of companies, which has been lacking until now.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Identifying the Community Structure of the International-Trade Multi\n  Network: We study the community structure of the multi-network of commodity-specific\ntrade relations among world countries over the 1992-2003 period. We compare\nstructures across commodities and time by means of the normalized mutual\ninformation index (NMI). We also compare them with exogenous community\nstructures induced by geographical distances and regional trade agreements. We\nfind that commodity-specific community structures are very heterogeneous and\nmuch more fragmented than that characterizing the aggregate ITN. This shows\nthat the aggregate properties of the ITN may result (and be very different)\nfrom the aggregation of very diverse commodity-specific layers of the multi\nnetwork. We also show that commodity-specific community structures, especially\nthose related to the chemical sector, are becoming more and more similar to the\naggregate one. Finally, our findings suggest that geographical distance is much\nmore correlated with the observed community structure than RTAs. This result\nstrengthens previous findings from the empirical literature on trade.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Kinetics of Social Contagion: Diffusion of information, behavioral patterns or innovations follows diverse\npathways depending on a number of conditions, including the structure of the\nunderlying social network, the sensitivity to peer pressure and the influence\nof media. Here we study analytically and by simulations a general model that\nincorporates threshold mechanism capturing sensitivity to peer pressure, the\neffect of `immune' nodes who never adopt, and a perpetual flow of external\ninformation. While any constant, non-zero rate of dynamically-introduced\nspontaneous adopters leads to global spreading, the kinetics by which the\nasymptotic state is approached shows rich behavior. In particular we find that,\nas a function of the immune node density, there is a transition from fast to\nslow spreading governed by entirely different mechanisms. This transition\nhappens below the percolation threshold of network fragmentation, and has its\norigin in the competition between cascading behavior induced by adopters and\nblocking due to immune nodes. This change is accompanied by a percolation\ntransition of the induced clusters.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The evolution of interdisciplinarity in physics research: Science, being a social enterprise, is subject to fragmentation into groups\nthat focus on specialized areas or topics. Often new advances occur through\ncross-fertilization of ideas between sub-fields that otherwise have little\noverlap as they study dissimilar phenomena using different techniques. Thus to\nexplore the nature and dynamics of scientific progress one needs to consider\nthe large-scale organization and interactions between different subject areas.\nHere, we study the relationships between the sub-fields of Physics using the\nPhysics and Astronomy Classification Scheme (PACS) codes employed for\nself-categorization of articles published over the past 25 years (1985-2009).\nWe observe a clear trend towards increasing interactions between the different\nsub-fields. The network of sub-fields also exhibits core-periphery\norganization, the nucleus being dominated by Condensed Matter and General\nPhysics. However, over time Interdisciplinary Physics is steadily increasing\nits share in the network core, reflecting a shift in the overall trend of\nPhysics research.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Fourth Generation Nuclear Weapons: Military effectiveness and collateral\n  effects: The paper begins with a general introduction and update to Fourth Generation\nNuclear Weapons (FGNW), and then addresses some particularly important military\naspects on which there has been only limited public discussion so far. These\naspects concern the unique military characteristics of FGNWs which make them\nradically different from both nuclear weapons based on previous-generation\nnuclear-explosives and from conventional weapons based on chemical-explosives:\nyields in the 1 to 100 tons range, greatly enhanced coupling to targets,\npossibility to drive powerful shaped-charge jets and forged fragments, enhanced\nprompt radiation effects, reduced collateral damage and residual radioactivity,\netc.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Information flow reveals prediction limits in online social activity: Modern society depends on the flow of information over online social\nnetworks, and users of popular platforms generate significant behavioral data\nabout themselves and their social ties. However, it remains unclear what\nfundamental limits exist when using these data to predict the activities and\ninterests of individuals, and to what accuracy such predictions can be made\nusing an individual's social ties. Here we show that 95% of the potential\npredictive accuracy for an individual is achievable using their social ties\nonly, without requiring that individual's data. We use information theoretic\ntools to estimate the predictive information within the writings of Twitter\nusers, providing an upper bound on the available predictive information that\nholds for any predictive or machine learning methods. As few as 8-9 of an\nindividual's contacts are sufficient to obtain predictability comparable to\nthat of the individual alone. Distinct temporal and social effects are visible\nby measuring information flow along social ties, allowing us to better study\nthe dynamics of online activity. Our results have distinct privacy\nimplications: information is so strongly embedded in a social network that in\nprinciple one can profile an individual from their available social ties even\nwhen the individual forgoes the platform completely.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Active-travel modelling: a methodological approach to networks for\n  walking and cycling commuting analysis: Walking and cycling, commonly referred to as active travel, have become\nintegral components of modern transport planning. Recently, there has been\ngrowing recognition of the substantial role that active travel can play in\nmaking cities more liveable, sustainable and healthy, as opposed to traditional\nvehicle-centred approaches. This shift in perspective has spurred interest in\ndeveloping new data sets of varying resolution levels to represent, for\ninstance, walking and cycling street networks. This has also led to the\ndevelopment of tailored computational tools and quantitative methods to model\nand analyse active travel flows.\n  In response to this surge in active travel-related data and methods, our\nstudy develops a methodological framework primarily focused on walking and\ncycling as modes of commuting. We explore commonly used data sources and tools\nfor constructing and analysing walking and cycling networks, with a particular\nemphasis on distance as a key factor that influences, describes, and predicts\ncommuting behaviour. Our ultimate aim is to investigate the role of different\nnetwork distances in predicting active commuting flows.\n  To achieve this, we analyse the flows in the constructed networks by looking\nat the detour index of shortest paths. We then use the Greater London Area as a\ncase study, and construct a spatial interaction model to investigate the\nobserved commuting patterns through the different networks. Our results\nhighlight the differences between chosen data sets, the uneven spatial\ndistribution of their performance throughout the city and its consequent effect\non the spatial interaction model and prediction of walking and cycling\ncommuting flows.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Climate impacts of particle physics: The pursuit of particle physics requires a stable and prosperous society.\nToday, our society is increasingly threatened by global climate change.\nHuman-influenced climate change has already impacted weather patterns, and\nglobal warming will only increase unless deep reductions in emissions of CO$_2$\nand other greenhouse gases are achieved. Current and future activities in\nparticle physics need to be considered in this context, either on the moral\nground that we have a responsibility to leave a habitable planet to future\ngenerations, or on the more practical ground that, because of their scale,\nparticle physics projects and activities will be under scrutiny for their\nimpact on the climate. In this white paper for the U.S. Particle Physics\nCommunity Planning Exercise (\"Snowmass\"), we examine several contexts in which\nthe practice of particle physics has impacts on the climate. These include the\nconstruction of facilities, the design and operation of particle detectors, the\nuse of large-scale computing, and the research activities of scientists. We\noffer recommendations on establishing climate-aware practices in particle\nphysics, with the goal of reducing our impact on the climate. We invite members\nof the community to show their support for a sustainable particle physics field\n(https://indico.fnal.gov/event/53795/).",
        "category": "physics_soc-ph"
    },
    {
        "text": "Spectral Anlysis on Explosive Percolation: We study the spectral properties of the process of explosive percolation. In\nparticular, we explore how the maximum eigenvalue of the adjacency matrix of a\nnetwork which governs the spreading efficiency evolves as the density of\nconnection increases. Interestingly, for networks with connectivity that grow\nin an explosive way, information spreading and mass transport are found to be\ncarried out inefficiently. In the conventional explosive percolation models\nthat we studied, the sudden emergences of large-scale connectivity are found to\ncome with relatively lowered efficiency of spreading. Nevertheless, the\nspreading efficiency of the explosive model can be increased by introducing\nheterogeneous structures into the networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Reducing complexity of multiagent systems with symmetry breaking: an\n  application to opinion dynamics with polls: In this paper we investigate the possibility of reducing the complexity of a\nsystem composed of a large number of interacting agents, whose dynamics feature\na symmetry breaking. We consider first order stochastic differential equations\ndescribing the behavior of the system at the particle (i.e., Lagrangian) level\nand we get its continuous (i.e., Eulerian) counterpart via a kinetic\ndescription. However, the resulting continuous model alone fails to describe\nadequately the evolution of the system, due to the loss of granularity which\nprevents it from reproducing the symmetry breaking of the particle system. By\nsuitably coupling the two models we are able to reduce considerably the\nnecessary number of particles while still keeping the symmetry breaking and\nsome of its large-scale statistical properties. We describe such a multiscale\ntechnique in the context of opinion dynamics, where the symmetry breaking is\ninduced by the results of some opinion polls reported by the media.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Concept of Average Loop Impact as a System-Wide Measure of Feedback\n  Dominance Analysis: In system dynamics, the concept of loop impact measures the effect of a\nfeedback loop on the curvature in the behaviour of the stocks in the loop\n(Hayward & Boswell, 2014). It is a ratio measure of the acceleration imparted\nby the loop into a stock and can also be thought of as a ratio measure of the\nforce exerted between adjacent stocks in the loop (Hayward & Roach, 2017). As\nsuch, a loop of n stocks is described by n impacts. Loop impact is used to\nexamine regions of loop dominance in stock behaviour. Sometimes, it is helpful\nto present a dominance analysis for a complete system rather than an individual\nstock. This paper introduces the concept of average loop impact to explore such\na system-wide loop dominance analysis.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Graph Metrics for Temporal Networks: Temporal networks, i.e., networks in which the interactions among a set of\nelementary units change over time, can be modelled in terms of time-varying\ngraphs, which are time-ordered sequences of graphs over a set of nodes. In such\ngraphs, the concepts of node adjacency and reachability crucially depend on the\nexact temporal ordering of the links. Consequently, all the concepts and\nmetrics proposed and used for the characterisation of static complex networks\nhave to be redefined or appropriately extended to time-varying graphs, in order\nto take into account the effects of time ordering on causality. In this chapter\nwe discuss how to represent temporal networks and we review the definitions of\nwalks, paths, connectedness and connected components valid for graphs in which\nthe links fluctuate over time. We then focus on temporal node-node distance,\nand we discuss how to characterise link persistence and the temporal\nsmall-world behaviour in this class of networks. Finally, we discuss the\nextension of classic centrality measures, including closeness, betweenness and\nspectral centrality, to the case of time-varying graphs, and we review the work\non temporal motifs analysis and the definition of modularity for temporal\ngraphs.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Uncovering hidden dependency in weighted networks via information\n  entropy: Interactions between elements, which are usually represented by networks,\nhave to delineate potentially unequal relationships in terms of their relative\nimportance or direction. The intrinsic unequal relationships of such kind,\nhowever, are opaque or hidden in numerous real systems. For instance, when a\nnode in a network with limited interaction capacity spends its capacity to its\nneighboring nodes, the allocation of the total amount of interactions to them\ncan be vastly diverse. Even if such potentially heterogeneous interactions\nepitomized by weighted networks are observable, as a result of the\naforementioned ego-centric allocation of interactions, the relative importance\nor dependency between two interacting nodes can only be implicitly accessible.\nIn this work, we precisely pinpoint such relative dependency by proposing the\nframework to discover hidden dependent relations extracted from weighted\nnetworks. For a given weighted network, we provide a systematic criterion to\nselect the most essential interactions for individual nodes based on the\nconcept of information entropy. The criterion is symbolized by assigning the\neffective number of neighbors or the effective out-degree to each node, and the\nresultant directed subnetwork decodes the hidden dependent relations by leaving\nonly the most essential directed interactions. We apply our methodology to two\ntime-stamped empirical network data, namely the international trade relations\nbetween nations in the world trade web (WTW) and the network of people in the\nhistorical record of Korea, Annals of the Joseon Dynasty (AJD). Based on the\ndata analysis, we discover that the properties of mutual dependency encoded in\nthe two systems are vastly different.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Molecular Communications at the Macroscale: A Novel Framework for\n  Modeling Epidemic Spreading and Mitigation: Using the notion of effective distance proposed by Brockmann and Helbing,\ncomplex spatiotemporal processes of epidemic spreading can be reduced to\ncircular wave propagation patterns with well-defined wavefronts. This hidden\nhomogeneity of contagion phenomena enables the mapping of virtual mobility\nnetworks to physical propagation channels. Subsequently, we propose a novel\ncommunications-inspired model of epidemic spreading and mitigation by\nestablishing the one-to-one correspondence between the essential components\ncomprising information and disease transmissions. The epidemic processes can be\nregarded as macroscale molecular communications, in which individuals are\nmacroscale information molecules carrying messages (epidemiological states). We\nthen present the notions of normalized ensemble-average prevalence (NEAP) and\nprevalence delay profile (PDP) to characterize the relative impact and time\ndifference of all the spreading paths, which are analogous to the classical\ndescription methods of path loss and power delay profile in communications.\nFurthermore, we introduce the metric of root mean square (RMS) delay spread to\nmeasure the distortion of early contagion dynamics caused by multiple infection\ntransmission routes. In addition, we show how social and medical interventions\ncan be understood from the perspectives of various communication modules. The\nproposed framework provides an intuitive, coherent, and efficient approach for\ncharacterization of the disease outbreaks by applying the deep-rooted\ncommunications theories as the analytical lens.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Entropy of dynamical social networks: Human dynamical social networks encode information and are highly adaptive.\nTo characterize the information encoded in the fast dynamics of social\ninteractions, here we introduce the entropy of dynamical social networks. By\nanalysing a large dataset of phone-call interactions we show evidence that the\ndynamical social network has an entropy that depends on the time of the day in\na typical week-day. Moreover we show evidence for adaptability of human social\nbehavior showing data on duration of phone-call interactions that significantly\ndeviates from the statistics of duration of face-to-face interactions. This\nadaptability of behavior corresponds to a different information content of the\ndynamics of social human interactions. We quantify this information by the use\nof the entropy of dynamical networks on realistic models of social\ninteractions.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Velocity-enhanced Cooperation of Moving Agents playing Public Goods\n  Games: In this Brief Report we study the evolutionary dynamics of the Public Goods\nGame in a population of mobile agents embedded in a 2-dimensional space. In\nthis framework, the backbone of interactions between agents changes in time,\nallowing us to study the impact that mobility has on the emergence of\ncooperation in structured populations. We compare our results with a static\ncase in which agents interact on top of a Random Geometric Graph. Our results\npoint out that a low degree of mobility enhances the onset of cooperation in\nthe system while a moderate velocity favors the fixation of the\nfull-cooperative state.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Identification of relevant performance indicators in round-robin\n  tournaments: A myriad of different data are generated to characterize a soccer match. Here\nwe discuss which performance indicators are particularly helpful to forecast\nthe future results of a team via an estimation of the underlying team strengths\nwith minimum statistical uncertainty. We introduce an appropriate statistical\nframework and exemplify it for different performance indicators for the German\npremier soccer league. Two aspects are involved: (i) It is quantified how well\nthe estimation process would work if no statistical noise due to finite\ninformation is present. The related score directly expresses to which degree\nthe chosen performance indicator reflects the underlying team strength. (ii)\nAdditionally, the reduction of the forecasting quality due to statistical noise\nis determined. From both pieces of information a normalized value can be\nconstructed which is a direct measure of the overall forecasting quality. It\nturns out that the so-called packing rate works best. New perspectives of\nperformance indicators enter when trying to understand the outcome of single\nmatches based on match-specific observations from the same match. Implications\nfor the purpose of forecasting as well as consequences for the interpretation\nof team strengths are discussed.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Optimal control of complex networks with conformity behavior: Despite the significant advances in identifying the driver nodes and energy\nrequiring in network control, a framework that incorporates more complicated\ndynamics remains challenging. Here, we consider the conformity behavior into\nnetwork control, showing that the control of undirected networked systems with\nconformity will become easier as long as the number of external inputs beyond a\ncritical point. We find that this critical point is fundamentally determined by\nthe network connectivity. In particular, we investigate the nodal structural\ncharacteristic in network control and propose optimal control strategy to\nreduce the energy requiring in controlling networked systems with conformity\nbehavior. We examine those findings in various synthetic and real networks,\nconfirming that they are prevailing in describing the control energy of\nnetworked systems. Our results advance the understanding of network control in\npractical applications.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The role of intra- and inter-group Matthew effect in the social dilemma\n  of public goods games: The Matthew effect describes the phenomenon where the rich tend to get\nricher. Such a success-driven mechanism has been studied in spatial public\ngoods games in an inter-group way, where each individual's social power is\nenhanced across all groups. For instance, factors like knowledge can exert an\nadvantage across various social contexts. In contrast, certain factors,\nespecially local material goods, only enhance advantages within their current\ngroup. Building on this, we further explore the intra-group Matthew effect\nwhere the enhancement of social power is calculated separately in each group.\nOur findings indicate that the intra-group Matthew effect sustains cooperation\nmore at high productivity, while the inter-group Matthew effect promotes\ncooperation at low productivity. Moreover, the mixture of the intra- and\ninter-group Matthew effect harms cooperation. This study provides insights into\naddressing social dilemmas by adjusting wealth accumulation across diverse\nsocial groups.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Power (Law) of Indian Markets: Analysing NSE and BSE trading\n  statistics: The nature of fluctuations in the Indian financial market is analyzed in this\npaper. We have looked at the price returns of individual stocks, with\ntick-by-tick data from the National Stock Exchange (NSE) and daily closing\nprice data from both NSE and the Bombay Stock Exchange (BSE), the two largest\nexchanges in India. We find that the price returns in Indian markets follow a\nfat-tailed cumulative distribution, consistent with a power law having exponent\n$\\alpha \\sim 3$, similar to that observed in developed markets. However, the\ndistributions of trading volume and the number of trades have a different\nnature than that seen in the New York Stock Exchange (NYSE). Further, the price\nmovement of different stocks are highly correlated in Indian markets.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Comparative model accuracy of a data-fitted generalized Aw-Rascle-Zhang\n  model: The Aw-Rascle-Zhang (ARZ) model can be interpreted as a generalization of the\nLighthill-Whitham-Richards (LWR) model, possessing a family of fundamental\ndiagram curves, each of which represents a class of drivers with a different\nempty road velocity. A weakness of this approach is that different drivers\npossess vastly different densities at which traffic flow stagnates. This\ndrawback can be overcome by modifying the pressure relation in the ARZ model,\nleading to the generalized Aw-Rascle-Zhang (GARZ) model. We present an approach\nto determine the parameter functions of the GARZ model from fundamental diagram\nmeasurement data. The predictive accuracy of the resulting data-fitted GARZ\nmodel is compared to other traffic models by means of a three-detector test\nsetup, employing two types of data: vehicle trajectory data, and sensor data.\nThis work also considers the extension of the ARZ and the GARZ models to models\nwith a relaxation term, and conducts an investigation of the optimal relaxation\ntime.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Italian primary school-size distribution and the city-size: a\n  complex nexus: We characterize the statistical law according to which Italian primary\nschool-size distributes. We find that the school-size can be approximated by a\nlog-normal distribution, with a fat lower tail that collects a large number of\nvery small schools. The upper tail of the school-size distribution decreases\nexponentially and the growth rates are distributed with a Laplace PDF. These\ndistributions are similar to those observed for firms and are consistent with a\nBose-Einstein preferential attachment process. The body of the distribution\nfeatures a bimodal shape suggesting some source of heterogeneity in the school\norganization that we uncover by an in-depth analysis of the relation between\nschools-size and city-size. We propose a novel cluster methodology and a new\nspatial interaction approach among schools which outline the variety of\npolicies implemented in Italy. Different regional policies are also discussed\nshedding lights on the relation between policy and geographical features.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Filter Bubble effect in the multistate voter model: Social media influence online activity by recommending to users content\nstrongly correlated with what they have preferred in the past. In this way they\nconstrain users within filter bubbles that strongly limit their exposure to new\nor alternative content. We investigate this type of dynamics by considering a\nmultistate voter model where, with a given probability $\\lambda$, a user\ninteracts with a \"personalized information\" suggesting the opinion most\nfrequently held in the past. By means of theoretical arguments and numerical\nsimulations, we show the existence of a nontrivial transition between a region\n(for small $\\lambda$) where consensus is reached and a region (above a\nthreshold $\\lambda_c$) where the system gets polarized and clusters of users\nwith different opinions persist indefinitely. The threshold always vanishes for\nlarge system size $N$, showing that consensus becomes impossible for a large\nnumber of users. This finding opens new questions about the side effects of the\nwidespread use of personalized recommendation algorithms.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Network Science approach to Modelling Emergence and Topological\n  Robustness of Supply Networks: A Review and Perspective: Due to the increasingly complex and interconnected nature of global supply\nchain networks (SCNs), a recent strand of research has applied network science\nmethods to model SCN growth and subsequently analyse various topological\nfeatures, such as robustness. This paper provides: (1) a comprehensive review\nof the methodologies adopted in literature for modelling the topology and\nrobustness of SCNs; (2) a summary of topological features of the real world\nSCNs, as reported in various data driven studies; and (3) a discussion on the\nlimitations of existing network growth models to realistically represent the\nobserved topological characteristics of SCNs. Finally, a novel perspective is\nproposed to mimic the SCN topologies reported in empirical studies, through\nfitness based generative network models.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Human language reveals a universal positivity bias: Using human evaluation of 100,000 words spread across 24 corpora in 10\nlanguages diverse in origin and culture, we present evidence of a deep imprint\nof human sociality in language, observing that (1) the words of natural human\nlanguage possess a universal positivity bias; (2) the estimated emotional\ncontent of words is consistent between languages under translation; and (3)\nthis positivity bias is strongly independent of frequency of word usage.\nAlongside these general regularities, we describe inter-language variations in\nthe emotional spectrum of languages which allow us to rank corpora. We also\nshow how our word evaluations can be used to construct physical-like\ninstruments for both real-time and offline measurement of the emotional content\nof large-scale texts.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Cycles of strategies and changes of distribution in public goods game:\n  An experimental investigation: In this communication, a simple mechanism in the optional public goods game\nis experimentally investigated using two experimental settings; and first time,\nthe cyclic strategy pattern in full state space is demonstrated by means of\nvelocity. It is, furthermore, elaborated that the strategies of cooperation,\ndefection and nonparticipant form a Rock-Paper-Scissors type cycle, and the\ncycle of three strategies are persistent over 200 rounds. This cycle is very\nsimilar to the cycle given by evolutionary dynamics e.g. replicator dynamics.\nThe mechanism that nonparticipant can sustain cooperation is driven by the\nRock-Paper-Scissors type of cyclic dominance in the three strategies. That is,\nif the cycle is existent, the cooperation will always sustain. Meanwhile, the\ndistribution of social states changes in the state space and from cooperation\nas the most frequent strategy to defection and, from defection to\nnonparticipant, forms a clear rotation path in a long run. These results seem\nto implicate that the evolutionary dynamics has ability to capture the real\ndynamics applying not only on biosphere, but also on human society.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Two transitions in spatial modular networks: Understanding the resilience of infrastructures such as transportation\nnetwork has significant importance for our daily life. Recently, a homogeneous\nspatial network model was developed for studying spatial embedded networks with\ncharacteristic link length such as power-grids and the brain. However, although\nmany real-world networks are spatially embedded and their links have\ncharacteristics length such as pipelines, power lines or ground transportation\nlines they are not homogeneous but rather heterogeneous. For example, density\nof links within cities are significantly higher than between cities. Here we\npresent and study numerically and analytically a similar realistic\nheterogeneous spatial modular model using percolation process to better\nunderstand the effect of heterogeneity on such networks. The model assumes that\ninside a city there are many lines connecting different locations, while long\nlines between the cities are sparse and usually directly connecting only a few\nnearest neighbours cities in a two dimensional plane. We find that this model\nexperiences two distinct continues transitions, one when the cities disconnect\nfrom each other and the second when each city breaks apart. Although the\ncritical threshold for site percolation in 2D grid remains an open question we\nanalytically find the critical threshold for site percolation in this model. In\naddition, while the homogeneous model experience a single transition having a\nunique phenomenon called \\textit{critical stretching} where a geometric\ncrossover from random to spatial structure in different scales found to stretch\nnon-linearly with the characteristic length at criticality. Here we show that\nthe heterogeneous model does not experience such a phenomenon indicating that\ncritical stretching strongly depends on the network structure.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Naming Game in Social Networks: Community Formation and Consensus\n  Engineering: We study the dynamics of the Naming Game [Baronchelli et al., (2006) J. Stat.\nMech.: Theory Exp. P06014] in empirical social networks. This stylized\nagent-based model captures essential features of agreement dynamics in a\nnetwork of autonomous agents, corresponding to the development of shared\nclassification schemes in a network of artificial agents or opinion spreading\nand social dynamics in social networks. Our study focuses on the impact that\ncommunities in the underlying social graphs have on the outcome of the\nagreement process. We find that networks with strong community structure hinder\nthe system from reaching global agreement; the evolution of the Naming Game in\nthese networks maintains clusters of coexisting opinions indefinitely. Further,\nwe investigate agent-based network strategies to facilitate convergence to\nglobal consensus.",
        "category": "physics_soc-ph"
    },
    {
        "text": "How to project a bipartite network?: The one-mode projecting is extensively used to compress the bipartite\nnetworks. Since the one-mode projection is always less informative than the\nbipartite representation, a proper weighting method is required to better\nretain the original information. In this article, inspired by the network-based\nresource-allocation dynamics, we raise a weighting method, which can be\ndirectly applied in extracting the hidden information of networks, with\nremarkably better performance than the widely used global ranking method as\nwell as collaborative filtering. This work not only provides a creditable\nmethod in compressing bipartite networks, but also highlights a possible way\nfor the better solution of a long-standing challenge in modern information\nscience: How to do personal recommendation?",
        "category": "physics_soc-ph"
    },
    {
        "text": "Estimation of crowd density applying wavelet transform and machine\n  learning: We conducted a simple experiment in which one pedestrian passed through a\ncrowded area and measured the body-rotational angular velocity with commercial\ntablets. Then, we developed a new method for predicting crowd density by\napplying the continuous wavelet transform and machine learning to the data\nobtained in the experiment. We found that the accuracy of prediction using\nangular velocity data was as high as that using raw velocity data. Therefore,\nwe concluded that angular velocity has relationship with crowd density and we\ncould estimate crowd density by angular velocity. Our research will contribute\nto management of safety and comfort of pedestrians by developing an easy way to\nmeasure crowd density.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Generalized gravity model for human migration: The gravity model (GM) analogous to Newton's law of universal gravitation has\nsuccessfully described the flow between different spatial regions, such as\nhuman migration, traffic flows, international economic trades, etc. This simple\nbut powerful approach relies only on the 'mass' factor represented by the scale\nof the regions and the 'geometrical' factor represented by the geographical\ndistance. However, when the population has a subpopulation structure\ndistinguished by different attributes, the estimation of the flow solely from\nthe coarse-grained geographical factors in the GM causes the loss of\ndifferential geographical information for each attribute. To exploit the full\ninformation contained in the geographical information of subpopulation\nstructure, we generalize the GM for population flow by explicitly harnessing\nthe subpopulation properties characterized by both attributes and geography. As\na concrete example, we examine the marriage patterns between the bride and the\ngroom clans of Korea in the past. By exploiting more refined geographical and\nclan information, our generalized GM properly describes the real data, a part\nof which could not be explained by the conventional GM. Therefore, we would\nlike to emphasize the necessity of using our generalized version of the GM,\nwhen the information on such nongeographical subpopulation structures is\navailable.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Modeling a vehicular traffic network. Part I: We propose three models for the traffic of vehicles within a network formed\nby sites (cities, car-rental agencies, parking lots, etc.) and connected by\ntwo-way arteries (roads, highways), that allow forecasting the vehicular flux\nin a sequence of $n$ consecutive steps, or units of time. An essential approach\nconsists in using, as an \"a priori\" information, previous observations and\nmeasurements. The formal tools used in our analysis consists in: (1)\nassociating a digraph to the network where the edges correspond to arteries and\nthe vertices with loops represent the sites. (2) From an initial set of\nnumbers, that are the distribution of vehicles within the network, we construct\na matrix that we transform into a stochastic matrix (SM) by normalizing the\nrows, whose entries are now transition probabilities. This matrix becomes the\ngenerator of the evolution of the traffic flow. And (3), we use the\nPerron-Frobenius theory for a formal analysis. We investigate three models: (a)\na closed four-site network having a conserved number of vehicles; (b) to this\nnetwork we add an influx and an outflux of vehicles to characterize an open\nsystem; asymptotically, $n \\rightarrow \\infty$, the SM raised to the power $n$\ngoes to a unique stationary matrix. And (c), we construct a nonlinear model\nbecause the formal structure permits the existence of several ($L$) stationary\nstates for the distribution of vehicles at each site, that alternate cyclically\nwith time. Each state represents the traffic for $L$ different moments. These\nmodels were used to analyze the traffic in a sector of the city of Tigre,\nlocated in the province of Buenos Aires, Argentina. The results are presented\nin a following paper.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A New Methodology of Spatial Crosscorrelation Analysis: The idea of spatial crosscorrelation was conceived of long ago. However,\nunlike the related spatial autocorrelation, the theory and method of spatial\ncrosscorrelation analysis have remained undeveloped. This paper presents a set\nof models and working methods for spatial crosscorrelation analysis. By analogy\nwith Moran's index newly expressed in a spatial quadratic form and by means of\nmathematical reasoning, I derive a theoretical framework for geographical\ncrosscorrelation analysis. First, two sets of spatial crosscorrelation\ncoefficients are defined, including a global spatial crosscorrelation\ncoefficient and a set of local spatial crosscorrelation coefficients. Second, a\npair of scatterplots of spatial crosscorrelation is proposed, and different\nscatterplots show different relationships between correlated variables. Based\non the spatial crosscorrelation coefficient, Pearson's correlation coefficient\ncan be decomposed into two parts: direct correlation (partial crosscorrelation)\nand indirect correlation (spatial crosscorrelation). As an example, the\nanalytical process is applied to the relationships between China's urbanization\nand economic development. Spatial crosscorrelation and spatial autocorrelation\ncan complement one another, and the spatial crosscorrelation scatterplots can\nbe used to reveal the causality inside a self-organized system. The spatial\ncrosscorrelation models will play a useful role in future geographical spatial\nanalysis.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Stochastic Transition Model for Discrete Agent Movements: We propose a calibrated two-dimensional cellular automaton model to simulate\npedestrian motion behavior. It is a v=4 (3) model with exclusion statistics and\nrandom shuffled dynamics. The underlying regular grid structure results in a\ndirection-dependent behavior, which has in particular not been considered\nwithin previous approaches. We efficiently compensate these grid-caused\ndeficiencies on model level.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Economic exchanges in a stratified society: End of the middle class?: We study the effect of the social stratification on the wealth distribution\non a system of interacting economic agents that are constrained to interact\nonly within their own economic class. The economical mobility of the agents is\nrelated to its success in exchange transactions. Different wealth distributions\nare obtained as a function of the width of the economic class. We find a range\nof widths in which the society is divided in two classes separated by a deep\ngap that prevents further exchange between poor and rich agents. As a\nconsequence, the middle wealth class is eliminated. The high values of the Gini\nindices obtained in these cases indicate a highly unequal society. On the other\nhand, lower and higher widths induce lower Gini indices and a fairer wealth\ndistribution.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Betweenness centrality in dense spatial networks: The betweenness centrality (BC) is an important quantity for understanding\nthe structure of complex large networks. However, its calculation is in general\ndifficult and known in simple cases only. In particular, the BC has been\nexactly computed for graphs constructed over a set of $N$ points in the\ninfinite density limit, displaying a universal behavior. We reconsider this\ncalculation and propose an expansion for large and finite densities. We compute\nthe lowest non-trivial order and show that it encodes how straight are shortest\npaths and is therefore non-universal and depends on the graph considered. We\ncompare our analytical result to numerical simulations obtained for various\ngraphs such as the minimum spanning tree, the nearest neighbor graph, the\nrelative neighborhood graph, the random geometric graph, the Gabriel graph, or\nthe Delaunay triangulation. We show that in most cases the agreement with our\nanalytical result is excellent even for densities of points that are relatively\nlow. This method and our results provide a framework for understanding and\ncomputing this important quantity in large spatial networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A framework for evaluating complex networks measurements: A good deal of current research in complex networks involves the\ncharacterization and/or classification of the topological properties of given\nstructures, which has motivated several respective measurements. This letter\nproposes a framework for evaluating the quality of complex network measurements\nin terms of their effective resolution, degree of degeneracy and\ndiscriminability. The potential of the suggested approach is illustrated with\nrespect to comparing the characterization of several model and real-world\nnetworks by using concentric and symmetry measurements. The results indicate a\nmarkedly superior performance for the latter type of mapping.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Topologically-induced suppression of explosive synchronization: Nowadays, explosive synchronization is a well documented phenomenon occurring\nin networks when the node frequency and its degree are correlated. This\nfirst-order transition, which may coexists with classical synchronization, has\nbeen recently causally linked to some pathological brain states like epilepsy\nand fibromyalgia. It is then intriguing how most of neuronal systems can\noperate in normal conditions avoiding explosive synchronization. Here, we have\ndiscovered that synchronization in networks where the oscillators are coupled\nvia degree-biased Laplacian operators, naturally controls the transition from\nexplosive to standard synchronization in neuronal-like systems. We prove\nanalytically that explosive synchronization emerges when using this theoretical\nsetting in star-like (neuronal) networks. As soon as this star-like network is\ntopologically converted to a network containing cycles, e.g., via synaptic\nconnections to other neurons, the explosive synchronization gives rise to\nclassical synchronization. This allows us to hypothesize that such topological\ncontrol of explosive synchronization could be a mechanism for the brain to\nnaturally work in normal, non-pathological, conditions.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Financial fire sales as continuous-state complex contagion: Trading activities in financial systems create various channels through which\nsystemic risk can propagate. An important contagion channel is financial fire\nsales, where a bank failure causes asset prices to fall due to asset\nliquidation, which in turn drives further bank defaults, triggering the next\nrounds of liquidation. This process can be considered as complex contagion, yet\nit cannot be modeled using the conventional binary-state contagion models\nbecause there is a continuum of states representing asset prices. Here, we\ndevelop a threshold model of continuous-state cascades in which the states of\neach node are represented by real values. We show that the solution of a\nmulti-state contagion model, for which the continuous states are discretized,\naccurately replicates the simulated continuous state distribution as long as\nthe number of states is moderately large. This discretization approach allows\nus to exploit the power of approximate master equations (AME) to trace the\ntrajectory of the fraction of defaulted banks and obtain the distribution of\nasset prices that characterize the dynamics of fire sales on asset-bank\nbipartite networks. We examine the accuracy of the proposed method using real\ndata on asset-holding relationships in exchange-traded funds (ETFs).",
        "category": "physics_soc-ph"
    },
    {
        "text": "Cost-effective implementation of the Paris Agreement using flexible\n  greenhouse gas metrics: Greenhouse gas (GHG) metrics, that is, conversion factors to evaluate the\nemissions of non-CO2 climate forcers on a common scale with CO2, serve crucial\nfunctions upon the implementation of the Paris Agreement. While different\nmetrics have been proposed, their economic cost-effectiveness has not been\ninvestigated under a range of pathways, including those temporarily missing or\nsignificantly overshooting the temperature targets of the Paris Agreement. Here\nwe show that cost-effective metrics for methane that minimize the overall cost\nof climate mitigation are time-dependent, primarily determined by the pathway,\nand strongly influenced by temperature overshoot. The Paris Agreement will\nimplement the conventional 100-year Global Warming Potential (GWP100), a good\napproximation of cost-effective metrics for the coming decades. In the longer\nterm, however, we suggest that parties consider adapting the choice of common\nmetrics to the future pathway as it unfolds, as part of the global stocktake,\nif cost-effectiveness is a key consideration.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Military Strategy in a Complex World: A strategy is a plan, method, or series of actions for obtaining a specified\ngoal. A military strategy typically employs the threat or use of military force\nin opposition to an adversary, and is called upon where large scale force is\nviewed as the way to achieve such goals. Strategic thinking is traditionally\nfocused on which part or combination of land, air, and naval forces is most\neffective. This may be too narrow an approach to accomplishing the ultimate\nend, which is generally political influence or control--or preventing influence\nor control by others--and almost never consists of physical destruction itself.\nIn order to broaden the discussion of military strategy, we consider here three\ndistinct effects of inflicting stress on an opponent: a) A fragile system is\ndamaged--possibly catastrophically, b) A robust system is largely unaffected,\nretaining much or all of its prior strength, c) Some systems actually gain\nstrength, a property which has recently been termed antifragility. Traditional\nperspectives of military strategy implicitly assume fragility, limiting their\nvalidity and resulting in surprise, and assume a specific end state rather than\nan overall condition of the system as a goal. Robustness and antifragility are\nrelevant both to offense and defense. While robustness and antifragility are\ndesirable in friendly systems, an enemy possessing these characteristics\nundermines the premise that an attack will achieve a desired increase in\ncontrol. Historical and contemporary examples demonstrate the failure of\ntraditional strategies against antifragile enemies--even devastating damage\ninflicted upon nations or other organizations did not weaken and defeat them,\nbut rather strengthened them, resulting in their victory. Underlying such\nsuccessful responses are socio-economic or political strengths. Our discussion\nis a basis for scientific analysis of improved strategic thinking.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Multiplexing induced explosive synchronization in Kuramoto oscillators\n  with inertia: Explosive synchronization (ES) of coupled oscillators on networks is shown to\nbe originated from existence of correlation between natural frequencies of\noscillators and degrees of corresponding nodes. Here, we demonstrate that ES is\na generic feature of multiplex network of second-order Kuramoto oscillators and\ncan exist in absence of a frequency-degree correlation. A monoplex network of\nsecond-order Kuramoto oscillators bearing homogeneous (heterogeneous)\ndegree-distribution is known to display the first-order (second-order)\ntransition to synchronization. We report that multiplexing of two such networks\nhaving homogeneous degree-distribution support the first-order transition in\nboth the layers thereby facilitating ES. More interesting is the multiplexing\nof a layer bearing heterogeneous degree-distribution with another layer bearing\nhomogeneous degree-distribution, which induces a first-order (ES) transition in\nthe heterogeneous layer which was incapable of showing the same in the\nisolation. Further, we report that such induced ES transition in the\nheterogeneous layer of multiplex networks can be controlled by varying inter\nand intra-layer coupling strengths. Our findings emphasize on importance of\nmultiplexing or impact of one layer on dynamical evolution of other layers of\nsystems having inherent multiplex or multilevel architecture.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Micro-economic Analysis of the Physical Constrained Markets: Game Theory\n  Application to Competitive Electricity Markets: Competition has been introduced in the electricity markets with the goal of\nreducing prices and improving efficiency. The basic idea which stays behind\nthis choice is that, in competitive markets, a greater quantity of the good is\nexchanged at a lower and a lower price, leading to higher market efficiency.\nElectricity markets are pretty different from other commodities mainly due to\nthe physical constraints related to the network structure that may impact the\nmarket performance. The network structure of the system on which the economic\ntransactions need to be undertaken poses strict physical and operational\nconstraints. Strategic interactions among producers that game the market with\nthe objective of maximizing their producer surplus must be taken into account\nwhen modeling competitive electricity markets. The physical constraints,\nspecific of the electricity markets, provide additional opportunity of gaming\nto the market players. Game theory provides a tool to model such a context.\nThis paper discussed the application of game theory to physical constrained\nelectricity markets with the goal of providing tools for assessing the market\nperformance and pinpointing the critical network constraints that may impact\nthe market efficiency. The basic models of game theory specifically designed to\nrepresent the electricity markets will be presented. IEEE30 bus test system of\nthe constrained electricity market will be discussed to show the network\nimpacts on the market performances in presence of strategic bidding behavior of\nthe producers.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Estimating the prevalence of malicious extraterrestrial civilizations: This paper attempts to provide an estimation of the prevalence of hostile\nextraterrestrial civilizations through an extrapolation of the probability that\nwe, as the human civilization, would attack or invade an inhabited exoplanet\nonce we become a Type-1 civilization in the Kardashev Scale capable of nearby\ninterstellar travel. The estimation is based on the world's history of\ninvasions in the last century, the military capabilities of the countries\ninvolved, and the global growth rate of energy consumption. Upper limits of\nstandard deviations are used in order to obtain the estimated probability of\nextraterrestrial invasion by a civilization whose planet we send a message to.\nResults show that such probability is two orders of magnitude lower than the\nimpact probability of a planet-killer asteroid. These findings could serve as a\nstarting point for an international debate about sending the first serious\ninterstellar radio messages to nearby potentially habitable planets.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Directed network comparison using motifs: Analyzing and characterizing the differences between networks is a\nfundamental and challenging problem in network science. Previously, most\nnetwork comparison methods that rely on topological properties have been\nrestricted to measuring differences between two undirected networks. However,\nmany networks, such as biological networks, social networks, and transportation\nnetworks, exhibit inherent directionality and higher-order attributes that\nshould not be ignored when comparing networks. Therefore, we propose a\nmotif-based directed network comparison method that captures local, global, and\nhigher-order differences between two directed networks. Specifically, we first\nconstruct a motif distribution vector for each node, which captures the\ninformation of a node's involvement in different directed motifs. Then, the\ndissimilarity between two directed networks is defined on the basis of a matrix\nwhich is composed of the motif distribution vector of every node and\nJensen-Shannon divergence. The performance of our method is evaluated via the\ncomparison of six real directed networks with their null models as well as\ntheir perturbed networks based on edge perturbation. Our method is superior to\nthe state-of-the-art baselines and is robust with different parameter settings.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Good Fences: The Importance of Setting Boundaries for Peaceful\n  Coexistence: We consider the conditions of peace and violence among ethnic groups, testing\na theory designed to predict the locations of violence and interventions that\ncan promote peace. Characterizing the model's success in predicting peace\nrequires examples where peace prevails despite diversity. Switzerland is\nrecognized as a country of peace, stability and prosperity. This is surprising\nbecause of its linguistic and religious diversity that in other parts of the\nworld lead to conflict and violence. Here we analyze how peaceful stability is\nmaintained. Our analysis shows that peace does not depend on integrated\ncoexistence, but rather on well defined topographical and political boundaries\nseparating groups. Mountains and lakes are an important part of the boundaries\nbetween sharply defined linguistic areas. Political canton and circle\n(sub-canton) boundaries often separate religious groups. Where such boundaries\ndo not appear to be sufficient, we find that specific aspects of the population\ndistribution either guarantee sufficient separation or sufficient mixing to\ninhibit intergroup violence according to the quantitative theory of conflict.\nIn exactly one region, a porous mountain range does not adequately separate\nlinguistic groups and violent conflict has led to the recent creation of the\ncanton of Jura. Our analysis supports the hypothesis that violence between\ngroups can be inhibited by physical and political boundaries. A similar\nanalysis of the area of the former Yugoslavia shows that during widespread\nethnic violence existing political boundaries did not coincide with the\nboundaries of distinct groups, but peace prevailed in specific areas where they\ndid coincide. The success of peace in Switzerland may serve as a model to\nresolve conflict in other ethnically diverse countries and regions of the\nworld.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A Quantum-like Model for Predicting Human Decisions in the Entangled\n  Social Systems: Human-centered systems of systems such as social networks, Internet of\nThings, or healthcare systems are growingly becoming major facets of modern\nlife. Realistic models of human behavior in such systems play a significant\nrole in their accurate modeling and prediction. Yet, human behavior under\nuncertainty often violates the predictions by the conventional probabilistic\nmodels. Recently, quantum-like decision theories have shown a considerable\npotential to explain the contradictions in human behavior by applying quantum\nprobability. But providing a quantum-like decision theory that could predict,\nrather than describe the current, state of human behavior is still one of the\nunsolved challenges. The main novelty of our approach is introducing an\nentangled Bayesian network inspired by the entanglement concept in quantum\ninformation theory, in which each human is a part of the entire society.\nAccordingly, society's effect on the dynamic evolution of the decision-making\nprocess, which is less often considered in decision theories, is modeled by the\nentanglement measures. The proposed predictive entangled quantum-like Bayesian\nnetwork (PEQBN) is evaluated on 22 experimental tasks. Results confirm that\nPEQBN provides more realistic predictions of human decisions under uncertainty,\nwhen compared with classical Bayesian networks and three recent quantum-like\napproaches.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Citations and gender diversity in reciprocal acknowledgement networks: Acknowledgements in scientific articles suggest not only gratitude, but also\nthe interactions among scientists. In this study, we examine the\nacknowledgement interactions employing data from open-access journals (PLOS\nseries). We built an acknowledgement network where the nodes represent authors\nand acknowledged people, while the links correspond to being mentioned in\nacknowledgements. Employing motif analysis, we showed how acknowledgement\nnetworks have developed, and how reciprocal relationships tend to emerge. To\nbetter understand these reciprocal relationships, we analysed the reciprocal\nsub-graphs of acknowledgement from two perspectives: citations and gender\ndiversity. Firstly, we counted the number of citations, from both reciprocal\nand non-reciprocal authors. We found that reciprocal authors predominantly tend\nto cite other reciprocal authors rather than non-reciprocal ones. For gender\ndiversity, we found that reciprocal pairs that include females tend to emerge\nmore than male-male pairs of reciprocity in various fields, despite the fewer\nnumber of females.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Heterogeneous impact of a lockdown on inter-municipality mobility: Without a vaccine, the fight against the spreading of the coronavirus has\nfocused on maintaining physical distance. To study the impact of such measures\non inter-municipality traffic, we analyze a mobile dataset with the daily flow\nof people in Portugal in March and April 2020. We find that the reduction in\ninter-municipality traffic depends strongly on its initial outflow. In\nmunicipalities where the mobility is low, the outflow reduced by $10-20\\%$ and\nthis decrease was independent of the population size. Whereas, for\nmunicipalities of high mobility, the reduction was a monotonic increasing\nfunction of the population size and it even exceeded $60\\%$ for the largest\nmunicipalities. As a consequence of such heterogeneities, there were\nsignificant structural changes on the most probable paths for the spreading of\nthe virus, which must be considered when modeling the impact of control\nmeasures.",
        "category": "physics_soc-ph"
    },
    {
        "text": "How to efficiently destroy a network with limited information: We address the general problem of how best to attack and destroy a network by\nnode removal, given limited or no prior information about the edges. We\nconsider a family of strategies in which nodes are randomly chosen, but not\nremoved. Instead, a random acquaintance (i.e., a first neighbour) of the chosen\nnode is removed from the network. By assigning an informal cost to the\ninformation about the network structure, we show using cost-benefit analysis\nthat acquaintance removal is the optimal strategy to destroy networks\nefficiently.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The universality of physical images at relative timescales on multiplex\n  networks: The duration of the accumulation rate (physical image) is a key factor in\nanalysis of counterintuitive phenomena involving relative timescales on\nmultiplex networks. Typically, the relative timescales are represented by\nmultiplying any layer by the same factor. However, researchers often overlook\nthe changes in the relative timescales caused by local parameters, resulting in\nincomplete analysis of phenomena. This paper examines the survival time of\nstifler individuals in the information-epidemic model on multiplex networks.\nThe relative timescales can be affected by the survival time (only one\nparameter), reversing the monotonically increasing phenomenon into a\nmonotonically decreasing one, that is, a counterintuitive phenomenon under\nincomplete analysis. Additionally, the relative timescales can influence the\nepidemic threshold, which is different from the previous studies. Our work\nsuggests that considering the physical image of relative timescales is crucial\nwhen analyzing multiplex networks, even when only one parameter is altered.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Dense Power-law Networks and Simplicial Complexes: There is increasing evidence that dense networks occur in on-line social\nnetworks, recommendation networks and in the brain. In addition to being dense,\nthese networks are often also scale-free, i.e. their degree distributions\nfollow $P(k)\\propto k^{-\\gamma}$ with $\\gamma\\in(1,2]$. Models of growing\nnetworks have been successfully employed to produce scale-free networks using\npreferential attachment, however these models can only produce sparse networks\nas the numbers of links and nodes being added at each time-step is constant.\nHere we present a modelling framework which produces networks that are both\ndense and scale-free. The mechanism by which the networks grow in this model is\nbased on the Pitman-Yor process. Variations on the model are able to produce\nundirected scale-free networks with exponent $\\gamma=2$ or directed networks\nwith power-law out-degree distribution with tunable exponent $\\gamma \\in\n(1,2)$. We also extend the model to that of directed $2$-dimensional simplicial\ncomplexes. Simplicial complexes are generalization of networks that can encode\nthe many body interactions between the parts of a complex system and as such\nare becoming increasingly popular to characterize different data sets ranging\nfrom social interacting systems to the brain. Our model produces dense directed\nsimplicial complexes with power-law distribution of the generalized out-degrees\nof the nodes.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Multi-Species Cohesion: Humans, machinery, AI and beyond: What large-scale cohesive behaviors -- desirable or dangerous -- can suddenly\nemerge from systems with interacting humans, machinery and software including\nAI? When will they emerge? How will they evolve and be controlled? Here we\noffer some answers to these urgent questions by introducing an aggregation\nmodel that accounts for entities' inter- and intra-species diversities. It\nyields a novel multi-dimensional generalization of existing aggregation\nphysics. We derive exact analytic solutions for the time-to-cohesion and\ngrowth-of-cohesion for two species, and some generalizations for an arbitrary\nnumber of species. These solutions reproduce -- and offer a microscopic\nexplanation for -- an anomalous nonlinear growth feature observed in related\nreal-world systems, e.g. Hamas-Hezbollah online support, human-machine team\ninteractions, AI-determined topic coherence. A key takeaway is that good and\nbad 'surprises' will appear increasingly quickly as humans-machinery-AI etc.\nmix more -- but the theory offers a rigorous approach for understanding and\ncontrolling this.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Distributional trends in the generation and end-use sector of low-carbon\n  hydrogen plants: This paper uses established and recently introduced methods from the applied\nmathematics and statistics literature to study trends in the end-use sector and\ncapacity of low-carbon hydrogen projects in recent and upcoming decades. First,\nwe examine distributions in plants over time for various end-use sectors and\nclassify them according to metric discrepancy, observing clear similarity\nacross all industry sectors. Next, we compare the distribution of usage sectors\namong different continents and examine the changes in sector distribution over\ntime. Finally, we judiciously apply several regression models to analyse the\nassociation between various predictors and the capacity of global hydrogen\nprojects. Across our experiments, we see a welcome exponential growth in the\ncapacity of zero-carbon hydrogen plants and significant growth of new and\nplanned hydrogen plants in the 2020's across every sector.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Entropy-based randomisation of rating networks: In the last years, due to the great diffusion of e-commerce, online rating\nplatforms quickly became a common tool for purchase recommendations. However,\ninstruments for their analysis did not evolve at the same speed. Indeed,\ninteresting information about users' habits and tastes can be recovered just\nconsidering the bipartite network of users and products, in which links have\ndifferent weights due to the score assigned to items. With respect to other\nweighted bipartite networks, in these systems we observe a maximum possible\nweight per link, that limits the variability of the outcomes. In the present\narticle we propose an entropy-based randomisation of (bipartite) rating\nnetworks by extending the Configuration Model framework: the randomised network\nsatisfies the constraints of the degree per rating, i.e. the number of given\nratings received by the specified product or assigned by the single user. We\nfirst show that such a null model is able to reproduce several non-trivial\nfeatures of the real network better than other null models. Then, using it as a\nbenchmark, we project the information contained in the real system on one of\nthe layers, showing, for instance, the division in communities of music albums\ndue to the taste of customers, or, in movies due the audience.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Suppressing disease spreading by using information diffusion on\n  multiplex networks: Although there is always an interplay between the dynamics of information\ndiffusion and disease spreading, the empirical research on the systemic\ncoevolution mechanisms connecting these two spreading dynamics is still\nlacking. Here we investigate the coevolution mechanisms and dynamics between\ninformation and disease spreading by utilizing real data and a proposed\nspreading model on multiplex network. Our empirical analysis finds asymmetrical\ninteractions between the information and disease spreading dynamics. Our\nresults obtained from both the theoretical framework and extensive stochastic\nnumerical simulations suggest that an information outbreak can be triggered in\na communication network by its own spreading dynamics or by a disease outbreak\non a contact network, but that the disease threshold is not affected by\ninformation spreading. Our key finding is that there is an optimal information\ntransmission rate that markedly suppresses the disease spreading. We find that\nthe time evolution of the dynamics in the proposed model qualitatively agrees\nwith the real-world spreading processes at the optimal information transmission\nrate.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Statistical Physics of Design: A key challenge in complex design problems that permeate science and\nengineering is the need to balance design objectives for specific design\nelements or subsystems with global system objectives. Global objectives give\nrise to competing design pressures, whose effects can be difficult to trace in\nsubsystem design. Here, using examples from arrangement problems, we show that\nthe systems-level application of statistical physics principles, which we term\n\"systems physics\", provides a detailed characterization of subsystem design in\nterms of the concepts of stress and strain from materials physics. We analyze\ninstances of routing problems in naval architectures, and show that systems\nphysics provides a direct means of classifying architecture types, and\nquantifying trade-offs between subsystem- and overall performance. Our approach\ngeneralizes straightforwardly to design problems in a wide range of other\ndisciplines that require concrete understanding of how the pressure to meet\noverall design objectives drives the outcomes for component subsystems.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Punishment and inspection for governing the commons in a\n  feedback-evolving game: Utilizing common resources is always a dilemma for community members. While\ncooperator players restrain themselves and consider the proper state of\nresources, defectors demand more than their supposed share for a higher payoff.\nTo avoid the tragedy of the common state, punishing the latter group seems to\nbe an adequate reaction. This conclusion, however, is less straightforward when\nwe acknowledge the fact that resources are finite and even a renewable resource\nhas limited growing capacity. To clarify the possible consequences, we consider\na coevolutionary model where beside the payoff-driven competition of cooperator\nand defector players the level of a renewable resource depends sensitively on\nthe fraction of cooperators and the total consumption of all players. The\napplied feedback-evolving game reveals that beside a delicately adjusted\npunishment it is also fundamental that cooperators should pay special attention\nto the growing capacity of renewable resources. Otherwise, even the usage of\ntough punishment cannot save the community from an undesired end.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Has the Relationship between Urban and Suburban Automobile Travel\n  Changed across Generations? Comparing Millennials and Generation Xers in the\n  United States: Using U.S. nationwide travel surveys for 1995, 2001, 2009 and 2017, this\nstudy compares Millennials with their previous generation (Gen Xers) in terms\nof their automobile travel across different neighborhood patterns. At the age\nof 16 to 28 years old, Millennials have lower daily personal vehicle miles\ntraveled and car trips than Gen Xers in urban (higher-density) and suburban\n(lower-density) neighborhoods. Such differences remain unchanged after\nadjusting for the socio-economic, vehicle ownership, life cycle, year-specific\nand regional-specific factors. In addition, the associations between\nresidential density and automobile travel for the 16- to 28-year-old\nMillennials are flatter than that for Gen Xers, controlling for the\naforementioned covariates. These generational differences remain for the 24- to\n36-year-old Millennials, during the period when the U.S. economy was recovering\nfrom the recession. These findings show that, in both urban and suburban\nneighborhoods, Millennials in the U.S. are less auto-centric than the previous\ngeneration during early life stages, regardless of economic conditions. Whether\nsuch difference persists over later life stages remains an open question and is\nworth continuous attention.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Perspective on attractive-repulsive interactions in dynamical networks:\n  progress and future: Emerging collective behavior in complex dynamical networks depends on both\ncoupling function and underlying coupling topology. Through this perspective,\nwe provide a brief yet profound excerpt of recent research efforts that explore\nhow the synergy of attractive and repulsive interactions influence the destiny\nof ensembles of interacting dynamical systems. We review the incarnation of\ncollective states ranging from chimera or solitary states to extreme events and\noscillation quenching arising as a result of different network arrangements.\nThough the existing literature demonstrates that many of the crucial\ndevelopments have been made, nonetheless, we come up with significant routes of\nfurther research in this field of study.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Robustness of a Network Formed of Spatially Embedded Networks: We present analytic and numeric results for percolation in a network formed\nof interdependent spatially embedded networks. We show results for a treelike\nand a random regular network of networks each with $(i)$ unconstrained\ninterdependent links and $(ii)$ interdependent links restricted to a maximum\nlength, $r$. Analytic results are given for each network of networks with\nunconstrained dependency links and compared with simulations. For the case of\ntwo spatially embedded networks it was found that only for $r>r_c\\approx8$ does\nthe system undergo a first order phase transition. We find that for treelike\nnetworks of networks $r_c$ significantly decreases as $n$ increases and rapidly\nreaches its limiting value, $r=1$. For cases where the dependencies form loops,\nsuch as in random regular networks, we show analytically and confirm through\nsimulations, that there is a certain fraction of dependent nodes, $q_{max}$,\nabove which the entire network structure collapses even if a single node is\nremoved. This $q_{max}$ decreases quickly with $m$, the degree of the random\nregular network of networks. Our results show the extreme sensitivity of\ncoupled spatial networks and emphasize the susceptibility of these networks to\nsudden collapse. The theory derived here can be used to find the robustness of\nany network of networks where the profile of percolation of a single network is\nknown.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Modeling Website Popularity Competition in the Attention-Activity\n  Marketplace: How does a new startup drive the popularity of competing websites into\noblivion like Facebook famously did to MySpace? This question is of great\ninterest to academics, technologists, and financial investors alike. In this\nwork we exploit the singular way in which Facebook wiped out the popularity of\nMySpace, Hi5, Friendster, and Multiply to guide the design of a new popularity\ncompetition model. Our model provides new insights into what Nobel Laureate\nHerbert A. Simon called the \"marketplace of attention,\" which we recast as the\nattention-activity marketplace. Our model design is further substantiated by\nuser-level activity of 250,000 MySpace users obtained between 2004 and 2009.\nThe resulting model not only accurately fits the observed Daily Active Users\n(DAU) of Facebook and its competitors but also predicts their fate four years\ninto the future.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Bonabeau model on a fully connected graph: Numerical simulations are reported on the Bonabeau model on a fully connected\ngraph, where spatial degrees of freedom are absent. The control parameter is\nthe memory factor f. The phase transition is observed at the dispersion of the\nagents power h_i. The critical value f_C shows a hysteretic behavior with\nrespect to the initial distribution of h_i. f_C decreases with the system size;\nthis decrease can be compensated by a greater number of fights between a global\nreduction of the distribution width of h_i. The latter step is equivalent to a\npartial forgetting.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Heterogeneous resource allocation can change social hierarchy in public\n  goods games: Public Goods Games represent one of the most useful tools to study group\ninteractions between individuals. However, even if they could provide an\nexplanation for the emergence and stability of cooperation in modern societies,\nthey are not able to reproduce some key features observed in social and\neconomical interactions. The typical shape of wealth distribution - known as\nPareto Law - and the microscopic organization of wealth production are two of\nthem. Here, we introduce a modification to the classical formulation of Public\nGoods Games that allows for the emergence of both of these features from first\nprinciples. Unlike traditional Public Goods Games on networks, where players\ncontribute equally to all the games in which they participate, we allow\nindividuals to redistribute their contribution according to what they earned in\nprevious rounds. Results from numerical simulations show that not only a Pareto\ndistribution for the payoffs naturally emerges but also that if players don't\ninvest enough in one round they can act as defectors even if they are formally\ncooperators. Finally, we also show that the players self-organize in a very\nproductive backbone that covers almost perfectly the minimum spanning tree of\nthe underlying interaction network. Our results not only give an explanation\nfor the presence of the wealth heterogeneity observed in real data but also\npoints to a conceptual change regarding how cooperation is defined in\ncollective dilemmas.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Process of price formation and the skewness of asset returns: Distributions of assets returns exhibit a slight skewness. In this note we\nshow that our model of endogenous price formation \\cite{Reimann2006} creates an\nasymmetric return distribution if the price dynamics are a process in which\nconsecutive trading periods are dependent from each other in the sense that\nopening prices equal closing prices of the former trading period. The\ncorresponding parameter $\\alpha$ is estimated from daily prices from 01/01/1999\n- 12/31/2004 for 9 large indices. For the S&P 500, the skewness distribution of\nall its constituting assets is also calculated. The skewness distribution due\nto our model is compared with the distribution of the empirical skewness values\nof the ingle assets.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Quantum Finance: Quantum theory is used to model secondary financial markets. Contrary to\nstochastic descriptions, the formalism emphasizes the importance of trading in\ndetermining the value of a security. All possible realizations of investors\nholding securities and cash is taken as the basis of the Hilbert space of\nmarket states. The temporal evolution of an isolated market is unitary in this\nspace. Linear operators representing basic financial transactions such as cash\ntransfer and the buying or selling of securities are constructed and simple\nmodel Hamiltonians that generate the temporal evolution due to cash flows and\nthe trading of securities are proposed. The Hamiltonian describing financial\ntransactions becomes local when the profit/loss from trading is small compared\nto the turnover. This approximation may describe a highly liquid and efficient\nstock market. The lognormal probability distribution for the price of a stock\nwith a variance that is proportional to the elapsed time is reproduced for an\nequilibrium market. The asymptotic volatility of a stock in this case is\nrelated to the long-term probability that it is traded.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The physics of randomness and regularities for languages (lifetimes,\n  family trees, and the second languages); in terms of random matrices: The physics of randomness and regularities for languages (mother tongues) and\ntheir lifetimes and family trees and for the second languages are studied in\nterms of two opposite processes; random multiplicative noise [1], and\nfragmentation [2], where the original model is given in the matrix format. We\nstart with a random initial world, and come out with the regularities, which\nmimic various empirical data [3] for the present languages.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Complexity science approach to economic crime: In this comment we discuss how complexity science and network science are\nparticularly useful for identifying and describing the hidden traces of\neconomic misbehaviour such as fraud and corruption.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Multirelational Organization of Large-scale Social Networks in an Online\n  World: The capacity to collect fingerprints of individuals in online media has\nrevolutionized the way researchers explore human society. Social systems can be\nseen as a non-linear superposition of a multitude of complex social networks,\nwhere nodes represent individuals and links capture a variety of different\nsocial relations. Much emphasis has been put on the network topology of social\ninteractions, however, the multi-dimensional nature of these interactions has\nlargely been ignored in empirical studies, mostly because of lack of data.\nHere, for the first time, we analyze a complete, multi-relational, large social\nnetwork of a society consisting of the 300,000 odd players of a massive\nmultiplayer online game. We extract networks of six different types of\none-to-one interactions between the players. Three of them carry a positive\nconnotation (friendship, communication, trade), three a negative (enmity, armed\naggression, punishment). We first analyze these types of networks as separate\nentities and find that negative interactions differ from positive interactions\nby their lower reciprocity, weaker clustering and fatter-tail degree\ndistribution. We then proceed to explore how the inter-dependence of different\nnetwork types determines the organization of the social system. In particular\nwe study correlations and overlap between different types of links and\ndemonstrate the tendency of individuals to play different roles in different\nnetworks. As a demonstration of the power of the approach we present the first\nempirical large-scale verification of the long-standing structural balance\ntheory, by focusing on the specific multiplex network of friendship and enmity\nrelations.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Evolution of Fairness in the Not Quite Ultimatum Game: The Ultimatum Game (UG) is an economic game where two players (proposer and\nresponder) decide how to split a certain amount of money. While traditional\neconomic theories based on rational decision making predict that the proposer\nshould make a minimal offer and the responder should accept it, human subjects\ntend to behave more fairly in UG. Previous studies suggested that extra\ninformation such as reputation, empathy, or spatial structure is needed for\nfairness to evolve in UG. Here we show that fairness can evolve without\nadditional information if players make decisions probabilistically and may\ncontinue interactions when the offer is rejected, which we call the Not Quite\nUltimatum Game (NQUG). Evolutionary simulations of NQUG showed that the\nprobabilistic decision making contributes to the increase of proposers' offer\namounts to avoid rejection, while the repetition of the game works to\nresponders' advantage because they can wait until a good offer comes. These\nsimple extensions greatly promote evolution of fairness in both proposers'\noffers and responders' acceptance thresholds.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Oligarchy as a Phase Transition: The effect of wealth-attained advantage\n  in a Fokker-Planck description of asset exchange: In earlier work, we derived a nonlinear, nonlocal Fokker-Planck equation for\nthe Yard-Sale Model of asset exchange. In the absence of redistribution, we\nshowed that the Gini coefficient is a Lyapunov functional for this model,\ntending to one in the time-asymptotic limit, corresponding to maximal\ninequality. When a one-parameter model of redistribution is introduced, we\nshowed that the model admits a steady state similar to Pareto's Law.\n  In this work, we analyze the form of this distribution in greater detail,\nboth analytically and numerically. We find that, while Pareto's Law is\napproximately valid for low redistribution, it gives way to something like\nGibrat's Law at higher redistribution. We also prove that, while this Pareto or\nGibrat behavior persists over many orders of magnitude, it ultimately gives way\nto gaussian decay at extremely large wealth.\n  Following the work of Moukarzel et al., we introduce a bias in favor of the\nwealthier agent. We derive the corresponding modification to the Fokker-Planck\nequation, and we show this leads to wealth condensation when the bias exceeds a\ncritical value. Earlier work took the bias to be a discontinuous function of\nthe wealth differential between the two transacting agents, and reported a\nfirst-order phase transition to absolute oligarchy. By contrast, in this work\nwe take the bias to be a continuous function of the wealth differential, and\nconsequently we observe a second-order phase transition with a region of\ncoexistence between the oligarch and a distribution of non-oligarchs.\n  We additionally show that the onset of wealth condensation has a reciprocal\neffect on the character of the non-oligarchical part of the distribution.\nSpecifically, we show that the above-mentioned gaussian decay at extremely\nlarge wealth is valid both above and below criticality, but degenerates to\nexponential decay precisely at criticality.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Multiobjective Optimization and Phase Transitions: Many complex systems obey to optimality conditions that are usually not\nsimple. Conflicting traits often interact making a Multi Objective Optimization\n(MOO) approach necessary. Recent MOO research on complex systems report about\nthe Pareto front (optimal designs implementing the best trade-off) in a\nqualitative manner. Meanwhile, research on traditional Simple Objective\nOptimization (SOO) often finds phase transitions and critical points. We\nsummarize a robust framework that accounts for phase transitions located\nthrough SOO techniques and indicates what MOO features resolutely lead to phase\ntransitions. These appear determined by the shape of the Pareto front, which at\nthe same time is deeply related to the thermodynamic Gibbs surface. Indeed,\nthermodynamics can be written as an MOO from where its phase transitions can be\nparsimoniously derived; suggesting that the similarities between transitions in\nMOO-SOO and Statistical Mechanics go beyond mere coincidence.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Reconstruction of Science Phylogeny: We are facing a real challenge when coping with the continuous acceleration\nof scientific production and the increasingly changing nature of science. In\nthis article, we extend the classical framework of co-word analysis to the\nstudy of scientific landscape evolution. Capitalizing on formerly introduced\nscience mapping methods with overlapping clustering, we propose methods to\nreconstruct phylogenetic networks from successive science maps, and give\ninsight into the various dynamics of scientific domains. Two indexes - the\npseudo-inclusion and the empirical quality - are introduced to qualify\nscientific fields and are used for reconstruction validation purpose.\nPhylogenetic dynamics appear to be strongly correlated to these two indexes,\nand to a weaker extent, to a third one previously introduced (density index).\nThese results suggest that there exist regular patterns in the \"life cycle\" of\nscientific fields. The reconstruction of science phylogeny should improve our\nglobal understanding of science evolution and pave the way toward the\ndevelopment of innovative tools for our daily interactions with its\nproductions. Over the long run, these methods should lead quantitative\nepistemology up to the point to corroborate or falsify theoretical models of\nscience evolution based on large-scale phylogeny reconstruction from databases\nof scientific literature.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Snowmass '21 Community Engagement Frontier 6: Public Policy and\n  Government Engagement: Congressional Advocacy for HEP Funding (The \"DC\n  Trip''): This document has been prepared as a Snowmass contributed paper by the Public\nPolicy \\& Government Engagement topical group (CEF06) within the Community\nEngagement Frontier. The charge of CEF06 is to review all aspects of how the\nHigh Energy Physics (HEP) community engages with government at all levels and\nhow public policy impacts members of the community and the community at large,\nand to assess and raise awareness within the community of direct\ncommunity-driven engagement of the U.S. federal government (\\textit{i.e.}\nadvocacy). The focus of this paper is the advocacy undertaken by the HEP\ncommunity that pertains directly to the funding of the field by the U.S.\nfederal government.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Detecting the community structure and activity patterns of temporal\n  networks: a non-negative tensor factorization approach: The increasing availability of temporal network data is calling for more\nresearch on extracting and characterizing mesoscopic structures in temporal\nnetworks and on relating such structure to specific functions or properties of\nthe system. An outstanding challenge is the extension of the results achieved\nfor static networks to time-varying networks, where the topological structure\nof the system and the temporal activity patterns of its components are\nintertwined. Here we investigate the use of a latent factor decomposition\ntechnique, non-negative tensor factorization, to extract the community-activity\nstructure of temporal networks. The method is intrinsically temporal and allows\nto simultaneously identify communities and to track their activity over time.\nWe represent the time-varying adjacency matrix of a temporal network as a\nthree-way tensor and approximate this tensor as a sum of terms that can be\ninterpreted as communities of nodes with an associated activity time series. We\nsummarize known computational techniques for tensor decomposition and discuss\nsome quality metrics that can be used to tune the complexity of the factorized\nrepresentation. We subsequently apply tensor factorization to a temporal\nnetwork for which a ground truth is available for both the community structure\nand the temporal activity patterns. The data we use describe the social\ninteractions of students in a school, the associations between students and\nschool classes, and the spatio-temporal trajectories of students over time. We\nshow that non-negative tensor factorization is capable of recovering the class\nstructure with high accuracy. In particular, the extracted tensor components\ncan be validated either as known school classes, or in terms of correlated\nactivity patterns, i.e., of spatial and temporal coincidences that are\ndetermined by the known school activity schedule.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Locating the source of interacting signal in complex networks: We investigate the problem of locating the source of a self-interacting\nsignal spreading in a complex networks. We use a well-known rumour model as an\nexample of the process with self-interaction. According to this model based on\nthe SIR epidemic dynamics, the infected nodes may interact and discourage each\nother from gossiping with probability $\\alpha$. We compare three algorithms of\nsource localization: Limited Pinto-Thiran-Vettarli (LPTV), Gradient Maximum\nLikelihood (GMLA) and one based on Pearson correlation between time and\ndistance. The results of numerical simulations show that additional\ninteractions between infected nodes decrease the quality of LPTV and Pearson.\nGMLA is the most resistant to harmful effects of the self-interactions, which\nis especially visible for medium and high level of stochasticity of the\nprocess, when spreading rate is below 0.5. The reason for this may be the fact\nthat GMLA uses only the nearest observers, which are much less likely affected\nby the interactions between infected nodes, because these contacts become\nimportant as the epidemics develops and the number of infected agents\nincreases.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Heterogeneous contributions can jeopardize cooperation in the Public\n  Goods Game: When studying social dilemma games, a crucial question arises regarding the\nimpact of general heterogeneity on cooperation, which has been shown to have\npositive effects in numerous studies. Here, we demonstrate that heterogeneity\nin the contribution value for the focal Public Goods Game can jeopardize\ncooperation. We show that there is an optimal contribution value in the\nhomogeneous case that most benefits cooperation depending on the lattice. In a\nheterogeneous scenario, where strategy and contribution coevolve, cooperators\nmaking contributions higher than the optimal value end up harming those who\ncontribute lower. This effect is notably detrimental to cooperation in the\nsquare lattice with von Neumann neighborhood, while it can have no impact in\nothers lattices. Furthermore, in parameter regions where a higher-contributing\ncooperator cannot normally survive alone, the exploitation of lower value\ncontribution cooperators allows their survival, resembling a parasitic\nbehavior. To obtain these results, we employed various distributions for the\ncontribution values in the initial condition and conducted Monte Carlo\nsimulations.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Stochastic evolutionary dynamics of minimum-effort coordination games: The minimum-effort coordination game, having potentially important\nimplications in both evolutionary biology and sociology, draws recently more\nattention for the fact that human behavior in this social dilemma is often\ninconsistent with the predictions of classic game theory. In the framework of\nclassic game theory, any common effort level is a strict and trembling hand\nperfect Nash equilibrium, so that no desideratum is provided for selecting\namong them. Behavior experiments, however, show that the effort levels employed\nby subjects are inversely related to the effort costs. Here, we combine\ncoalescence theory and evolutionary game theory to investigate this game in\nfinite populations. Both analytic results and individual-based simulations show\nthat effort costs play a key role in the evolution of contribution levels,\nwhich is in good agreement with those observed experimentally. Besides\nwell-mixed populations, set structured populations, where the population\nstructure itself is a consequence of the evolutionary process, have also been\ntaken into consideration. Therein we find that large number of sets and\nmoderate migration rate greatly promote effort levels, especially for high\neffort costs. Our results may provide theoretical explanations for coordination\nbehaviors observed in real life from an evolutionary perspective.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Evolutions of Individuals Use of Lyon's Bike Sharing System: Bike sharing systems (BSS) have been growing fast all over the world, along\nwith the number of articles analyzing such systems. However the lack of\ntemporally large trip databases has limited the analysis of BSS users behavior\nin the long term. This article studies the V'elo'v - a BSS located in Lyon,\nFrance - subscribers commitment in the long term and the evolution of their\nusage over time. Using a 5-year dataset covering 121,000 long-term distinct\nusers, we show the heterogeneous individual trajectories masked by the overall\nsystem stability. Users follow two main trajectories: about 60% remain in the\nsystem for at most one year, showing a low median activity (47 trips); the\nremaining 40% correspond to more active users (median activity of 96 trips in\ntheir first year) that remain continuously active for several years (mean time\n= 2.9 years). This latter class exhibits a relatively stable activity,\ndecreasing slightly over the years. We show that middle-aged, male and urban\nusers are over represented among the 'stable' users.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Statistical mechanics of human resource allocation: A mathematical\n  modeling of job-matching in labor markets: We provide a mathematical model to investigate the human resource allocation\nproblem for agents, say, university graduates who are looking for their\npositions in labor markets. The basic model is described by the so-called Potts\nspin glass which is well-known in the research field of statistical physics. In\nthe model, each Potts spin (a tiny magnet in atomic scale length) represents\nthe action of each student, and it takes a discrete variable corresponding to\nthe company he/she applies for. We construct the energy to include three\ndistinct effects on the students' behavior, namely, collective effect, market\nhistory and international ranking of companies. In this model system, the\ncorrelations (the adjacent matrix) between students are taken into account\nthrough the pairwise spin-spin interactions. We carry out computer simulations\nto examine the efficiency of the model. We also show that some chiral\nrepresentation of the Potts spin enables us to obtain some analytical insights\ninto our labor markets.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Hidden dependence of spreading vulnerability on topological complexity: Many dynamical phenomena in complex systems concern spreading that plays out\non top of networks with changing architecture over time -- commonly known as\ntemporal networks. A complex system's proneness to facilitate spreading\nphenomena, which we abbreviate as its `spreading vulnerability', is often\nsurmised to be related to the topology of the temporal network featured by the\nsystem. Yet, cleanly extracting spreading vulnerability of a complex system\ndirectly from the topological information of the temporal network remains a\nchallenge. Here, using data from a diverse set of real-world complex systems,\nwe develop the `entropy of temporal entanglement' as a novel and insightful\nquantity to measure topological complexities of temporal networks. We show that\nthis parameter-free quantity naturally allows for topological comparisons\nacross vastly different complex systems. Importantly, by simulating three\ndifferent types of stochastic dynamical processes playing out on top of\ntemporal networks, we demonstrate that the entropy of temporal entanglement\nserves as a quantitative embodiment of the systems' spreading vulnerability,\nirrespective of the details of the processes. In being able to do so, i.e., in\nbeing able to quantitatively extract a complex system's proneness to facilitate\nspreading phenomena from topology, this entropic measure opens itself for\napplications in a wide variety of natural, social, biological and engineered\nsystems.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Minimum curvilinear automata with similarity attachment for network\n  embedding and link prediction in the hyperbolic space: The idea of minimum curvilinearity (MC) is that the hidden geometry of\ncomplex networks, in particular when they are sufficiently sparse, clustered,\nsmall-world and heterogeneous, can be efficiently navigated using the minimum\nspanning tree (MST), which is a greedy navigator. The local topological\ninformation drives the global geometrical navigation and the MST can be\ninterpreted as a growing path that greedily maximizes local similarity between\nthe nodes attached at each step by globally minimizing their overall distances\nin the network. This is also valid in absence of the network structure and in\npresence of only the nodes geometrically located over the network generative\nmanifold in a high-dimensional space. We know that random geometric graphs in\nthe hyperbolic space are an adequate model for realistic complex networks: the\nexplanation of this connection is that complex networks exhibit hierarchical,\ntree-like organization, and in turn the hyperbolic geometry is the geometry of\ntrees. Here we show that, according to a mechanism that we define similarity\nattachment, the visited node sequence of a network automaton can efficiently\napproximate the nodes' angular coordinates in the hyperbolic disk, that\nactually represent an ordering of their similarities. This is a consequence of\nthe fact that the MST, during its greedy growing process, at each step\nsequentially attaches the node most similar (less distant) to its own cohort.\nMinimum curvilinear automata (MCA) displays embedding accuracy which seems\nsuperior to HyperMap-CN and inferior to coalescent embedding, however its link\nprediction performance on real networks is without precedent for methods based\non the hyperbolic space. Finally, depending on the data structure used to build\nthe MST, the MCA's time complexity can also approach a linear dependence from\nthe number of edges.",
        "category": "physics_soc-ph"
    },
    {
        "text": "German Cities with Universities: Socioeconomic Position and University\n  Performance: We investigated the role of universities in the prosperity of cities and\nregions. Performance characteristics of universities are derived from the\nLeiden Ranking 2020. The socioeconomic strength of a city is determined with\nthe urban scaling methodology. Our study shows a significant relation between\nthe presence of a university in a city and its socioeconomic indicators,\nparticularly for larger cities, and that this is especially the case for\nuniversities with higher values of their output, impact and collaboration\nindicators.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Science and the Future: Introduction: The contradiction between physical and economical sciences concerning the\ngrowth of the production/consumption mechanism is analyzed. It is then shown\nthat if one wishes to keep the security level stable or to enhance it in a\ngrowing economy the cost of security grows faster than the gross wealth. The\nresult is a typical evolution in which the net wealth increases up to a\nmaximum, then abruptly collapses. Besides this, any system of relations based\non a growing volume of exchanges is bound to go progressively out of control.\nThe voluntary blindness of the ruling classes toward these facts is leading our\nsocieties to a disaster. This fate is not inescapable provided we learn to\ndismantle the myth of perpetual growth.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Tracing the evolution of physics with a keyword co-occurrence network: Describing the evolution of science is a salient work not only for revealing\nthe scientific trend but also for establishing a scientific classification\nsystem. In this paper, we investigate the evolution of science by observing the\nstructure and change of keyword co-occurrence networks. Starting from seven\ntarget physics fields and their initial keywords selected by experts from the\nKorean Physical Society, we generate keyword co-occurrence networks better to\ncapture topological structure with our proposed approach. In this way, we can\nconstruct a more relevant and abundant keyword network from a small set of\ninitial keywords. With these networks, we successfully identify the scientific\nsub-field by detecting communities and extracting core keywords of each\ncommunity. Furthermore, we trace the temporal evolution of sub-fields with the\ntime-snapshot keyword network, the resultant temporal change of the community\nmembership explains the evolution of the research field well. Our approach for\ntracing the evolution of the research field with a keyword co-occurrence\nnetwork can shed light on identifying and assessing the evolution of science.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Sequences of purchases in credit card data reveal life styles in urban\n  populations: Zipf-like distributions characterize a wide set of phenomena in physics,\nbiology, economics and social sciences. In human activities, Zipf-laws describe\nfor example the frequency of words appearance in a text or the purchases types\nin shopping patterns. In the latter, the uneven distribution of transaction\ntypes is bound with the temporal sequences of purchases of individual choices.\nIn this work, we define a framework using a text compression technique on the\nsequences of credit card purchases to detect ubiquitous patterns of collective\nbehavior. Clustering the consumers by their similarity in purchases sequences,\nwe detect five consumer groups. Remarkably, post checking, individuals in each\ngroup are also similar in their age, total expenditure, gender, and the\ndiversity of their social and mobility networks extracted by their mobile phone\nrecords. By properly deconstructing transaction data with Zipf-like\ndistributions, this method uncovers sets of significant sequences that reveal\ninsights on collective human behavior.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Carta a academia: por uma refunda\u00e7\u00e3o do CNPQ e da ci\u00eancia\n  brasileira: In this ``Letter'', I do introduce my point of view about the role of science\nin the development and organization of our societies, emphasizing that science\nis too far from our societies. In Brazil we follow a way similarly to that of\nUSA and France concerning this phenomenon, marked by the lack of participation\nof our academies and scientific community in politics and economy, i.e. we can\nsee that strategic decisions are been hold by only electoral bias and, when\nthere is the presence of academic science, usually it is restricted to the\nfields of economical, political and social sciences. I conclude that the\nstrategic planning of a country, or of the planet as a whole, needs that\nacademy stay close of society, been this one necessary both not enough\ncondition to the proper functioning of any democracy. In the Brazil case more\nspecifically, I propose that this political change in our academies begin by\none profound thought about the Brazilian Science with a consequent refoundation\nof the CNPQ (Nacional Council for Science and Technology development).",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Donation-Payment Gift Card Concept: how to give twice with one card: Standard economic theory, starting with Adam Smith's invisible hand, holds\nthat those who trade for their own selfish motives of maximizing their private\npreferences may contribute more to the public wealth than those who claim\naltruistic motives. Under restrictive conditions, this has been shown to result\nfrom a self-organizing mechanism acting at the global system level, which\nensures that optimal allocation of resources derives from competition within\nthe rule of law. In a nutshell, according to this view, being competitively\nselfish adds value for all. But to what degree can doing good add competitive\nvalue to products and services? We propose the creation of the Donation-Payment\ncard as a solution to the legal and moral issues plaguing the emerging\nanonymous, direct-to-consumer, stored-value prepaid (SVP) card market. Our\nproposal to redirect millions of dollars whose status is in conflict not only\nimproves society and empowers people, but also represents the best free-market\nsolution to the SVP-Cards industry. The Donation-Payment card kills two birds\nwith one stone by marrying business interests with societal benefits. In\naddition, think of the state of mind of a grand-parent giving a SVP-card to a\ngrand-child, who also feels like \"killing two birds with one stone\" or should\nwe say more aptly feels like \"giving twice with one card\".",
        "category": "physics_soc-ph"
    },
    {
        "text": "Intrinsic Harmonic Spaces: A Solution to the Ancient Problem of Perfect\n  Tuning: In this article we solve this ancient problem of perfect tuning in all keys\nand present a system were all harmonies are conserved at once. It will become\nclear, when we expose our solution, why this solution could not be found in the\nway in which earlier on musicians and scientist have been approaching the\nproblem. We follow indeed a different approach. We first construct a\nmathematical representation of the complete harmony by means of a vector space,\nwhere the different tones are represented in complete harmonic way for all keys\nat once. One of the essential differences with earlier systems is that tones\nwill no longer be ordered within an octave, and we find the octave-like\nordering back as a projection of our system. But it is exactly by this\nprojection procedure that the possibility to create a harmonic system for all\nkeys at once is lost. So we see why the old way of ordering tones within an\noctave could not lead to a solution of the problem. We indicate in which way a\nreal musical instrument could be built that realizes our harmonic scheme.\nBecause tones are no longer ordered within an octave such a musical instrument\nwill be rather unconventional. It is however a physically realizable musical\ninstrument, at least for the Pythagorean harmony. We also indicate how perfect\nharmonies of every dimension could be realized by computers.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Dynamics of latent voters: We study the effect of latency on binary-choice opinion formation models.\nLatency is introduced into the models as an additional dynamic rule: after a\nvoter changes its opinion, it enters a waiting period of stochastic length\nwhere no further changes take place. We first focus on the voter model and show\nthat as a result of introducing latency, the average magnetization is not\nconserved, and the system is driven toward zero magnetization, independently of\ninitial conditions. The model is studied analytically in the mean-field case\nand by simulations in one dimension. We also address the behavior of the\nMajority Rule model with added latency, and show that the competition between\nimitation and latency leads to a rich phenomenology.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Nature of thermodynamics equation of state towards economics equation of\n  state: This work critics on nature of thermodynamics coordinates and on roles of the\nvariables in the equation of state (EoS). Coordinate variables in the EoS are\nanalyzed so that central concepts are noticed and are used to lay a foundation\nin building of a new EoS or in testing EoS status of a newly constructed\nempirical equation. With these concepts, we classify EoS into two classes. We\nfind that the EoS of market with unitary price demand and linear\nprice-dependent supply function proposed by \\cite{GumjMarket}, is not an EoS\nbecause it has only one degree of freedom.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Future of Nuclear Energy: Facts and Fiction: An update using\n  2009/2010 Data: An update of our 2009 study, \"The Future of Nuclear Energy, Facts and\nFiction\" using the 2009 and the available 2010 data, including a critical look\nat the just published 2009 edition of the Red Book, is presented. Since January\n2009, eight reactors with a capacity of 4.9 GWe have been connected to the\nelectric grid and four older reactors, with a combined capacity of 2.64 GWe\nhave been terminated. Furthermore, 27 reactor constructions, dominated by China\n(18) and Russia (4), have been initiated.\n  The nuclear fission produced electric energy in 2009 followed the slow\ndecline, observed since 2007, with a total production of 2560 TWhe, 41 TWhe\n(1.6%) less than in 2008 and roughly 100 TWhe less than in the record year\n2006. The preliminary data from the first 10 months of 2010 in the OECD\ncountries indicate that nuclear power production in North-America remained at\nthe 2009 levels, while one observes a recovery in Europe with an increase of\n2.5% and a strong rise of 5% in the OECD Asia-Pacific area compared to the same\nperiod in 2009.\n  Worldwide uranium mining has increased during 2009 by about 7000 tons to\nalmost 51000 tons. Still roughly 18000 tons of the 2010 world uranium\nrequirements need to be provided from the civilian and military reserves.\n  Perhaps the most remarkable new data from the just published 2009 edition of\nthe Red Book, are that (1) the best understood RAR (reasonable assured) and IR\n(inferred) resources, with a price tag of less than 40 US dollars/Kg, have been\ninconsistently absorbed in the two to three times higher price categories and\n(2) uranium mining in Kazakhstan is presented with a short lifetime. The\npresented mining capacity numbers indicate an uranium extraction peak of 28000\ntons during the years 2015-2020, from which it will decline quickly to 14000\ntons by 2025 and to only 5000-6000 tons by 2035.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Scaling the h-index for different scientific ISI fields: We propose a simple way to put in a common scale the h values of researchers\nworking in different scientific ISI fields, so that the previsible misuse of\nthis index for inter-areas comparison might be prevented, or at least,\nalleviated.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Order in Innovation: Is calendar time the true clock of innovation? By combining complexity\nscience with innovation economics and using vaccine datasets containing over\nthree million citations and eight regulatory authorisations, we discover that\ncalendar time and network order describe innovation progress at varying\naccuracy. First, we present a method to establish a mathematical link between\ntechnological evolution and complex networks. The result is a path of events\nthat narrates innovation bottlenecks. Next, we quantify the position and\nproximity of documents to these innovation paths and find that research, by and\nlarge, proceed from basic research, applied research, development, to\ncommercialisation. By extension, we are able to causally quantify the\nparticipation of innovation funders. When it comes to vaccine innovation,\ndiffusion-oriented entities are preoccupied with basic, later-stage research;\nbiopharmaceuticals tend to participate in applied development activities and\nclinical trials at the later-stage; while mission-oriented entities tend to\ninitiate early-stage research. Future innovation programs and funding\nallocations would benefit from better understanding innovation orders.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Optimal frequency setting of metro services in the age of COVID-19\n  distancing measures: Public transport is one of the most disrupted sectors of the COVID-19\npandemic with reported ridership drops up to 90% in majorly affected countries.\nAs many government authorities strive to partially resume activities, public\ntransport operators are in an urgent need for models that can evaluate the\nimpact of different social distancing policies on operational and\npassenger-related costs. In this study, we introduce a mixed-integer quadratic\nprogramming model for the redesign of public transport services considering the\noperational, passenger, and revenue loss-related costs by evaluating the\neffects of different social distancing policies. Our model is applied at the\nmetro network of Washington D.C. and provides optimal redistribution of\nvehicles across lines for different social distancing scenarios. This model can\nbe used as a decision support tool by other policymakers and public transport\noperators that are in need of evaluating the costs related to the\nimplementation of different social distancing policies.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Lost options commitment: how short-term policies affect long-term scope\n  of action: We propose to explore the sustainability of climate policies based on a novel\ncommitment metric. This metric allows to quantify how future generations' scope\nof action is affected by short-term climate policy. In an example application,\nwe show that following a moderate emission scenario like SSP2-4.5 could commit\nfuture generations to heavily rely on carbon dioxide removal or/and solar\nradiation modification to avoid unmanageable sea level rise.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Stability of Transient Relationships: In contrast to long-term relationships, far less is known about the temporal\nevolution of transient relationships, although these constitute a substantial\nfraction of people's communication networks. Previous literature suggests that\nratings of relationship emotional intensity decay gradually until the\nrelationship ends. Using mobile phone data from three countries (US, UK, and\nItaly), we demonstrate that the volume of communication between ego and its\ntransient alters does not display such a systematic decay, instead showing a\nlack of any dominant trends. This means that the communication volume of egos\nto groups of similar transient alters is stable. We show that alters with\nlonger lifetimes in ego's network receive more calls, with the lifetime of the\nrelationship being predictable from call volume within the first few weeks of\nfirst contact. This is observed across all three countries, which include\nsamples of egos at different life stages. The relation between early call\nvolume and lifetime is consistent with the suggestion that individuals\ninitially engage with a new alter so as to evaluate their potential as a tie in\nterms of homophily.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Self-isolation or borders closing: what prevents epidemic spreading\n  better?: Pandemic distribution of COVID-19 in the world has motivated us to discuss\ncombined effects of network clustering and adaptivity on epidemic spreading. We\naddress the question concerning the choice of optimal mechanism for most\neffective prohibiting disease propagation in a connected network: adaptive\nclustering, which mimics self-isolation (SI) in local communities, or sharp\ninstant clustering, which looks like frontiers closing (FC) between cities and\ncountries. SI-networks are \"adaptively grown\" under condition of maximization\nof small cliques in the entire network, while FC-networks are \"instantly\ncreated\". Running the standard SIR model on clustered SI- and FC-networks, we\ndemonstrate that the adaptive network clustering prohibits the epidemic\nspreading better than the instant clustering in the network with similar\nparameters. We found that SI model has scale-free property for degree\ndistribution $P(k)\\sim k^{\\eta}$ with small critical exponent $-2<\\eta<-1$ and\nargue that scale-free behavior emerges due to the randomness in the initial\ndegree distributions and is absent for random regular graphs.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Optimizing state change detection in functional temporal networks\n  through dynamic community detection: Dynamic community detection provides a coherent description of network\nclusters over time, allowing one to track the growth and death of communities\nas the network evolves. However, modularity maximization, a popular method for\nperforming multilayer community detection, requires the specification of an\nappropriate null model as well as resolution and interlayer coupling\nparameters. Importantly, the ability of the algorithm to accurately detect\ncommunity evolution is dependent on the choice of these parameters. In\nfunctional temporal networks, where evolving communities reflect changing\nfunctional relationships between network nodes, it is especially important that\nthe detected communities reflect any state changes of the system. Here, we\npresent analytical work suggesting that a uniform null model provides improved\nsensitivity to the detection of small evolving communities in temporal\ncorrelation networks. We then propose a method for increasing the sensitivity\nof modularity maximization to state changes in nodal dynamics by modeling\nself-identity links between layers based on the self-similarity of the network\nnodes between layers. This method is more appropriate for functional temporal\nnetworks from both a modeling and mathematical perspective, as it incorporates\nthe dynamic nature of network nodes. We motivate our method based on\napplications in neuroscience where network nodes represent neurons and\nfunctional edges represent similarity of firing patterns in time. Finally, we\nshow that in simulated data sets of neuronal spike trains, updating interlayer\nlinks based on the firing properties of the neurons provides superior community\ndetection of evolving network structure when group of neurons change their\nfiring properties over time.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Stratified economic exchange on networks: We investigate a model of stratified economic interactions between agents\nwhen the notion of spatial location is introduced. The agents are placed on a\nnetwork with near-neighbor connections. Interactions between neighbors can\noccur only if the difference in their wealth is less than a threshold value\nthat defines the width of the economic classes. By employing concepts from\nspatiotemporal dynamical systems, three types of patterns can be identified in\nthe system as parameters are varied: laminar, intermittent and turbulent\nstates. The transition from the laminar state to the turbulent state is\ncharacterized by the activity of the system, a quantity that measures the\naverage exchange of wealth over long times. The degree of inequality in the\nwealth distribution for different parameter values is characterized by the Gini\nCoefficient. High levels of activity are associated to low values of the Gini\ncoefficient. It is found that the topological properties of the network have\nlittle effect on the activity of the system, but the Gini coefficient increases\nwhen the clustering coefficient of the network is increased.",
        "category": "physics_soc-ph"
    },
    {
        "text": "On the heterogeneity of urban expansion profiles in Europe: The difference of a city's artificial land use (ALU) radial profile to the\naverage ALU profile is examined for 585 European cities. Using Urban Atlas 2012\ndata, a radial (or monocentric) approach is used to calculate a city's land use\nprofile in relation to distance to the city centre. A scaling law is used which\ncontrols for city size and population. As a consequence, cities of varying\ndegrees of size can be contrasted in a comparable way. Utilising the mean ALU\nprofile for the entire sample of 585 cities, the difference to the mean profile\nis calculated for each city. Using these differences allows us to examine\nheterogeneity of the ALU across European cities but also examine these\ndifferences within cities. We utilise city groupings by city size and country\nto attempt to understand these differences. Combining Urban Atlas and Corine\nLand Cover data, the impact of water on the ALU profiles is examined. A city\nclassification is also introduced which considers the difference to the average\ncurve. Ordering methods are used to visualise cities within these\nclassifications. Results highlight the level of heterogeneity between cities.\nRemoving water, we can see that the cities with the highest levels of water\nhave a higher level of ALU on average. Spain and France are found to have\ncontrasting levels of ALU, Spanish cities having below average ALU and France\nabove average. Using seriation techniques enables us to group and order cities\ninto a typology which can be used to benchmark cities.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Multifractality of complex networks: We demonstrate analytically and numerically the possibility that the fractal\nproperty of a scale-free network cannot be characterized by a unique fractal\ndimension and the network takes a multifractal structure. It is found that the\nmass exponents $\\tau(q)$ for several deterministic, stochastic, and real-world\nfractal scale-free networks are nonlinear functions of $q$, which implies that\nstructural measures of these networks obey the multifractal scaling. In\naddition, we give a general expression of $\\tau(q)$ for some class of fractal\nscale-free networks by a mean-field approximation. The multifractal property of\nnetwork structures is a consequence of large fluctuations of local node density\nin scale-free networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Stability of Synchrony against Local Intermittent Fluctuations in\n  Tree-like Power Grids: 80% of all Renewable Energy Power in Germany is installed in tree-like\ndistribution grids. Intermittent power fluctuations from such sources introduce\nnew dynamics into the lower grid layers. At the same time, distributed\nresources will have to contribute to stabilize the grid against these\nfluctuations in the future. In this paper, we model a system of distributed\nresources as oscillators on a tree-like, lossy power grid and its ability to\nwithstand desynchronization from localized intermittent renewable infeed.\n  We find a remarkable interplay of network structure and the position of the\nnode at which the fluctuations are fed in. An important precondition for our\nfindings is the presence of losses in distribution grids. Then, the most\nnetwork central node splits the network into branches with different influence\non network stability. Troublemakers, i.e. nodes at which fluctuations are\nespecially exciting the grid, tend to be downstream branches with high net\npower outflow. For low coupling strength, we also find branches of nodes\nvulnerable to fluctuations anywhere in the network. These network regions can\nbe predicted at high confidence using an eigenvector based network measure\ntaking the turbulent nature of perturbations into account.\n  While we focus here on tree-like networks, the observed effects also appear,\nalbeit less pronounced, for weakly meshed grids. On the other hand the observed\neffects disappear for lossless power grids often studied in the complex systems\nliterature.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Can machine learning predict citizen-reported angler behavior?: Prediction of angler behaviors, such as catch rates and angler pressure, is\nessential to maintaining fish populations and ensuring angler satisfaction.\nAngler behavior can partly be tracked by online platforms and mobile phone\napplications that provide fishing activities reported by recreational anglers.\nMoreover, angler behavior is known to be driven by local site attributes. Here,\nthe prediction of citizen-reported angler behavior was investigated by\nmachine-learning methods using auxiliary data on the environment,\nsocioeconomics, fisheries management objectives, and events at a freshwater\nbody. The goal was to determine whether auxiliary data alone could predict the\nreported behavior. Different spatial and temporal extents and temporal\nresolutions were considered. Accuracy scores averaged 88% for monthly\npredictions at single water bodies and 86% for spatial predictions on a day in\na specific region across Canada. At other resolutions and scales, the models\nonly achieved low prediction accuracy of around 60%. The study represents a\nfirst attempt at predicting angler behavior in time and space at a large scale\nand establishes a foundation for potential future expansions in various\ndirections.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Mobile phone records to feed activity-based travel demand models: MATSim\n  for studying a cordon toll policy in Barcelona: Activity-based models appeared as an answer to the limitations of the\ntraditional trip-based and tour-based four-stage models. The fundamental\nassumption of activity-based models is that travel demand is originated from\npeople performing their daily activities. This is why they include a consistent\nrepresentation of time, of the persons and households, time-dependent routing,\nand microsimulation of travel demand and traffic. In spite of their potential\nto simulate traffic demand management policies, their practical application is\nstill limited. One of the main reasons is that these models require a huge\namount of very detailed input data hard to get with surveys. However, the\npervasive use of mobile devices has brought a valuable new source of data. The\nwork presented here has a twofold objective: first, to demonstrate the\ncapability of mobile phone records to feed activity-based transport models,\nand, second, to assert the advantages of using activity-based models to\nestimate the effects of traffic demand management policies. Activity diaries\nfor the metropolitan area of Barcelona are reconstructed from mobile phone\nrecords. This information is then employed as input for building a transport\nMATSim model of the city. The model calibration and validation process proves\nthe quality of the activity diaries obtained. The possible impacts of a cordon\ntoll policy applied to two different areas of the city and at different times\nof the day is then studied. Our results show the way in which the modal share\nis modified in each of the considered scenario. The possibility of evaluating\nthe effects of the policy at both aggregated and traveller level, together with\nthe ability of the model to capture policy impacts beyond the cordon toll area\nconfirm the advantages of activity-based models for the evaluation of traffic\ndemand management policies.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Random node reinforcement and $K$-core structure of complex networks: To enhance robustness of complex networked systems, a simple method is\nintroducing reinforced nodes which always function during failure propagation.\nA random scheme of node reinforcement can be considered as a benchmark for\nfinding an optimal reinforcement solution. Yet there still lacks a systematic\nevaluation on how node reinforcement affects network structure at a mesoscopic\nlevel upon failures. Here we study this problem through the lens of $K$-cores\nof networks. Based on an analytical percolation framework, we first show that,\non uncorrelated random graphs, with a critical size of reinforced nodes, an\nabrupt emergence of $K$-cores is smoothed out to a continuous one, and a\ndetailed phase diagram is derived. We then show that, with a cost-benefit\nanalysis on random reinforcement, for proper weight factors in cost functions\nwith constant and increasing marginal costs, a gain function shows a\nunimodality, thus we can analytically find an optimal reinforcement fraction by\nlocating the maximal gain. In all, our framework offers a gain-oriented\nanalytical perspective to designing robust interconnected systems.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Opinion percolation in structured population: In a recent work [Shao $et$ $al$ 2009 Phys. Rev. Lett. \\textbf{108} 018701],\na nonconsensus opinion (NCO) model was proposed, where two opinions can stably\ncoexist by forming clusters of agents holding the same opinion. The NCO model\non lattices and several complex networks displays a phase transition behavior,\nwhich is characterized by a large spanning cluster of nodes holding the same\nopinion appears when the initial fraction of nodes holding this opinion is\nabove a certain critical value. In the NCO model, each agent will convert to\nits opposite opinion if there are more than half of agents holding the opposite\nopinion in its neighborhood. In this paper, we generalize the NCO model by\nassuming that each agent will change its opinion if the fraction of agents\nholding the opposite opinion in its neighborhood exceeds a threshold $T$\n($T\\geq 0.5$). We call this generalized model as the NCOT model. We apply the\nNCOT model on different network structures and study the formation of opinion\nclusters. We find that the NCOT model on lattices displays a continuous phase\ntransition. For random graphs and scale-free networks, the NCOT model shows a\ndiscontinuous phase transition when the threshold is small and the average\ndegree of the network is large, while in other cases the NCOT model displays a\ncontinuous phase transition.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Temporal statistical analysis on human article creation patterns: Inspired by previous works on human dynamics, we collect the temporal\nstatistics of the article creation by three Western scientists and an Eastern\nwriter. We investigate the distributions of the time intervals between the\ncreations of every two consecutive articles. All four time distributions are\nfound to be deviate from the Poisson statistics, and show an approximate\npower-law distribution. The power-law exponents are different with respect to\nindividual, indicating that there is no universally shared article creation\npattern.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Motion-induced synchronization in metapopulations of mobile agents: We study the influence of motion on the emergence of synchronization in a\nmetapopulation of random walkers moving on a heterogeneous network and subject\nto Kuramoto interactions at the network nodes. We discover a novel mechanism of\ntransition to macroscopic dynamical order induced by the walkers' motion.\nFurthermore, we observe two different microscopic paths to synchronization:\ndepending on the rules of the motion, either low-degree nodes or the hubs drive\nthe whole system towards synchronization. We provide analytical arguments to\nunderstand these results.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Speed Limit: Obey, or Not Obey?: It is commonly expected that drivers maintain a driving speed that is lower\nthan or around the posted speed limit, as failure to obey may result in safety\nrisks and fines. By taking randomly selected road segments as examples, this\nstudy compares the percentages of speeding vehicles in five countries\nworldwide, namely, two European countries (Germany and Italy), two Asian\ncountries (Japan and China), and one North American country (the United\nStates). Contrary to expectations, our results show that more than 80% of\ndrivers violate the posted speed limits in the studied road segments in Italy,\nJapan, and the United States. In particular, a significant portion (45.3%) of\ndrivers in Italy exceed the posted speed limit by a substantial margin (30\nkm/h), while few speeding vehicles are observed in the road segment examined in\nChina. Meanwhile, it is found that drivers on low-speed-limit roads are more\nlikely to exceed the posted speed limit, particularly when there are fewer\non-road vehicles. The comparison of different countries' speeding fines\nindicates that for the purpose of preventing speeding, increasing fines (as\nItaly has done) is less effective than enhancing supervision (as China has\ndone). The findings remind law enforcement agencies and traffic authorities of\nthe importance of the supervision of driver's behavior and the necessity of\nrevisiting the rationale for the current speed limit settings.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Architecture of the Florida Power Grid as a Complex Network: We study the Florida high-voltage power grid as a technological network\nembedded in space. Measurements of geographical lengths of transmission lines,\nthe mixing of generators and loads, the weighted clustering coefficient, as\nwell as the organization of edge conductance weights show a complex\narchitecture quite different from random-graph models usually considered. In\nparticular, we introduce a parametrized mixing matrix to characterize the\nmixing pattern of generators and loads in the Florida Grid, which is\nintermediate between the random mixing case and the semi-bipartite case where\ngenerator-generator transmission lines are forbidden. Our observations motivate\nan investigation of optimization (design) principles leading to the structural\norganization of power grids. We thus propose two network optimization models\nfor the Florida Grid as a case study. Our results show that the Florida Grid is\noptimized not only by reducing the construction cost (measured by the total\nlength of power lines), but also through reducing the total pairwise edge\nresistance in the grid, which increases the robustness of power transmission\nbetween generators and loads against random line failures. We then embed our\nmodels in spatial areas of different aspect ratios and study how this geometric\nfactor affects the network structure, as well as the box-counting fractal\ndimension of the grids generated by our models.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The dual-Barab\u00e1si-Albert model: The ability to sample random networks that can accurately represent real\nsocial contact networks is essential to the study of viral epidemics. The\nBarab\\'asi-Albert model and its extensions attempt to capture reality by\ngenerating networks with power-law degree distributions, but properties of the\nresulting distributions (e.g. minimum, average, and maximum degree) are often\nunrealistic of the social contacts the models attempt to capture. I propose a\nnovel extension of the Barab\\'asi-Albert model, which I call the\n\"dual-Barab\\'asi-Albert\" (DBA) model, that attempts to better capture these\nproperties of real networks of social contact.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Evolution of cooperation in costly institutes: We show that in a situation where individuals have a choice between a costly\ninstitute and a free institute to perform a collective action task, the\nexistence of a participation cost promotes cooperation in the costly institute.\nDespite paying for a participation cost, costly cooperators, who join the\ncostly institute and cooperate, can out-perform defectors, who predominantly\njoin a free institute. This, not only promotes cooperation in the costly\ninstitute but also facilitates the evolution of cooperation in the free\ninstitute. A costly institute out-performs a free institute when the\nprofitability of the collective action is low. On the other hand, a free\ninstitute performs better when the collective action's profitability is high.\nFurthermore, we show that in a structured population, when individuals have a\nchoice between different institutes, a mutualistic relation between cooperators\nwith different institute preferences emerges and helps the evolution of\ncooperation.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Universal statistical properties of poker tournaments: We present a simple model of Texas hold'em poker tournaments which retains\nthe two main aspects of the game: i. the minimal bet grows exponentially with\ntime; ii. players have a finite probability to bet all their money. The\ndistribution of the fortunes of players not yet eliminated is found to be\nindependent of time during most of the tournament, and reproduces accurately\ndata obtained from Internet tournaments and world championship events. This\nmodel also makes the connection between poker and the persistence problem\nwidely studied in physics, as well as some recent physical models of biological\nevolution, and extreme value statistics.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Early decarbonisation of the European energy system pays off: For a given carbon budget over several decades, different transformation\nrates for the energy system yield starkly different results. Here we consider a\nbudget of 33 GtCO2 for the cumulative carbon dioxide emissions from the\nEuropean electricity, heating, and transport sectors between 2020 and 2050,\nwhich represents Europe's contribution to the Paris Agreement. We have found\nthat following an early and steady path in which emissions are strongly reduced\nin the first decade is more cost-effective than following a late and rapid path\nin which low initial reduction targets quickly deplete the carbon budget and\nrequire a sharp reduction later. We show that solar photovoltaic, onshore and\noffshore wind can become the cornerstone of a fully decarbonised energy system\nand that installation rates similar to historical maxima are required to\nachieve timely decarbonisation. Key to those results is a proper representation\nof existing balancing strategies through an open, hourly-resolved, networked\nmodel of the sector-coupled European energy system.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Match Analysis an undervalued coaching tool: From a Biomechanical point of view, Judo competition is an intriguing complex\nnonlinear system, with many chaotic and fractals aspects, It is also the test\nbed in which all coaching capabilities and athlete's performances are evaluated\nand put to the test. Competition is the moment of truth of all conditioning\ntime, preparation and technical work, before developed, and it is also the\nclimax of the teaching point of view. Furthermore, it is the most important\nsource of technical assessment. Studying it is essential to the coaches because\nthey can obtain useful information for their coaching. Match Analysis could be\nseen as the master key in all situation sports (dual or team) like Judo, to\nhelp in useful way the difficult task of coach or best for National or Olympic\ncoaching equips. In this paper it is presented a short summary of the most\nimportant methodological achievement in judo match analysis. It is also\npresented, at light of the last technological improvement, the first\nsystematization toward new fields. On the basis of technological development,\nMatch Analysis could be a valuable source of four levels of information: 1st.\nAthlete's Physiological data 2nd. Athlete's Technical data 3rd. Athlete's\nStrategically data 4th. Adversary's Scouting These new interesting ways opened\nby this powerful coaching help, are very useful for national team technical\nmanagement. In the last part of the paper, the analysis is focalized toward a,\ntill now, misused information: \"Dromograms\" (athletes' shifting paths) study as\nuseful source of fighting habit of athletes.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Finding the Fairest Voting System using Likelihood Analysis: A crucial aspect of democracy is the notion of free and fair elections. While\nmuch attention has been placed on factors such as voter turnout and the quality\nof the candidates, the voting system used in an election is just as\nconsequential in determining the winner. In Social Choice Theory, voting\nsystems are evaluated based on whether or not they violate fairness criteria.\nArrow's Impossibility Theorem states that no voting method can jointly satisfy\nfive fairness criteria: unanimity, transitivity, independence of irrelevant\nalternatives, unrestricted domain, and non-dictatorship. However, voting\nsystems are not equally likely to violate criteria. This paper takes a\nstatistical approach to determine which of 12 voting systems is the fairest\nbased on their probabilistic likelihood of violating nine social choice\ncriteria. The criteria include Arrow's five conditions, the Condorcet winner\nand loser criteria, the majority criteria, and citizen sovereignty. Elections\nwith up to 10 million voters and between three and six candidates are simulated\nusing Impartial Culture and Impartial Anonymous Culture. We first compute the\nfrequencies with which voting systems violate individual criteria and then\ncompute Joint Satisfaction frequencies, the likelihood that a voting system\njointly satisfies all nine criteria. We find that Pairwise Majority is the most\nlikely to jointly satisfy the nine conditions, but increasingly violates\ntransitivity as the number of candidates increases. Of the systems that satisfy\ntransitivity, the Baldwin method has the highest Joint Satisfaction frequency\nin elections with three candidates. As the number of candidates increase, the\nJoint Satisfaction frequencies decrease rapidly for all systems.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A Probability Density Function for Google's stocks: It is an approach to introduce the forward Kolmogorov equation as an\ninteresting natural ingredient in studying the evolution of the market stock\nprices.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Physics to the rescue?: A vast body of literature addresses the complex nature of science's\nreproducibility crisis. In contrast with this perceived complexity, some recent\npapers from the discipline of physics suggests that irreproducibility does not\npoint to a systemic crisis, but is, on the contrary, a sign that the science\nsystem works properly. These works, while acknowledging the difference between\nphysics and other disciplines mired in the reproducibility crisis, hint that\nall disciplines could learn from Physics. The present work suggests that this\noptimistic message, when addressed to struggling disciplines, may invite\ncomplacency over other relevant dimensions of crisis, delay its solution, and\nget into the way of a truly joint effort from all disciplines to tackle the\nimportant social and environmental predicaments of the present age.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Dynamic Modeling of Cascading Failure in Power Systems: The modeling of cascading failure in power systems is difficult because of\nthe many different mechanisms involved; no single model captures all of these\nmechanisms. Understanding the relative importance of these different mechanisms\nis an important step in choosing which mechanisms need to be modeled for\nparticular types of cascading failure analysis. This work presents a dynamic\nsimulation model of both power networks and protection systems, which can\nsimulate a wider variety of cascading outage mechanisms, relative to existing\nquasi-steady state (QSS) models. The model allows one to test the impact of\ndifferent load models and protections on cascading outage sizes. This paper\ndescribes each module of the developed dynamic model and demonstrates how\ndifferent mechanisms interact. In order to test the model we simulated a batch\nof randomly selected $N-2$ contingencies for several different static load\nconfigurations, and found that the distribution of blackout sizes and event\nlengths from the proposed dynamic simulator correlates well with historical\ntrends. The results also show that load models have significant impacts on the\ncascading risks. This dynamic model was also compared against a QSS model based\non the dc power flow approximations; we find that the two models largely agree,\nbut produce substantially different results for later stages of cascading.",
        "category": "physics_soc-ph"
    },
    {
        "text": "PageRank model of opinion formation on social networks: We propose the PageRank model of opinion formation and investigate its rich\nproperties on real directed networks of Universities of Cambridge and Oxford,\nLiveJournal and Twitter. In this model the opinion formation of linked electors\nis weighted with their PageRank probability. We find that the society elite,\ncorresponding to the top PageRank nodes, can impose its opinion to a\nsignificant fraction of the society. However, for a homogeneous distribution of\ntwo opinions there exists a bistability range of opinions which depends on a\nconformist parameter characterizing the opinion formation. We find that\nLiveJournal and Twitter networks have a stronger tendency to a totalitar\nopinion formation. We also analyze the Sznajd model generalized for scale-free\nnetworks with the weighted PageRank vote of electors.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Nodal domain partition and the number of communities in networks: It is difficult to detect and evaluate the number of communities in complex\nnetworks, especially when the situation involves with an ambiguous boundary\nbetween the inner- and inter-community densities. In this paper, Discrete Nodal\nDomain Theory could be used to provide a criterion to determine how many\ncommunities a network would have and how to partition these communities by\nmeans of the topological structure and geometric characterization. By capturing\nthe signs of certain Laplacian eigenvectors we can separate the network into\nseveral reasonable clusters. The method leads to a fast and effective algorithm\nwith application to a variety of real networks data sets.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The elephant in the room: multi-authorship and the assessment of\n  individual researchers: When a group of individuals creates something, credit is usually divided\namong them. Oddly, that does not apply to scientific papers. The most commonly\nused performance measure for individual researchers is the h-index, which does\nnot correct for multi-authorship. Each author claims full credit for each paper\nand each ensuing citation. This mismeasure of achievement is fuelling a\nflagrant increase in multi-authorship. Several alternatives to the h-index have\nbeen devised, and one of them, the individual h-index (hI), is logical,\nintuitive and easily calculated. Correcting for multi-authorship would end\ngratuitous authorship and allow proper attribution and unbiased comparisons.",
        "category": "physics_soc-ph"
    },
    {
        "text": "On the Diversity of Non-Linear Transient Dynamics in Several Types of\n  Complex Networks: Dynamic systems characterized by diversified evolutions are not only more\nflexible, but also more resilient to attacks, failures and changing conditions.\nThis article addresses the quantification of the diversity of non-linear\ntransient dynamics obtained in undirected and unweighted complex networks as a\nconsequence of self-avoiding random walks. The diversity of walks starting at a\nspecific node $i$ is quantified in terms of a signature composed by the\nentropies of the node visit probabilities along each of the initial steps. Six\ntheoretical models of complex networks are considered: Erd\\H{o}s-R\\'enyi,\nBarab\\'asi-Albert, Watts-Strogatz, a geographical model, as well as two\nrecently introduced knitted networks formed by paths. The random walk diversity\nis explored at the level of network categories and of individual nodes. Because\nthe diversity at successive steps of the walks tends to be correlated,\nprincipal component analysis is systematically applied in order to identify the\nmore relevant linear combinations of the diversity entropies and to obtain\noptimal dimensionality reduction. Several interesting results are reported,\nincluding the facts that the transient diversity tends to increase with the\naverage degree for all considered network models and that the Watts and\nStrogatz and geographical models tend to yield diversity entropies which\nincrease more gradually with the number of steps, contrasting sharply with the\nsteep increases verified for the other four considered models. The principal\nlinear combination of the diversities identified by the principal component\nanalysis method is shown to allow an interesting characterization of individual\nnodes as well as partitioning of networks into subgraphs of similar diversity.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Andrzej Pekalski networks of scientific interests with internal degrees\n  of freedom through self-citation analysis: Old and recent theoretical works by Andrzej Pekalski (APE) are recalled as\npossible sources of interest for describing network formation and clustering in\ncomplex (scientific) communities, through self-organisation and percolation\nprocesses. Emphasis is placed on APE self-citation network over four decades.\nThe method is that used for detecting scientists field mobility by focusing on\nauthor's self-citation, co-authorships and article topics networks as in [1,2].\nIt is shown that APE's self-citation patterns reveal important information on\nAPE interest for research topics over time as well as APE engagement on\ndifferent scientific topics and in different networks of collaboration. Its\ninteresting complexity results from \"degrees of freedom\" and external fields\nleading to so called internal shock resistance. It is found that APE network of\nscientific interests belongs to independent clusters and occurs through rare or\ndrastic events as in irreversible \"preferential attachment processes\", similar\nto those found in usual mechanics and thermodynamics phase transitions.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Latitudinal trends in human primary activities: characterizing the\n  winter day as a synchronizer: This work analyzes time use surveys from 19 countries (17 European and 2\nAmerican) in the middle latitude range from 38{\\deg} to 61{\\deg} latitude\naccounting for 45% of world population in that range. Time marks for primary\nactivities (sleeping, working and eating) are systematically contrasted against\nlight/dark conditions related to latitude.\n  The analysis reveals that winter sunrise is a synchronizer for labor start\ntime below 54{\\deg} where they occur within the winter civil twilight region.\nWinter sunset is a source of synchronization for labor end times.\n  Winter terminator also punctuate meal times in Europe with dinner times\noccurring 3h after winter sunset time within a strip of 1h, which is 40%\nnarrower than variability of dinner local times.\n  The sleep-wake cycle of laborers in a weekday is shown to be related to\nwinter sunrise whereas standard population's cycle appears to be irrespective\nof latitude.\n  The significance of the winter terminator depends on two competing factors\naverage daily labor time (some 7h30m) and winter daytime ---the shortest\nphotoperiod---. Winter terminator gains significance when shortest photoperiod\nroughly matches to daily labor time plus a reasonable lunch break. That is\nwithin a latitude range from 38{\\deg} to 54{\\deg}.\n  The significance of winter terminator as a source of synchronization is also\nrelated to contemporary year round time schedules: the shortest photoperiod\nrepresents the worst case scenario the society faces.\n  Average daily sleep times show little trend with the shortest photoperiod\nslope 5min/h for a Pearson coefficient $r^2=0.242$. Average labor time may have\na weak coupling with the shortest photoperiod: slope 29min/h for $r^2=0.338$.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Disparate Patterns of Movements and Visits to Points of Interests\n  Located in Urban Hotspots across U.S. Metropolitan Cities during COVID-19: We examined the effect of social distancing on changes in visits to urban\nhotspot points of interest. Urban hotspots, such as central business districts,\nare gravity activity centers orchestrating movement and mobility patterns in\ncities. In a pandemic situation, urban hotspots could be potential\nsuperspreader areas as visits to urban hotspots can increase the risk of\ncontact and transmission of a disease among a population. We mapped\norigin-destination networks from census block groups to points of interest\n(POIs) in sixteen cities in the United States. We adopted a coarse-grain\napproach to study movement patterns of visits to POIs among the hotspots and\nnon-hotspots from January to May 2020. Also, we conducted chi-square tests to\nidentify POIs with significant flux-in changes during the analysis period. The\nresults showed disparate patterns across cities in terms of reduction in POI\nvisits to hotspot areas. The sixteen cities are divided into two categories\nbased on visits to POIs in hotspot areas. In one category, which includes the\ncities of, San Francisco, Seattle, and Chicago, we observe a considerable\ndecrease in visits to POIs in hotspot areas, while in another category,\nincluding the cites of, Austin, Houston, and San Diego, the visits to hotspot\nareas did not greatly decrease during the social distancing period. In\naddition, while all the cities exhibited overall decreasing visits to POIs, one\ncategory maintained the proportion of visits to POIs in the hotspots. The\nproportion of visits to some POIs (e.g., Restaurant and Other Eating Places)\nremained stable during the social distancing period, while some POIs had an\nincreased proportion of visits (e.g., Grocery Stores). The findings highlight\nthat social distancing orders do yield disparate patterns of reduction in\nmovements to hotspots POIs.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Localization of eigenvector centrality in networks with a cut vertex: We show that eigenvector centrality exhibits localization phenomena on\nnetworks that can be easily partitioned by the removal of a vertex cut set, the\nmost extreme example being networks with a cut vertex. Three distinct types of\nlocalization are identified in these structures. One is related to the\nwell-established hub node localization phenomenon and the other two are\nintroduced and characterized here. We gain insights into these problems by\nderiving the relationship between eigenvector centrality and Katz centrality.\nThis leads to an interpretation of the principal eigenvector as an\napproximation to more robust centrality measures which exist in the full span\nof an eigenbasis of the adjacency matrix.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Online geolocalized emotion across US cities during the COVID crisis:\n  Universality, policy response, and connection with local mobility: As the COVID-19 pandemic began to sweep across the US it elicited a wide\nspectrum of responses, both online and offline, across the population. To aid\nthe development of effective spatially targeted interventions in the midst of\nthis turmoil, it is important to understand the geolocalization of these online\nemotional responses, as well as their association with offline behavioral\nresponses. Here, we analyze around 13 million geotagged tweets in 49 cities\nacross the US from the first few months of the pandemic to assess regional\ndependence in online sentiments with respect to a few major topics, and how\nthese sentiments correlate with policy development and human mobility.\nSurprisingly, we observe universal trends in overall and topic-based sentiments\nacross cities over the time period studied, with variability primarily seen\nonly in the immediate impact of federal guidelines and local lockdown policies.\nWe also find that these local sentiments are highly correlated with and\npredictive of city-level mobility, while the correlations between sentiments\nand local cases and deaths are relatively weak. Our findings point to\nwidespread commonalities in the online public emotional responses to COVID\nacross the US, both temporally and relative to offline indicators, in contrast\nwith the high variability seen in early local containment policies. This study\nalso provides new insights into the use of social media data in crisis\nmanagement by integrating offline data to gain an in-depth understanding of\npublic emotional responses, policy development, and local mobility.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Opinion inertia and coarsening in the Persistent Voter model: We consider the Persistent Voter model (PVM), a variant of the Voter model\n(VM) that includes transient, dynamically-induced zealots. Due to peer\nreinforcement, the internal confidence $\\eta_i$ of a normal voter increases by\nsteps of size $\\Delta\\eta$ and once it gets above a given threshold, it becomes\na zealot. Then, its opinion remains frozen until enough interactions with the\nopposite opinion occur and its confidence is reset. No longer a zealot, the\nregular voter may change opinion once again. This opinion inertia mechanism,\nalbeit simplified, is responsible for an effective surface tension and the PVM\nhas a crossover from a fluctuation-driven dynamics, as in the VM, to a\ncurvature-driven one, as in the Ising Model at low temperature (IM0). The\naverage time $\\tau$ to attain consensus is non-monotonic on $\\Delta\\eta$ and\nhas a minimum at $\\Delta\\eta_{\\min}$. In this paper we clarify the mechanisms\nthat accelerate the system towards consensus close to $\\Delta\\eta_{\\min}$.\nClose to the crossover at $\\Delta\\eta_{\\min}$, the intermediate region around\nthe domains where the regular voters accumulate (the active region, AR) is\nlarge and the surface tension, albeit small, is still enough to keep the shape\nand reduce the fragmentation of the domains. The large size of the AR in the\nregion of $\\Delta\\eta_{\\min}$ has two important effects that accelerates the\ndynamics. First, it dislodges the zealots in the bulk of the domains and\nsecond, it maximally suppresses the slowly-evolving stripes that normally form\nin Ising-like models. This suggests the importance of understanding the role of\nthe AR, where the change of opinion is facilitated, and the interplay between\nregular voters and zealots when attempting to disrupt polarized states.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Sustaining a network by controlling a fraction of nodes: Multi-stability is a widely observed phenomenon in real complex networked\nsystems, such as technological infrastructures, ecological systems, gene\nregulation, transportation and more. When a system functions normally but there\nexists also a potential state with abnormal low activity, although the system\nis at equilibrium it might make a transition into the low activity undesired\nstate due to external disturbances and perturbations. Thus, such a system can\nbe regarded as unsustainable, due to the danger of falling into the potential\ninactive state. Here we explore, analytically and by simulations, how\nsupporting the activity of a fraction $\\rho$ of nodes can turn an unsustainable\nsystem to be sustainable by eliminating the inactive potential stable state. We\nthus unveil a new sustainability phase diagram in the presence of a fraction of\ncontrolled nodes $\\rho$. This phase diagram could provide guidelines to sustain\na network by external intervention and/or by strengthening the connectivity of\nthe network.",
        "category": "physics_soc-ph"
    },
    {
        "text": "New nodes attack strategies for real complex weighted networks: In this work we introduce a new nodes attack strategy removing nodes with\nhighest conditional weighted betweenness centrality (CondWBet). We compare its\nefficacy with well-known attack strategies from literature over five real-world\ncomplex weighted networks. We use the network weighted efficiency (WEFF) like a\nmeasure encompassing the weighted structure of the network in addition to the\ncommonly used binary-topological measure, the largest connected cluster (LCC).\nWe find that the recently proposed conditional betweenness strategy (CondBet)\n(Nguyen et al. 2019) is the best to fragment the LCC in all cases. Further, we\nfind that the introduced CondWBet strategy is the best to decrease the network\nefficiency (WEFF) in 3 out of 5 cases. Last, CondWBet is be the most effective\nstrategy to reduce WEFF at the beginning of the removal process whereas the\nStrength that removes nodes with highest link weights first, shows the highest\nefficacy in the final phase of the removal process when the network is broken\nin many small clusters. These last outcomes would suggest that a better\nattacking strategy could be a combination of the CondWBet and Strength\nstrategies",
        "category": "physics_soc-ph"
    },
    {
        "text": "Resolution of the stochastic strategy spatial prisoner's dilemma by\n  means of particle swarm optimization: We study the evolution of cooperation among selfish individuals in the\nstochastic strategy spatial prisoner's dilemma game. We equip players with the\nparticle swarm optimization technique, and find that it may lead to highly\ncooperative states even if the temptations to defect are strong. The concept of\nparticle swarm optimization was originally introduced within a simple model of\nsocial dynamics that can describe the formation of a swarm, i.e., analogous to\na swarm of bees searching for a food source. Essentially, particle swarm\noptimization foresees changes in the velocity profile of each player, such that\nthe best locations are targeted and eventually occupied. In our case, each\nplayer keeps track of the highest payoff attained within a local topological\nneighborhood and its individual highest payoff. Thus, players make use of their\nown memory that keeps score of the most profitable strategy in previous\nactions, as well as use of the knowledge gained by the swarm as a whole, to\nfind the best available strategy for themselves and the society. Following\nextensive simulations of this setup, we find a significant increase in the\nlevel of cooperation for a wide range of parameters, and also a full resolution\nof the prisoner's dilemma. We also demonstrate extreme efficiency of the\noptimization algorithm when dealing with environments that strongly favor the\nproliferation of defection, which in turn suggests that swarming could be an\nimportant phenomenon by means of which cooperation can be sustained even under\nhighly unfavorable conditions. We thus present an alternative way of\nunderstanding the evolution of cooperative behavior and its ubiquitous presence\nin nature, and we hope that this study will be inspirational for future efforts\naimed in this direction.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Characterizing the process of reaching consensus for social systems: A novel way of characterizing the process of reaching consensus for a social\nsystem is given. The foundation of the characterization is based on the theorem\nwhich states that the sufficient and necessary condition for a system to reach\nthe state of consensus is the occurrence of communicators, defined as the units\nof the system that can directly communicate with all the others simultaneously.\nA model is proposed to illustrate the characterization explicitly. The\nexistence of communicators provides an efficient way for unifying two systems\nthat a state of consensus is guaranteed after the mergence.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Small world yields optimal public goods in presence of both altruistic\n  and selfish cooperators: Empirical studies have shown that individuals' behaviors are largely\ninfluenced by social conformity, including punishment. However, a\ncoevolutionary theoretical framework that takes into account effects of\nconformity on individuals' punishment behaviors has not been put forward yet.\nHerein we propose a coevolutionary game model to extend the theory of\ncooperation with conformity in spatial public goods game by considering pool\npunishment, as well as two converse feedback modes of conformity that strongly\naffect cooperators' punishment behaviors. We focus on how different parameters\nand spatial structures govern evolutionary dynamics on three different kinds of\nnetworks by employing mean-field analysis based on replicator dynamics and\nMonte Carlo simulations. On regular lattices, defectors are overall extincted\nsince cooperators, especially selfish cooperators, have great evolutionary\nadvantages due to strong network reciprocity, and at the same time the number\nof altruistic cooperators decays. Conversely, abundant shortcuts in regular\nrandom networks lead to the prevalence of altruistic cooperators, but\ncooperators suffer from free-riding behaviors of defectors. Of particular\ninterest, we find that small-world topology can simultaneously help cooperators\nsuccessfully outperform defectors by means of strong network reciprocity, and\nenable rich contacting opportunities with defectors to facilitate the expansion\nof altruistic cooperators. Therefore, we clarify that small world is the\noptimal topology subject to the dominance of altruistic cooperators.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Social groups in pedestrian crowds: Review of their influence on the\n  dynamics and their modelling: Pedestrians are often encountered walking in the company of some social\nrelations, rather than alone. The social groups thus formed, in variable\nproportions depending on the context, are not randomly organised but exhibit\ndistinct features, such as the well-known tendency of 3-member groups to be\narranged in a V-shape. The existence of group structures is thus likely to\nimpact the collective dynamics of the crowd, possibly in a critical way when\nemergency situations are considered. After turning a blind eye to these group\naspects for years, endeavours to model groups in crowd simulation software have\nthrived in the past decades. This fairly short review opens on a description of\ntheir empirical characteristics and their impact on the global flow. Then, it\naims to offer a pedagogical discussion of the main strategies to model such\ngroups, within different types of models, in order to provide guidance for\nprospective modellers.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Effect of shortest path multiplicity on congestion of multiplex networks: Shortest paths are representative of discrete geodesic distances in graphs,\nand many descriptors of networks depend on their counting. In multiplex\nnetworks, this counting is radically important to quantify the switch between\nlayers and it has crucial implications in the transportation efficiency and\ncongestion processes. Here we present a mathematical approach to the\ncomputation of the joint distribution of distance and multiplicity\n(degeneration) of shortest paths in multiplex networks, and exploit its\nrelation to congestion processes. The results allow to approximate\nsemi-analytically the onset of congestion in multiplex networks as a function\nof the congestion of its layers.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Correlated bursts in temporal networks slow down spreading: Spreading dynamics has been considered to take place in temporal networks,\nwhere temporal interaction patterns between nodes show non-Poissonian bursty\nnature. The effects of inhomogeneous interevent times (IETs) on the spreading\nhave been extensively studied in recent years, yet little is known about the\neffects of correlations between IETs on the spreading. In order to investigate\nthose effects, we study two-step deterministic susceptible-infected (SI) and\nprobabilistic SI dynamics when the interaction patterns are modeled by\ninhomogeneous and correlated IETs, i.e., correlated bursts. By analyzing the\ntransmission time statistics in a single-link setup and by simulating the\nspreading in Bethe lattices and random graphs, we conclude that the positive\ncorrelation between IETs slows down the spreading. We also argue that the\nshortest transmission time from one infected node to its susceptible neighbors\ncan successfully explain our numerical results.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Understanding widely scattered traffic flows, the capacity drop,\n  platoons, and times-to-collision as effects of variance-driven time gaps: We investigate the adaptation of the time headways in car-following models as\na function of the local velocity variance, which is a measure of the\ninhomogeneity of traffic flow. We apply this mechanism to several car-following\nmodels and simulate traffic breakdowns in open systems with an on-ramp as\nbottleneck. Single-vehicle data generated by several 'virtual detectors' show a\nsemi-quantitative agreement with microscopic data from the Dutch freeway A9.\nThis includes the observed distributions of the net time headways and\ntimes-to-collision for free and congested traffic. While the times-to-collision\nshow a nearly universal distribution in free and congested traffic, the modal\nvalue of the time headway distribution is shifted by a factor of about two in\ncongested conditions. Macroscopically, this corresponds to the 'capacity drop'\nat the transition from free to congested traffic. Finally, we explain the wide\nscattering of one-minute flow-density data by a self-organized variance-driven\nprocess that leads to the spontaneous formation and decay of long-lived\nplatoons even for deterministic dynamics on a single lane.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Phone Walkers: A study of human dependence on inactive mobile\n  devices: The development of mobile phones has largely increased human interactions.\nWhilst the use of these devices for communication has received significant\nattention, there has been little analysis of more passive interactions. Through\ncensus data on casual social groups, this work suggests a clear pattern of\nmobile phones being carried in people's hands, without the person using it\n(that is, not looking at it). Moreover, this study suggests that when\nindividuals join members of the opposite sex there is a clear tendency to stop\nholding mobile phones whilst walking. Although it is not clear why people hold\ntheir phones whilst walking in such large proportions (38% of solitary women,\nand 31% of solitary men), we highlight several possible explanation for holding\nthe device, including the need to advertise status and affluence, to maintain\nimmediate connection with friends and family, and to mitigate feelings related\nto anxiety and security.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Hurricanes Increase Climate Change Conversations on Twitter: The public understanding of climate change plays a critical role in\ntranslating climate science into climate action. In the public discourse,\nclimate impacts are often discussed in the context of extreme weather events.\nHere, we analyse 65 million Twitter posts and 240 thousand news media articles\nrelated to 18 major hurricanes from 2010 to 2022 to clarify how hurricanes\nimpact the public discussion around climate change. First, we analyse news\ncontent and show that climate change is the most prominent non-hurricane\nspecific topic discussed by the news media in relation to hurricanes. Second,\nwe perform a comparative analysis between reliable and questionable news media\noutlets, finding that the language around climate change varies between news\nmedia providers. Finally, using geolocated data, we show that accounts in\nregions affected by hurricanes discuss climate change at a significantly higher\nrate than accounts in unaffected areas, with references to climate change\nincreasing by, on average, 80% after impact, and up to 200% for the largest\nhurricanes. Our findings demonstrate how hurricanes have a key impact on the\npublic awareness of climate change.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Detectability of communities in heterogeneous networks: Communities are fundamental entities for the characterization of the\nstructure of real networks. The standard approach to the identification of\ncommunities in networks is based on the optimization of a quality function\nknown as \"modularity\". Although modularity has been at the center of an intense\nresearch activity and many methods for its maximization have been proposed, not\nmuch it is yet known about the necessary conditions that communities need to\nsatisfy in order to be detectable with modularity maximization methods. Here,\nwe develop a simple theory to establish these conditions, and we successfully\napply it to various classes of network models. Our main result is that\nheterogeneity in the degree distribution helps modularity to correctly recover\nthe community structure of a network and that, in the realistic case of\nscale-free networks with degree exponent $\\gamma < 2.5$, modularity is always\nable to detect the presence of communities.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Development of Econophysics: A biased account and perspective from\n  Kolkata: We present here a somewhat personalized account of the emergence of\neconophysics as an attractive research topic in physical as well as social\nsciences. After a rather detailed story telling about our endeavors from\nKolkata, we give a brief description of the main research achievements in a\nsimple and non-technical language. We also present briefly, in technical\nlanguage, a piece of our recent research result. We conclude our paper with a\nbrief perspective.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Efficiency of communities and financial markets during the 2020 pandemic: This paper investigates the relationship between the spread of the COVID-19\npandemic, the state of community activity, and the financial index performance\nacross 20 countries. First, we analyze which countries behaved similarly in\n2020 with respect to one of three multivariate time series: daily COVID-19\ncases, Apple mobility data and national equity index price. Next, we study the\ntrajectories of all three of these attributes in conjunction to determine which\nexhibited greater similarity. Finally, we investigate whether country financial\nindices or mobility data responded quicker to surges in COVID-19 cases. Our\nresults indicate that mobility data and national financial indices exhibited\nthe most similarity in their trajectories, with financial indices responding\nquicker. This suggests that financial market participants may have interpreted\nand responded to COVID-19 data more efficiently than governments. Further,\nresults imply that efforts to study community mobility data as a leading\nindicator for financial market performance during the pandemic were misguided.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Path Integral Control in Infectious Disease Modeling: COVID-19, a global pandemic of unprecedented scale, has had a profound impact\non nations worldwide, resulting in the tragic loss of nearly 1.1 million lives\nin the United States and a staggering 7 million worldwide. In the absence of\neffective vaccines, governments across the globe resorted to the implementation\nof lockdown measures as a vital strategy to mitigate the virus's relentless\nspread. While these restrictions were widely enforced, crucial sectors like\npublic health and safety remained operational, ensuring the continuity of\nessential services. The timing and stringency of lockdown measures in various\nU.S. states were intricately tailored to the severity of the outbreak within\ntheir respective regions. Lockdowns effectively curtailed social interactions,\nthereby significantly reducing virus transmission. However, it is essential to\nstrike a balance, as prolonged lockdowns can sow apprehension among the\npopulace, impeding the resumption of normal social activities due to the\npersistent fear of contracting COVID-19. These prolonged restrictions have\nreverberated throughout the business landscape, resulting in reduced consumer\nand employee participation, ultimately denting long-term profitability\nsustainability. Businesses that lacked the resilience of adequate inventory\nfaced the dire prospect of permanent closure. Regrettably, the absence of\nsubstantial government financial support has made business closure an all too\ncommon outcome, with the arduous task of revival to former employment levels.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Peer-to-Peer and Mass Communication Effect on Revolution Dynamics: Revolution dynamics is studied through a minimal Ising model with three main\ninfluences (fields): personal conservatism (power-law distributed),\ninter-personal and group pressure, and a global field incorporating\npeer-to-peer and mass communications, which is generated bottom-up from the\nrevolutionary faction. A rich phase diagram appears separating possible\nterminal stages of the revolution, characterizing failure phases by the\nfeatures of the individuals who had joined the revolution. An exhaustive\nsolution of the model is produced, allowing predictions to be made on the\nrevolution's outcome.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Meet, Discuss and Trust each other: large versus small groups: In this paper we propose a dynamical interpretation of the sociological\ndistinction between large and small groups of interacting individuals. In the\nformer case individual behaviors are largely dominated by the group effect,\nwhile in the latter mutual relationships do matter. Numerical and analytical\ntools are combined to substantiate our claims.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Importance of the time acquisition difference between DMSP/OLS and\n  SNPP/VIIRS/DNB and the RAW trends in Europe: Using the SNPP-VIIRS/DNB and the DMSP-OLS it is possible to have an idea of\nthe evolution of the light pollution until the LEDs started to appear\nmassively. Another of the issues of dealing with these two datasets is the\ndifferent time of acquisition. In general this means that countries get dimmer\nlate at night, although that is not always true. A counterexample of this is\nthe country of the Netherlands because of the opening of the shutters late at\nnight. Other countries have a much bigger drop late at night because of some\npractices like the total or partial turn off of public and private lighting but\nalso dimming policies. Examples of this are countries like France, Austria and\nthe UK.",
        "category": "physics_soc-ph"
    },
    {
        "text": "From the Grid to the Smart Grid, Topologically: The Smart Grid is not just about the digitalization of the Power Grid. In its\nmore visionary acceptation, it is a model of energy management in which the\nusers are engaged in producing energy as well as consuming it, while having\ninformation systems fully aware of the energy demand-response of the network\nand of dynamically varying prices. A natural question is then: to make the\nSmart Grid a reality will the Distribution Grid have to be updated? We assume a\npositive answer to the question and we consider the lower layers of Medium and\nLow Voltage to be the most affected by the change. In our previous work, we\nhave analyzed samples of the Dutch Distribution Grid in our previous work and\nwe have considered possible evolutions of these using synthetic topologies\nmodeled after studies of complex systems in other technological domains in\nanother previous work. In this paper, we take an extra important further step\nby defining a methodology for evolving any existing physical Power Grid to a\ngood Smart Grid model thus laying the foundations for a decision support system\nfor utilities and governmental organizations. In doing so, we consider several\npossible evolution strategies and apply then to the Dutch Distribution Grid. We\nshow how more connectivity is beneficial in realizing more efficient and\nreliable networks. Our proposal is topological in nature, and enhanced with\neconomic considerations of the costs of such evolutions in terms of cabling\nexpenses and economic benefits of evolving the Grid.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Heider balance and social distance: The Heider balance is a state of a group of people with established mutual\nrelations between them. These relations, friendly or hostile, can be measured\nin the Bogardus scale of the social distance. In previous works on the Heider\nbalance, these relations have been described with integers 0 and $\\pm1$.\nRecently we have proposed real numbers instead. Also, differential equations\nhave been used to simulate the time evolution of the relations, which were\nallowed to vary within a given range. In this work, we investigate an influence\nof this allowed range on the system dynamics. As a result, we have found that a\nnarrowing of the range of relations leads to a large delay in achieving the\nHeider balance. Another point is that a slight shift of the initial\ndistribution of the social distance towards friendship can lead to a total\nelimination of hostility.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Decisions and disease: a mechanism for the evolution of cooperation: In numerous contexts, individuals may decide whether they take actions to\nmitigate the spread of disease, or not. Mitigating the spread of disease\nrequires an individual to change their routine behaviours to benefit others,\nresulting in a 'disease dilemma' similar to the seminal prisoner's dilemma. In\nthe classical prisoner's dilemma, evolutionary game dynamics predict that all\nindividuals evolve to 'defect.' We have discovered that when the rate of\ncooperation within a population is directly linked to the rate of spread of the\ndisease, cooperation evolves under certain conditions. For diseases which do\nnot confer immunity to recovered individuals, if the time scale at which\nindividuals receive information is sufficiently rapid compared to the time\nscale at which the disease spreads, then cooperation emerges. Moreover, in the\nlimit as mitigation measures become increasingly effective, the disease can be\ncontrolled, and the rate of infections tends to zero. Our model is based on\ntheoretical mathematics and therefore unconstrained to any single context. For\nexample, the disease spreading model considered here could also be used to\ndescribe social and group dynamics. In this sense, we may have discovered a\nfundamental and novel mechanism for the evolution of cooperation in a broad\nsense.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Revealing latent factors of temporal networks for mesoscale intervention\n  in epidemic spread: The customary perspective to reason about epidemic mitigation in temporal\nnetworks hinges on the identification of nodes with specific features or\nnetwork roles. The ensuing individual-based control strategies, however, are\ndifficult to carry out in practice and ignore important correlations between\ntopological and temporal patterns. Here we adopt a mesoscopic perspective and\npresent a principled framework to identify collective features at multiple\nscales and rank their importance for epidemic spread. We use tensor\ndecomposition techniques to build an additive representation of a temporal\nnetwork in terms of mesostructures, such as cohesive clusters and\ntemporally-localized mixing patterns. This representation allows to determine\nthe impact of individual mesostructures on epidemic spread and to assess the\neffect of targeted interventions that remove chosen structures. We illustrate\nthis approach using high-resolution social network data on face-to-face\ninteractions in a school and show that our method affords the design of\neffective mesoscale interventions.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Ultrametricity increases the predictability of cultural dynamics: A quantitative understanding of societies requires useful combinations of\nempirical data and mathematical models. Models of cultural dynamics aim at\nexplaining the emergence of culturally homogeneous groups through social\ninfluence. Traditionally, the initial cultural traits of individuals are chosen\nuniformly at random, the emphasis being on characterizing the model outcomes\nthat are independent of these (`annealed') initial conditions. Here, motivated\nby an increasing interest in forecasting social behavior in the real world, we\nreverse the point of view and focus on the effect of specific (`quenched')\ninitial conditions, including those obtained from real data, on the final\ncultural state. We study the predictability, rigorously defined in an\ninformation-theoretic sense, of the \\emph{social content} of the final cultural\ngroups (i.e. who ends up in which group) from the knowledge of the initial\ncultural traits. We find that, as compared to random and shuffled initial\nconditions, the hierarchical ultrametric-like organization of empirical\ncultural states significantly increases the predictability of the final social\ncontent by largely confining cultural convergence within the lower levels of\nthe hierarchy. Moreover, predictability correlates with the compatibility of\nshort-term social coordination and long-term cultural diversity, a property\nthat has been recently found to be strong and robust in empirical data. We also\nintroduce a null model generating initial conditions that retain the\nultrametric representation of real data. Using this ultrametric model,\npredictability is highly enhanced with respect to the random and shuffled\ncases, confirming the usefulness of the empirical hierarchical organization of\nculture for forecasting the outcome of social influence models.",
        "category": "physics_soc-ph"
    },
    {
        "text": "On Minimal Sets to Destroy the $k$-Core in Random Networks: We study the problem of finding the smallest set of nodes in a network whose\nremoval results in an empty $k$-core; where the $k$-core is the sub-network\nobtained after the iterative removal of all nodes of degree smaller than $k$.\nThis problem is also known in the literature as finding the minimal contagious\nset. The main contribution of our work is an analysis of the performance of the\nrecently introduced corehd algorithm [Scientific Reports, 6, 37954 (2016)] on\nrandom networks taken from the configuration model via a set of deterministic\ndifferential equations. Our analyses provides upper bounds on the size of the\nminimal contagious set that improve over previously known bounds. Our second\ncontribution is a new heuristic called the weak-neighbor algorithm that\noutperforms all currently known local methods in the regimes considered.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Intransitiveness in the Penney Game and in Random Walks on rings,\n  networks, communities and cities: The concept of intransitiveness for games, which is the condition for which\nthere is no first-player winning strategy can arise surprisingly, as happens in\nthe Penney game, an extension of the heads or tails. Since a game can be\nconverted into a random walk on a graph, i.e., a Markov process, we extend the\nintransitiveness concept to such systems. The end of the game generally\nconsists in the appearance of a pre-defined pattern. In the language of random\nwalk this corresponds to an absorbing trap, since once that the game has\nreached this condition the game comes to an end. Therefore, the\nintransitiveness of the game can be mapped into a problem of competition among\ntraps. We analyse in details random walkers on several kind of networks (rings,\nscale-free, hierarchical and city-inspired) with several variations: traps can\nbe partially absorbing, the walker can be biased and the initial distribution\ncan be arbitrary. We found that the transitivity concept can be quite useful\nfor characterizing the combined properties of a graph and that of the walkers.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Nonproliferation and fusion power plants: This is an abridged abstract; please see the full paper. This paper evaluates\nwhether the nuclear nonproliferation regime applies to fusion power plants and\nfinds that, legally, the regime does not apply. The paper then examines whether\nthe nonproliferation regime should apply to fusion based on a technical\nevaluation. The paper concludes that fusion should continue to fall outside the\nnonproliferation regime and that the global, dual-use export control regime,\nincluding potentially developing a \"controls by design\" usage-based control\nregime, is better suited for commercial fusion energy.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Community detection based on \"clumpiness\" matrix in complex networks: The \"clumpiness\" matrix of a network is used to develop a method to identify\nits community structure. A \"projection space\" is constructed from the\neigenvectors of the clumpiness matrix and a border line is defined using some\nkind of angular distance in this space. The community structure of the network\nis identified using this borderline and/or hierarchical clustering methods. The\nperformance of our algorithm is tested on some computer-generated and\nreal-world networks. The accuracy of the results is checked using normalized\nmutual information. The effect of community size heterogeneity on the accuracy\nof the method is also discussed.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Visualizing the \"Heartbeat\" of a City with Tweets: Describing the dynamics of a city is a crucial step to both understanding the\nhuman activity in urban environments and to planning and designing cities\naccordingly. Here we describe the collective dynamics of New York City and\nsurrounding areas as seen through the lens of Twitter usage. In particular, we\nobserve and quantify the patterns that emerge naturally from the hourly\nactivities in different areas of New York City, and discuss how they can be\nused to understand the urban areas. Using a dataset that includes more than 6\nmillion geolocated Twitter messages we construct a movie of the geographic\ndensity of tweets. We observe the diurnal \"heartbeat\" of the NYC area. The\nlargest scale dynamics are the waking and sleeping cycle and commuting from\nresidential communities to office areas in Manhattan. Hourly dynamics reflect\nthe interplay of commuting, work and leisure, including whether people are\npreoccupied with other activities or actively using Twitter. Differences\nbetween weekday and weekend dynamics point to changes in when people wake and\nsleep, and engage in social activities. We show that by measuring the average\ndistances to the heart of the city one can quantify the weekly differences and\nthe shift in behavior during weekends. We also identify locations and times of\nhigh Twitter activity that occur because of specific activities. These include\nearly morning high levels of traffic as people arrive and wait at air\ntransportation hubs, and on Sunday at the Meadowlands Sports Complex and Statue\nof Liberty. We analyze the role of particular individuals where they have large\nimpacts on overall Twitter activity. Our analysis points to the opportunity to\ndevelop insight into both geographic social dynamics and attention through\nsocial media analysis.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The nature and origin of heavy tails in retweet activity: Modern social media platforms facilitate the rapid spread of information\nonline. Modelling phenomena such as social contagion and information diffusion\nare contingent upon a detailed understanding of the information-sharing\nprocesses. In Twitter, an important aspect of this occurs with retweets, where\nusers rebroadcast the tweets of other users. To improve our understanding of\nhow these distributions arise, we analyse the distribution of retweet times. We\nshow that a power law with exponential cutoff provides a better fit than the\npower laws previously suggested. We explain this fit through the burstiness of\nhuman behaviour and the priorities individuals place on different tasks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Disorder Unleashes Panic in Bitcoin Dynamics: The behaviour of Bitcoin owners is reflected in the structure and the number\nof bitcoin transactions encoded in the Blockchain. Likewise, the behaviour of\nBitcoin traders is reflected in the formation of bullish and bearish trends in\nthe crypto market. In light of these observations, we wonder if human behaviour\nunderlies some relationship between the Blockchain and the crypto market. To\naddress this question, we map the Blockchain to a spin-lattice problem, whose\nconfigurations form ordered and disordered patterns, representing the behaviour\nof Bitcoin owners. This novel approach allows us to obtain time series suitable\nto detect a causal relationship between the dynamics of the Blockchain and\nmarket trends of the Bitcoin and to find that disordered patterns in the\nBlockchain precede Bitcoin panic selling. Our results suggest that human\nbehaviour underlying Blockchain evolution and the crypto market brings out a\nfascinating connection between disorder and panic in Bitcoin dynamics.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Recommender systems may enhance the discovery of novelties: Recommender systems are vital for shaping user online experiences. While some\nbelieve they may limit new content exploration and promote opinion\npolarization, a systematic analysis is still lacking. We present a model that\nexplores the influence of recommender systems on novel content discovery.\nSurprisingly, analytical and numerical findings reveal these techniques can\nenhance novelty discovery rates. Also, distinct algorithms with similar\ndiscovery rates yield varying opinion polarization outcomes. Our approach\noffers a framework to enhance recommendation techniques beyond accuracy\nmetrics.",
        "category": "physics_soc-ph"
    },
    {
        "text": "How to fit the degree distribution of the air network?: We investigate three different approaches for fitting the degree\ndistributions of China-, US- and the composite China+US air network, in order\nto reveal the nature of such distributions and the potential theoretical\nbackground on which they are based. Our first approach is the fitting with\nq-statistics probability distribution, done separately in two regimes. This\nyields acceptable outcomes but generates two sets of fitting parameters. The\nsecond approach is an entire fitting to all data points with the formula\nproposed by Tsallis et al. So far, this trial is not able to produce consistent\nresults. In the third approach, we fit the data with two composite\ndistributions which may lack theoretical support for the moment.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Fixation and Polarization in a Three-Species Opinion Dynamics Model: Motivated by the dynamics of cultural change and diversity, we generalize the\nthree-species constrained voter model on a complete graph introduced in [J.\nPhys. A 37, 8479 (2004)]. In this opinion dynamics model, a population of size\nN is composed of \"leftists\" and \"rightists\" that interact with \"centrists\": a\nleftist and centrist can both become leftists with rate (1+q)/2 or centrists\nwith rate (1-q)/2 (and similarly for rightists and centrists), where q denotes\nthe bias towards extremism (q>0) or centrism (q<0). This system admits three\nabsorbing fixed points and a \"polarization\" line along which a frozen mixture\nof leftists and rightists coexist. In the realm of Fokker-Planck equation, and\nusing a mapping onto a population genetics model, we compute the fixation\nprobability of ending in every absorbing state and the mean times for these\nevents. We therefore show, especially in the limit of weak bias and large\npopulation size when |q|~1/N and N>>1, how fluctuations alter the mean field\npredictions: polarization is likely when q>0, but there is always a finite\nprobability to reach a consensus; the opposite happens when q<0. Our findings\nare corroborated by stochastic simulations.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Experimental examination of the alleged influence of cultural\n  differences on single-file pedestrian fundamentals: Multiple studies have addressed what influences the fundamental diagram in\nsingle-file experiments. Several studies indicate that there are differences by\ngender when group composition is taken into account. Even for the simplest\nsystems, such as pedestrian flows in corridors, there are still some\ndeficiencies in the detailed understanding of this fundamental relationship.\nThe effect of cultural differences can only be seen in a different density and\nvelocity interval if a comparison is conducted between the two different\ncultural groups. Our results indicate that the velocities of male and female\npedestrians are different. For instance, when N = 20, the velocity is 0.65 to\n0.69m/s for Chinese, while for Ghanaians, the velocity is 0.51 to 0.62m/s.\nSimilarly, when the densities change N = 40 in Chinese, they are 0.26 to\n0.32m/s, while the Ghanaians are 0.21 to 0.28m/s. In addition, we investigated\nwhether pedestrian behavior, corridor length, body weight, and height between\nChinese and Ghanaian pedestrians influence cultural differences in a\nsingle-file fundamental diagram. Regression analysis makes it possible to\ndetermine whether there is a difference between the two groups.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Cooperation on Social Networks and Its Robustness: In this work we have used computer models of social-like networks to show by\nextensive numerical simulations that cooperation in evolutionary games can\nemerge and be stable on this class of networks. The amounts of cooperation\nreached are at least as much as in scale-free networks but here the population\nmodel is more realistic. Cooperation is robust with respect to different\nstrategy update rules, population dynamics, and payoff computation. Only when\nstraight average payoff is used or there is high strategy or network noise does\ncooperation decrease in all games and disappear in the Prisoner's Dilemma.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Mathematical model bridges disparate timescales of lifelong learning: Lifelong learning occurs on timescales ranging from minutes to decades.\nPeople can lose themselves in a new skill, practicing for hours until\nexhausted. And they can pursue mastery over days or decades, perhaps abandoning\nold skills entirely to seek out new challenges. A full understanding of\nlearning requires an account that integrates these timescales. Here, we present\na minimal quantitative model that unifies the nested timescales of learning.\nOur dynamical model recovers classic accounts of skill acquisition, and\ndescribes how learning emerges from moment-to-moment dynamics of motivation,\nfatigue, and work, while also situated within longer-term dynamics of skill\nselection, mastery, and abandonment. We apply this model to explore the\nbenefits and pitfalls of a variety of training regimes and to characterize\nindividual differences in motivation and skill development. Our model connects\npreviously disparate timescales -- and the subdisciplines that typically study\neach timescale in isolation -- to offer a unified account of the timecourse of\nskill acquisition.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Web of Connections between Tourism Companies in Elba: Structure and\n  Dynamics: Tourism destination networks are amongst the most complex dynamical systems,\ninvolving a myriad of human-made and natural resources. In this work we report\na complex network-based systematic analysis of the Elba (Italy) tourism\ndestination network, including the characterization of its structure in terms\nof a set of several traditional measurements, the investigation of its\nmodularity, as well as its comprehensive study in terms of the recently\nreported superedges approach. In particular, structural (the number of paths of\ndistinct lengths between pairs of nodes, as well as the number of reachable\ncompanies) and dynamical features (transition probabilities and the\ninward/outward activations and accessibilities) are measured and analyzed,\nleading to a series of important findings related to the interactions between\ntourism companies. Among the several reported results, it is shown that the\ntype and size of the companies influence strongly their respective activations\nand accessibilities, while their geographical position does not seem to matter.\nIt is also shown that the Elba tourism network is largely fragmented and\nheterogeneous, so that it could benefit from increased integration.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Beyond pairwise strategy updating in the prisoner's dilemma game: In spatial games players typically alter their strategy by imitating the most\nsuccessful or one randomly selected neighbor. Since a single neighbor is taken\nas reference, the information stemming from other neighbors is neglected, which\nbegets the consideration of alternative, possibly more realistic approaches.\nHere we show that strategy changes inspired not only by the performance of\nindividual neighbors but rather by entire neighborhoods introduce a\nqualitatively different evolutionary dynamics that is able to support the\nstable existence of very small cooperative clusters. This leads to phase\ndiagrams that differ significantly from those obtained by means of pairwise\nstrategy updating. In particular, the survivability of cooperators is possible\neven by high temptations to defect and over a much wider uncertainty range. We\nsupport the simulation results by means of pair approximations and analysis of\nspatial patterns, which jointly highlight the importance of local information\nfor the resolution of social dilemmas.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Bursty Human Dynamics: Bursty dynamics is a common temporal property of various complex systems in\nNature but it also characterises the dynamics of human actions and\ninteractions. At the phenomenological level it is a feature of all systems that\nevolve heterogeneously over time by alternating between periods of low and high\nevent frequencies. In such systems, bursts are identified as periods in which\nthe events occur with a rapid pace within a short time-interval while these\nperiods are separated by long periods of time with low frequency of events. As\nsuch dynamical patterns occur in a wide range of natural phenomena, their\nobservation, characterisation, and modelling have been a long standing\nchallenge in several fields of research. However, due to some recent\ndevelopments in communication and data collection techniques it has become\npossible to follow digital traces of actions and interactions of humans from\nthe individual up to the societal level. This led to several new observations\nof bursty phenomena in the new but largely unexplored area of human dynamics,\nwhich called for the renaissance to study these systems using research concepts\nand methodologies, including data analytics and modelling. As a result, a large\namount of new insight and knowledge as well as innovations have been\naccumulated in the field, which provided us a timely opportunity to write this\nbrief monograph to make an up-to-date review and summary of the observations,\nappropriate measures, modelling, and applications of heterogeneous bursty\npatterns occurring in the dynamics of human behaviour.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Consequence of reputation in an open-ended Naming Game: We study a modified version of the Naming Game, a recently introduced model\nwhich describes how shared vocabulary can emerge spontaneously in a population\nwithout any central control. In particular, we introduce a new mechanism that\nallows a continuous interchange with the external inventory of words. A novel\nplaying strategy, influenced by the hierarchical structure that individuals'\nreputation defines in the community, is implemented. We analyze how these\nfeatures influence the convergence times, the cognitive efforts of the agents\nand the scaling behavior in memory and time.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Risk aversion promotes cooperation: Cooperative dynamics are central to our understanding of many phenomena in\nliving and complex systems, including the transition to multicellularity, the\nemergence of eusociality in insect colonies, and the development of\nfull-fledged human societies. However, we lack a universal mechanism to explain\nthe emergence of cooperation across length scales, across species, and scalable\nto large populations of individuals. We present a novel framework for modelling\ncooperation games with an arbitrary number of players by combining reaction\nnetworks, methods from quantum mechanics applied to stochastic complex systems,\ngame theory and stochastic simulations of molecular reactions. Using this\nframework, we propose a novel and robust mechanism based on risk aversion that\nleads to cooperative behaviour in population games. Rather than individuals\nseeking to maximise payouts in the long run, individuals seek to obtain a\nminimum set of resources with a given level of confidence and in a limited time\nspan. We explicitly show that this mechanism leads to the emergence of new Nash\nequilibria in a wide range of cooperation games. Our results suggest that risk\naversion is a viable mechanism to explain the emergence of cooperation in a\nvariety of contexts and with an arbitrary number of individuals greater than\nthree.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Downside Risk analysis applied to Hedge Funds universe: Hedge Funds are considered as one of the portfolio management sectors which\nshows a fastest growing for the past decade. An optimal Hedge Fund management\nrequires an appropriate risk metrics. The classic CAPM theory and its Ratio\nSharpe fail to capture some crucial aspects due to the strong non-Gaussian\ncharacter of Hedge Funds statistics. A possible way out to this problem while\nkeeping the CAPM simplicity is the so-called Downside Risk analysis. One\nimportant benefit lies in distinguishing between good and bad returns, that is:\nreturns greater or lower than investor's goal. We revisit most popular Downside\nRisk indicators and provide new analytical results on them. We compute these\nmeasures by taking the Credit Suisse/Tremont Investable Hedge Fund Index Data\nand with the Gaussian case as a benchmark. In this way an unusual transversal\nlecture of the existing Downside Risk measures is provided.",
        "category": "physics_soc-ph"
    },
    {
        "text": "First results on applying a non-linear effect formalism to alliances\n  between political parties and buy and sell dynamics: We discuss a non linear extension of a model of alliances in politics,\nrecently proposed by one of us. The model is constructed in terms of operators,\ndescribing the \\emph{interest} of three parties to form, or not, some political\nalliance with the other parties. The time evolution of what we call \\emph{the\ndecision functions} is deduced by introducing a suitable hamiltonian, which\ndescribes the main effects of the interactions of the parties amongst\nthemselves and with their \\emph{environments}, {which are }generated by their\nelectors and by people who still have no clear {idea }for which party to vote\n(or even if to vote). The hamiltonian contains some non-linear effects, which\ntakes into account the role of a party in the decision process of the other two\nparties. Moreover, we show how the same hamiltonian can also be used to\nconstruct a formal structure which can describe the dynamics of buying and\nselling financial assets (without however implying a specific price setting\nmechanism).",
        "category": "physics_soc-ph"
    },
    {
        "text": "Allocation of seats in the European Parliament and a degressive\n  proportionality: Distribution of seats in The European Parliament postulated by Treaty of\nLisbon should be degressively proportional. The meaning of degressively\nproportional concept can be found in two principles annexed to the draft of\nEuropean Parliament resolution. The first, referred as the principle of fair\ndivision, states that \"the larger the population of a Member State, the greater\nis entitlement to a large number of seats\". The other condition, referred to as\nthe principle of relative proportionality, holds that \"the larger the\npopulation of a country, the more inhabitants are represented by each of its\nMembers of the EU\". We postulate a clear and fair method which determines\nuniquely a distribution of seats in the European Parliament which fulfil the\nrequirements of degressive proportionality. More generally, let $l_i$ be any\nnon-increasing sequence of real positive numbers. We say that a sequence of\nnatural numbers $m_{i}$ is degressively proportional with respect to the\nsequence $l_{i}$, if $m_{i}$ and $l_{i}/m_{i}$ are non-increasing sequences.\nOur method can be instrumental in uniquely determining a degressively\nproportional sequence $m_{i}$ with respect to $l_{i}$ which fulfils given\nconditions.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Network science: a review focused on tourism: This paper presents a review of the methods of the science of networks with\nan application to the field of tourism studies. The basic definitions and\ncomputational techniques are described and a case study (Elba, Italy) used to\nillustrate the effect of network typology on information diffusion. A static\nstructural characterization of the network formed by destination stakeholders\nis derived from stakeholder interviews and website link analysis. This is\nfollowed by a dynamic analysis of the information diffusion process within the\ndestination demonstrating that stakeholder cohesion and adaptive capacity have\na positive effect on information diffusion. The outcomes and the implications\nof this analysis for improving destination management are discussed.",
        "category": "physics_soc-ph"
    },
    {
        "text": "How fast can one overcome the paradox of the energy transition? A\n  physico-economic model for the European power grid: The paradox of the energy transition is that the low marginal costs of new\nrenewable energy sources (RES) drag electricity prices down and discourage\ninvestments in flexible productions that are needed to compensate for the lack\nof dispatchability of the new RES. The energy transition thus discourages the\ninvestments that are required for its own harmonious expansion. To investigate\nhow this paradox can be overcome, we argue that, under certain assumptions,\nfuture electricity prices are rather accurately modeled from the residual load\nobtained by subtracting non-flexible productions from the load. Armed with the\nresulting economic indicator, we investigate future revenues for European power\nplants with various degree of flexibility. We find that, if neither carbon\ntaxes nor fuel prices change, flexible productions would be financially\nrewarded better and sooner if the energy transition proceeds faster but at more\nor less constant total production, i.e. by reducing the production of thermal\npower plants at the same rate as the RES production increases. Less flexible\nproductions, on the other hand, would see their revenue grow more moderately.\nOur results indicate that a faster energy transition with a quicker withdrawal\nof thermal power plants would reward flexible productions faster.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Manifold Cities: Social variables of urban areas in the UK: In the 21st century ongoing rapid urbanization highlights the need to gain\ndeeper insights into the social structure of cities. While work on this\nchallenge can profit from abundant data sources, the complexity of this data\nitself proves to be a challenge. In this paper we use diffusion maps, a\nmanifold learning method, to discover hidden manifolds in the UK 2011 census\ndata set. The census key statistics and quick statistics report 1450 different\nstatistical features for each census output area. Here we focus primarily on\nthe city of Bristol and the surrounding countryside, comprising 3490 of these\noutput areas. Our analysis finds the main variables that span the census\nresponses, highlighting that university student density and poverty are the\nmost important explanatory variables of variation in census responses.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The anatomy of urban social networks and its implications in the\n  searchability problem: The appearance of large geolocated communication datasets has recently\nincreased our understanding of how social networks relate to their physical\nspace. However, many recurrently reported properties, such as the spatial\nclustering of network communities, have not yet been systematically tested at\ndifferent scales. In this work we analyze the social network structure of over\n25 million phone users from three countries at three different scales: country,\nprovinces and cities. We consistently find that this last urban scenario\npresents significant differences to common knowledge about social networks.\nFirst, the emergence of a giant component in the network seems to be controlled\nby whether or not the network spans over the entire urban border, almost\nindependently of the population or geographic extension of the city. Second,\nurban communities are much less geographically clustered than expected. These\ntwo findings shed new light on the widely-studied searchability in\nself-organized networks. By exhaustive simulation of decentralized search\nstrategies we conclude that urban networks are searchable not through\ngeographical proximity as their country-wide counterparts, but through an\nhomophily-driven community structure.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Robust Design Optimization for Egressing Pedestrians in Unknown\n  Environments: In this paper, we deal with a size-variable group of pedestrians moving in a\nunknown confined environment and searching for an exit. Pedestrian dynamics are\nsimulated by means of a recently introduced microscopic (agent-based) model,\ncharacterized by an exploration phase and an egress phase. First, we study the\nmodel to reveal the role of its main parameters and its qualitative properties.\nSecond, we tackle a robust optimization problem by means of the Particle Swarm\nOptimization method, aiming at reducing the time-to-target by adding in the\nwalking area multiple obstacles optimally placed and shaped. Robustness is\nsought against the number of people in the group, which is an uncertain\nquantity described by a random variable with given probability density\ndistribution.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Generating large scale-free networks with the Chung-Lu random graph\n  model: Random graph models are a recurring tool-of-the-trade for studying network\nstructural properties and benchmarking community detection and other network\nalgorithms. Moreover, they serve as test-bed generators for studying diffusion\nand routing processes on networks. In this paper, we illustrate how to generate\nlarge random graphs having a power-law degree distribution using the Chung--Lu\nmodel. In particular, we are concerned with the fulfilment of a fundamental\nhypothesis that must be placed on the model parameters, without which the\ngenerated graphs lose all the theoretical properties of the model, notably, the\ncontrollability of the expected node degrees and the absence of correlations\nbetween the degrees of two nodes joined by an edge. We provide explicit\nformulas for the model parameters to generate random graphs that have several\ndesirable properties, including a power-law degree distribution with any\nexponent larger than $2$, a prescribed asymptotic behaviour of the largest and\naverage expected degrees, and the presence of a giant component.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Smart Testing and Selective Quarantine for the Control of Epidemics: This paper is based on the observation that, during Covid-19 epidemic, the\nchoice of which individuals should be tested has an important impact on the\neffectiveness of selective confinement measures. This decision problem is\nclosely related to the problem of optimal sensor selection, which is a very\nactive research subject in control engineering. The goal of this paper is to\npropose a policy to smartly select the individuals to be tested. The main idea\nis to model the epidemics as a stochastic dynamic system and to select the\nindividual to be tested accordingly to some optimality criteria, e.g. to\nminimize the probability of undetected asymptomatic cases. Every day, the\nprobability of infection of the different individuals is updated making use of\nthe stochastic model of the phenomenon and of the information collected in the\nprevious days. Simulations for a closed community of 10000 individuals show\nthat the proposed technique, coupled with a selective confinement policy, can\nreduce the spread of the disease while limiting the number of individuals\nconfined if compared to the simple contact tracing of positive and to an\noff-line test selection strategy based on the number of contacts.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Causal Estimation of Stay-at-Home Orders on SARS-CoV-2 Transmission: Accurately estimating the effectiveness of stay-at-home orders (SHOs) on\nreducing social contact and disease spread is crucial for mitigating pandemics.\nLeveraging individual-level location data for 10 million smartphones, we\nobserve that by April 30th---when nine in ten Americans were under a\nSHO---daily movement had fallen 70% from pre-COVID levels. One-quarter of this\ndecline is causally attributable to SHOs, with wide demographic differences in\ncompliance, most notably by political affiliation. Likely Trump voters reduce\nmovement by 9% following a local SHO, compared to a 21% reduction among their\nClinton-voting neighbors, who face similar exposure risks and identical\ngovernment orders. Linking social distancing behavior with an epidemic model,\nwe estimate that reductions in movement have causally reduced SARS-CoV-2\ntransmission rates by 49%.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Diversity-induced resonance in the response to social norms: In this paper we focus on diversity-induced resonance, which was recently\nfound in bistable, excitable and other physical systems. We study the\nappearance of this phenomenon in a purely economic model of cooperating and\ndefecting agents. Agent's contribution to a public good is seen as a social\nnorm. So defecting agents face a social pressure, which decreases if\nfree-riding becomes widespread. In this model, diversity among agents naturally\nappears because of the different sensitivity towards the social norm. We study\nthe evolution of cooperation as a response to the social norm (i) for the\nreplicator dynamics, and (ii) for the logit dynamics by means of numerical\nsimulations. Diversity-induced resonance is observed as a maximum in the\nresponse of agents to changes in the social norm as a function of the degree of\nheterogeneity in the population. We provide an analytical, mean-field approach\nfor the logit dynamics and find very good agreement with the simulations. From\na socio-economic perspective, our results show that, counter-intuitively,\ndiversity in the individual sensitivity to social norms may result in a society\nthat better follows such norms as a whole, even if part of the population is\nless prone to follow them.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A new competition format to avoid collusion in three-team sports\n  tournaments: The usual FIFA World Cup-TM group stage format is eight groups of four teams\nfor a total of 32 teams. The top two teams from each group advance to the next\nround, resulting in a 16-team knockout stage. The next edition of the World Cup\nwill have 48 teams. Although FIFA initially proposed a configuration of 16\nthree-team groups, it has ultimately settled on 12 four-team groups. The format\nchosen, based on four-team groups, has clear organizational disadvantages and\neven a lack of fairness in the competition. But, in return, there is less risk\nof collusion in groups of four teams than in groups of three teams. This study\nproposes a modification to the three-team group format to reduce the risk of\ncollusion by making the order of the matches not predefined in advance. The\nresults obtained with Monte Carlo simulations show that the new three-team\ncompetition format with dynamic match order is comparable, in terms of\ncollusion risk, to the four-team group system commonly used in past World Cups.",
        "category": "physics_soc-ph"
    },
    {
        "text": "You better watch out: US COVID-19 wave dynamics versus vaccination\n  strategy: We employ the epidemic Renormalization Group (eRG) framework to understand,\nreproduce and predict the COVID-19 pandemic diffusion across the US. The human\nmobility across different geographical US divisions is modelled via open source\nflight data alongside the impact of social distancing for each such division.\nWe analyse the impact of the vaccination strategy on the current pandemic wave\ndynamics in the US. We observe that the ongoing vaccination campaign will not\nimpact the current pandemic wave and therefore strict social distancing\nmeasures must still be enacted. To curb the current and the next waves our\nresults indisputably show that vaccinations alone are not enough and strict\nsocial distancing measures are required until sufficient immunity is achieved.\nOur results are essential for a successful vaccination strategy in the US.",
        "category": "physics_soc-ph"
    },
    {
        "text": "An objective criterion for cluster detection in stochastic epidemic\n  models: The correct identification of clusters is crucial for an accurate monitoring\nof the spread of a disease and also in many other natural, social and physical\nphenomena which exhibit an epidemic structure. Nevertheless, even when an\naccurate mathematical model is available, no simple tool exists which allows\none to identify how many independent clusters are present and to link elements\nto the appropriate clusters. Here we develop an automatic method for the\ndetection of the internal structure of the clusters and their number,\nindependently of the model that describes the dynamics of the phenomenon. It is\nsubstantially based on the difference of the log-likelihood $\\delta {\\cal LL}$,\nthat is evaluated when all elements are connected and when they are grouped\ninto clusters. As a function of the number of connected elements $\\delta {\\cal\nLL}$ presents a change of slope and a singularity which can be both used in\ncluster identification. Our method is validated for an epidemic model with a\nminimal temporal structure and for the Epidemic Type Aftershock Sequence model\ndescribing the spatio-temporal clustering of earthquakes.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Forecasting and control in overlapping generations model: chaos\n  stabilization via artificial intelligence: Irregular, especially chaotic, behavior is often undesirable for economic\nprocesses because it presents challenges for predicting their dynamics. In this\nsituation, control of such a process by its mathematical model can be used to\nsuppress chaotic behavior and to transit the system from irregular to regular\ndynamics.\n  In this paper, we have constructed an overlapping generations model with a\ncontrol function. By applying evolutionary algorithms we showed that in the\nabsence of control, both regular and irregular behavior (periodic and chaotic)\ncould be observed in this model. We then used the synthesis of control by the\nPyragas control method with two control parameters to solve the problem of\ncontrolling the irregular behavior of the model. We solved a number of\noptimization problems applying evolutionary algorithms to select control\nparameters in order to ensure stability of periodic orbits. We compared\nqualitative and quantitative characteristics of the model's dynamics before and\nafter applying control and verified the results obtained using simulation.\n  We thus demonstrated that artificial intelligence technologies (in\nparticular, evolutionary algorithms) combined with the Pyragas control method\nare well suited for in-depth analysis and stabilization of irregular dynamics\nin the model considered in this paper.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Several Multiplexes in the same City: The role of wealth differences in\n  urban mobility: In this work we analyze the architecture of real urban mobility networks from\nthe multiplex perspective. In particular, based on empirical data about the\nmobility patterns in the cities of Bogot\\'a and Medell\\'{\\i}n, each city is\nrepresented by six multiplex networks, each one representing the\norigin-destination trips performed by a subset of the population corresponding\nto a particular socioeconomic status. The nodes of each multiplex are the\ndifferent urban locations whereas links represent the existence of a trip from\none node (origin) to another (destination). On the other hand, the different\nlayers of each multiplex correspond to the different existing transportation\nmodes. By exploiting the characterization of multiplex transportation networks\ncombining different transportation modes, we aim at characterizing the mobility\npatterns of each subset of the population. Our results show that the\nsocioeconomic characteristics of the population have an extraordinary impact in\nthe layer organization of these multiplex systems.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Spreading dynamics of infectious diseases on structured society with\n  daily cycles: We are facing a common serious issue, infectious diseases, and trying to\nsuppress the spreading of infection. We need less contact with each other to\ndecrease the chance of infection, but this means loss of economic activity, as\nwell. This tradeoff is inevitable in our society, because we still need direct\ncommunication and commuting, so far. The focus of our paper is the structure of\nsociety, on which we have direct contacts. We study on spreading process with\nartificial sosiety model, where each agent has daily cycle and go office and\nback home, every day. At the same time, infection spreads along SIR model. We\nshow both slow infection and short commuting can be realized with some\nstructures and vice versa. The most effective factor for such features is\nmodularity of society. In highly modular society, agents live around the\ndestined office, but agents commute long way to their office and can be\ninfected fast, in not modular society. The first infection point is one more\nfactor for the features. If the first infection takes place around the office,\ninfection spreads slower. On the contrary, if the first one takes place far\naway from the office, infection can be fast. We show a design principle, high\nmodularity and sparsely distributed offices, for good society and discuss on\npossible solutions in real society, where we live in.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Particle velocity controls phase transitions in contagion dynamics: Interactions often require the proximity between particles. The movement of\nparticles, thus, drives the change of the neighbors which are located in their\nproximity, leading to a sequence of interactions. In pathogenic contagion,\ninfections occur through proximal interactions, but at the same time the\nmovement facilitates the co-location of different strains. We analyze how the\nparticle velocity impacts on the phase transitions on the contagion process of\nboth a single infection and two cooperative infections. First, we identify an\noptimal velocity (close to half of the interaction range normalized by the\nrecovery time) associated with the largest epidemic threshold, such that\ndecreasing the velocity below the optimal value leads to larger outbreaks.\nSecond, in the cooperative case, the system displays a continuous transition\nfor low velocities, which becomes discontinuous for velocities of the order of\nthree times the optimal velocity. Finally, we describe these characteristic\nregimes and explain the mechanisms driving the dynamics.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Bring your friend! Real or virtual?: A monopolist faces a partially uninformed population of consumers,\ninterconnected through a directed social network. In the network, the\nmonopolist offers rewards to informed consumers (influencers) conditional on\ninforming uninformed consumers (influenced). Rewards are needed to bear a\ncommunication cost. We investigate the incentives for the monopolist to move to\na denser network and the impact of this decision on social welfare. Social\nwelfare increases in information diffusion which, for given communication\nincentives, is higher in denser networks. However, the monopolist internalizes\ntransfers and thus may prefer an environment with less competition between\ninformed consumers. The presence of highly connected influencers (hubs) is the\nmain driver that aligns monopolist incentives and welfare.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Information-sharing and aggregation models for interacting minds: We study mathematical models of the collaborative solving of a two-choice\ndiscrimination task. We estimate the difference between the shared performance\nfor a group of n observers over a single person performance. Our paper is a\ntheoretical extension of the recent work of Bahrami et al. (2010) from a dyad\n(a pair) to a group of n interacting minds. We analyze several models of\ncommunication, decision-making and hierarchical information-aggregation.\n  The maximal slope of psychometric function (closely related to the percentage\nof right answers vs. easiness of the task) is a convenient parameter\ncharacterizing performance. For every model we investigated, the group\nperformance turns out to be a product of two numbers: a scaling factor\ndepending of the group size and an average performance. The scaling factor is a\npower function of the group size (with the exponent ranging from 0 to 1),\nwhereas the average is arithmetic mean, quadratic mean, or maximum of the\nindividual slopes. Moreover, voting can be almost as efficient as more\nelaborate communication models, given the participants have similar individual\nperformances.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Replicator-mutator dynamics of linguistic convergence and divergence: People tend to align their use of language to the linguistic behaviour of\ntheir own ingroup and to simultaneously diverge from the language use of\noutgroups. This paper proposes to model this phenomenon of sociolinguistic\nidentity maintenance as an evolutionary game in which individuals play the\nfield and the dynamics are supplied by a multi-population extension of the\nreplicator-mutator equation. Using linearization, the stabilities of all\ndynamic equilibria of the game in its fully symmetric two-population special\ncase are found. The model is then applied to an empirical test case from\nadolescent sociolinguistic behaviour. It is found that the empirically attested\npopulation state corresponds to one of a number of stable equilibria of the\ngame under an independently plausible value of a parameter controlling the rate\nof linguistic mutations. An asymmetric three-population extension of the game,\nexplored with numerical solution methods, furthermore predicts to which\nspecific equilibrium the system converges.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Effects of updating rules on the coevolving prisoner's dilemma: We studied the effect of three strategy updating rules in coevolving\nprisoner's dilemma games where agents (nodes) can switch both the strategy and\nsocial partners. Under two node-based strategy updating rules, strategy\nupdating occurs between a randomly chosen focal node and its randomly selected\nneighbour. The focal agent becomes the strategy recipient and may imitate the\nstrategy of the neighbour according to the payoff difference, i.e.\nvoter-model-like dynamics (VMLD), or becomes a strategy donor and thus may be\nimitated by the neighbour, i.e. invasion-process-like dynamics (IPLD). For\nedge-based updating rules, one edge is selected, and the roles of the two\nconnected nodes (donor or recipient) are randomly decided, i.e. edge-based\ndynamics (EBD). A computer simulation shows that partner switching supports the\nevolution of cooperation under VMLD, which has been utilised in many studies on\nspatial evolutionary games, whereas cooperators often vanish under IPLD. The\nEBD results lie between these two processes. This difference is prominent among\nnodes with large degrees. In addition, partner switching induces a\nnon-monotonic relationship between the fraction of cooperators and intensity of\nselection under VMLD and EBD, and a weak or strong selection supports\ncooperation. In contrast, only a strong selection supports cooperators under\nIPLD. Similar differences in the enhancement of cooperation are observed when\ngames are played on static heterogeneous networks. Our results imply that the\ndirection of imitation is quite important for understanding the evolutionary\nprocess of cooperation.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Scaling relations and finite-size scaling in gravitationally correlated\n  lattice percolation models: In some systems, the connecting probability (and thus the percolation\nprocess) between two sites depends on the geometric distance between them. To\nunderstand such process, we propose gravitationally correlated percolation\nmodels for link-adding networks on the two-dimensional lattice $G$ with two\nstrategies $S_{\\rm max}$ and $S_{\\rm min}$, to add a link $l_{i,j}$ to connect\nsite $i$ and site $j$ with mass $m_i$ and $m_j$, respectively; $m_i$ and $m_j$\nare sizes of the clusters which contain site $i$ and site $j$, respectively.\nThe probability to add the link $l_{i,j}$ is related to the generalized gravity\n$g_{ij} \\equiv m_i m_j/r_{ij}^d$, where $r_{ij}$ is the geometric distance\nbetween $i$ and $j$, and $d$ is an adjustable decaying exponent. In the\nbeginning of the simulation, all sites of $G$ are occupied and there is no\nlink. In the simulation process, two inter-cluster links $l_{i,j}$ and\n$l_{k,n}$ are randomly chosen and the generalized gravities $g_{ij}$ and\n$g_{kn}$ are computed. In the strategy $S_{\\rm max}$, the link with larger\ngeneralized gravity is added. In the strategy $S_{\\rm min}$, the link with\nsmaller generalized gravity is added, which include percolation on the Erd\\H\nos-R\\'enyi random graph and the Achlioptas process of explosive percolation as\nthe limiting cases, $d \\to \\infty$ and $d \\to 0$, respectively. Adjustable\nstrategies facilitate or inhibit the network percolation in a generic view. We\ncalculate percolation thresholds $T_c$ and critical exponents $\\beta$ by\nnumerical simulations. We also obtain various finite-size scaling functions for\nthe node fractions in percolating clusters or arrival of saturation length with\ndifferent intervening strategies.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Modelling major failures in power grids in the whole range: Empirical research with electricity transmission networks reliability data\nshows that the size of major failures - in terms of energy not supplied (ENS),\ntotal loss of power (TLP) or restoration time (RT) - appear to follow a power\nlaw behaviour in the upper tail of the distribution. However, this pattern -\nalso known as Pareto distribution - is not valid in the whole range of those\nmajor events. We aimed to find a probability distribution that we could use to\nmodel them, and hypothesized that there is a two-parameter model that fits the\npattern of those data well in the entire domain. We considered the major\nfailures produced between 2002 and 2009 in the European power grid; analyzed\nthose reliability indicators: ENS, TLP and RT; fitted six alternative models:\nPareto II, Fisk, Lognormal, Pareto, Weibull and Gamma distributions, to the\ndata by maximum likelihood; compared these models by the Bayesian information\ncriterion; tested the goodness-of-fit of those models by a Kolmogorov-Smirnov\ntest method based on bootstrap resampling; and validated them graphically by\nrank-size plots. We found that Pareto II distribution is, in the case of ENS\nand TLP, an adequate model to describe major events reliability data of power\ngrids in the whole range, and in the case of RT, is the best choice of the six\nalternative models analyzed.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The strategy of survival for a competition between normal and anomalous\n  diffusion: In this paper, we study the competition of two diffusion processes for\nachieving the maximum possible diffusion in an area. This competition, however,\ndoes not occur in the same circumstance; one of these processes is a normal\ndiffusion with a higher growth rate, and another one is an anomalous diffusion\nwith a lower growth rate. The trivial solution of the proposed model suggests\nthat the winner is the one with the higher growth rate. But, the question is:\nwhat characteristics and strategies should the second diffusion include to\nprolong the survival in such a competition? The studied diffusion equations\ncorrespond to the SI model such that the anomalous diffusion has memory\ndescribed by a fractional order derivative. The strategy promise that anomalous\ndiffusion reaches maximum survival in case of forgetting some parts of the\nmemory. This model can represent some of real phenomena, such as the contest of\ntwo companies in a market share, the spreading of two epidemic diseases, the\ndiffusion of two species, or any reaction-diffusion related to real-world\ncompetition.",
        "category": "physics_soc-ph"
    },
    {
        "text": "How to design a multi-stage tournament when some results are carried\n  over?: The paper discusses the strategy-proofness of sports tournaments with\nmultiple group stages, where the results of matches already played in the\nprevious round against teams in the same group are carried over. These\ntournaments, widely used in handball and other sports, are shown to be\nincentive incompatible in the sense that a team can be strictly better off by\nnot exerting full effort in a game. Historical examples are presented when a\nteam was ex ante disinterested in winning by a high margin. We propose a family\nof incentive compatible designs. Their main characteristics are compared to the\noriginal format via simulations. Carrying over half of the points scored in the\nprevious round turns out to be a promising policy.",
        "category": "physics_soc-ph"
    },
    {
        "text": "On Watts' Cascade Model with Random Link Weights: We study an extension of Duncan Watts' 2002 model of information cascades in\nsocial networks where edge weights are taken to be random, an innovation\nmotivated by recent applications of cascade analysis to systemic risk in\nfinancial networks. The main result is a probabilistic analysis that\ncharacterizes the cascade in an infinite network as the fixed point of a\nvector-valued mapping, explicit in terms of convolution integrals that can be\nefficiently evaluated numerically using the fast Fourier transform algorithm. A\nsecond result gives an approximate probabilistic analysis of cascades on \"real\nworld networks\", finite networks based on a fixed deterministic graph.\nExtensive cross testing with Monte Carlo estimates shows that this approximate\nanalysis performs surprisingly well, and provides a flexible microscope that\ncan be used to investigate properties of information cascades in real world\nnetworks over a wide range of model parameters.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Competing Activists--Political Polarization: Recent empirical findings suggest that societies have become more polarized\nin various countries. That is, the median voter of today represents a smaller\nfraction of society compared to two decades ago and yet, the mechanisms\nunderlying this phenomenon are not fully understood. Since interactions between\ninfluential actors (\"activists\") and voters play a major role in opinion\nformation, e.g. through social media, we develop a macroscopic opinion model in\nwhich competing activists spread their political ideas in specific groups of\nsociety. These ideas spread further to other groups in declining strength.\nWhile unilateral spreading shifts the opinion distribution, competition of\nactivists leads to additional phenomena: Small heterogeneities among competing\nactivists cause them to target different groups in society, which amplifies\npolarization. For moderate heterogeneities, we obtain target cycles and further\namplification of polarization. In such cycles, the stronger activist\ndifferentiates himself from the weaker one, while the latter aims to imitate\nthe stronger activist.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Critical size of ego communication networks: With the help of information and communication technologies, studies on the\noverall social networks have been extensively reported recently. However,\ninvestigations on the directed Ego Communication Networks (ECNs) remain\ninsufficient, where an ECN stands for a sub network composed of a centralized\nindividual and his/her direct contacts. In this paper, the directed ECNs are\nbuilt on the Call Detail Records (CDRs), which cover more than 7 million people\nof a provincial capital city in China for half a year. Results show that there\nis a critical size for ECN at about 150, above which the average emotional\ncloseness between ego and alters drops, the balanced relationship between ego\nand network collapses, and the proportion of strong ties decreases. This paper\nnot only demonstrate the significance of ECN size in affecting its properties,\nbut also shows accordance with the \"Dunbar's Number\". These results can be\nviewed as a cross-culture supportive evidence to the well-known Social Brain\nHypothesis (SBH).",
        "category": "physics_soc-ph"
    },
    {
        "text": "Self-organized global control of carbon emissions: There is much disagreement concerning how best to control global carbon\nemissions. We explore quantitatively how different control schemes affect the\ncollective emission dynamics of a population of emitting entities. We uncover a\ncomplex trade-off which arises between average emissions (affecting the global\nclimate), peak pollution levels (affecting citizens' everyday health),\nindustrial efficiency (affecting the nation's economy), frequency of\ninstitutional intervention (affecting governmental costs), common information\n(affecting trading behavior) and market volatility (affecting financial\nstability). Our findings predict that a self-organized free-market approach at\nthe level of a sector, state, country or continent, can provide better control\nthan a top-down regulated scheme in terms of market volatility and monthly\npollution peaks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Research on the visitor flow pattern of Expo 2010: Expo 2010 Shanghai China was a successful, splendid and unforgettable event,\nremaining us with valuable experiences. The visitor flow pattern of Expo is\ninvestigated in this paper. The Hurst exponent, mean value and standard\ndeviation of visitor volume prove that the visitor flow is fractal with\nlong-term stability and correlation as well as obvious fluctuation in short\nperiod. Then the time series of visitor volume is converted to complex network\nby visibility algorithm. It can be inferred from the topological properties of\nthe visibility graph that the network is scale-free, small-world and\nhierarchically constructed, conforming that the time series are fractal and\nclose relationship exit between the visitor volume on different days.\nFurthermore, it is inevitable to show some extreme visitor volume in the\noriginal visitor flow, and these extreme points may appear in group to a great\nextent.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Uncertainty Reduction for Stochastic Processes on Complex Networks: Many real-world systems are characterized by stochastic dynamical rules where\na complex network of interactions among individual elements probabilistically\ndetermines their state. Even with full knowledge of the network structure and\nof the stochastic rules, the ability to predict system configurations is\ngenerally characterized by a large uncertainty. Selecting a fraction of the\nnodes and observing their state may help to reduce the uncertainty about the\nunobserved nodes. However, choosing these points of observation in an optimal\nway is a highly nontrivial task, depending on the nature of the stochastic\nprocess and on the structure of the underlying interaction pattern. In this\npaper, we introduce a computationally efficient algorithm to determine\nquasioptimal solutions to the problem. The method leverages network sparsity to\nreduce computational complexity from exponential to almost quadratic, thus\nallowing the straightforward application of the method to mid-to-large-size\nsystems. Although the method is exact only for equilibrium stochastic processes\ndefined on trees, it turns out to be effective also for out-of-equilibrium\nprocesses on sparse loopy networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A note on general epidemic region for infinite regular graphs: We study the contagion game with the bilingual option on infinite regular\ngraphs introduced and modeled mathematically in [N. Immorlica et al. (2007)].\nIn the reference, Immorlica et al. studied conditions for an innovation to\nbecome epidemic over infinite regular trees, the grid, and the infinite\nthick-lines in terms of payoff enhancement and cost of the bilingual option. We\nimproved their results by showing that the class of infinite regular trees make\nan innovation least advantageous to become epidemic considering the whole class\nof infinite regular graphs. Moreover, we show that any infinite\n$\\Delta$-regular graph containing the infinite $\\Delta$-tree structure is also\nleast advantageous to be epidemic. Also, we construct an infinite family of\ninfinite $\\Delta$-regular graphs (including the thick $\\Delta$-line) that is\nthe most advantageous to be epidemic as known so far.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The influence of persuasion in opinion formation and polarization: We present a model that explores the influence of persuasion in a population\nof agents with positive and negative opinion orientations. The opinion of each\nagent is represented by an integer number $k$ that expresses its level of\nagreement on a given issue, from totally against $k=-M$ to totally in favor\n$k=M$. Same-orientation agents persuade each other with probability $p$,\nbecoming more extreme, while opposite-orientation agents become more moderate\nas they reach a compromise with probability $q$. The population initially\nevolves to (a) a polarized state for $r=p/q>1$, where opinions' distribution is\npeaked at the extreme values $k=\\pm M$, or (b) a centralized state for $r<1$,\nwith most opinions around $k=\\pm 1$. When $r \\gg 1$, polarization lasts for a\ntime that diverges as $r^M \\ln N$, where $N$ is the population's size. Finally,\nan extremist consensus ($k=M$ or $-M$) is reached in a time that scales as\n$r^{-1}$ for $r \\ll 1$.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Economy Versus Disease Spread: Reopening Mechanisms for COVID 19: We study mechanisms for reopening economic activities that explore the trade\noff between containing the spread of COVID-19 and maximizing economic impact.\nThis is of current importance as many organizations, cities, and states are\nformulating reopening strategies. Our mechanisms, referred to as group\nscheduling, are based on partitioning the population into groups and scheduling\neach group on appropriate days with possible gaps (when all are quarantined).\nEach group interacts with no other group and, importantly, any person who is\nsymptomatic in a group is quarantined. Specifically, our mechanisms are\ncharacterized by three parameters $(g,d,t)$, where $g$ is the number of groups,\n$d$ is the number of days a group is continuously scheduled, and $t$ is the gap\nbetween cycles. We show that our mechanisms effectively trade off economic\nactivity for more effective control of the COVID-19 virus. In particular, we\nshow that the $(2,5,0)$ mechanism, which partitions the population into two\ngroups that alternatively work for five days each, flat lines the number of\nCOVID-19 cases quite effectively, while still maintaining economic activity at\n70% of pre-COVID-19 level. We also study mechanisms such as $(2,3,2)$ and\n$(3,3,0)$ that achieve a somewhat lower economic output (about 50%) at the cost\nof more aggressive control of the virus; these could be applicable in\nsituations when the disease spread is more rampant in the population. We\ndemonstrate the efficacy of our mechanisms by theoretical analysis and\nextensive experimental simulations on various epidemiological models. Our\nmechanisms prove beneficial just by regulating human interactions. Moreover,\nour results show that if the disease transmission (reproductive) rate is made\nlower by following social distancing, mask wearing, and other public health\nguidelines, it can further increase the efficacy of our mechanisms.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Exploring Spatial Segregation Induced by Competition Avoidance as\n  Driving Mechanism for Emergent Coexistence in Microbial Communities: This study investigates the role of spatial segregation, prompted by\ncompetition avoidance, as a key mechanism for emergent coexistence within\nmicrobial communities. Recognizing these communities as complex adaptive\nsystems, we challenge the sufficiency of mean-field pairwise interaction models\nand consider the impact of spatial dynamics. We developed an individual-based\nspatial simulation depicting bacterial movement through a pattern of random\nwalks influenced by competition avoidance, leading to the formation of\nspatially segregated clusters. This model was integrated with a Lotka-Volterra\nmetapopulation framework focused on competitive interactions. Our findings\nreveal that spatial segregation combined with low diffusion rates and high\ncompositional heterogeneity among patches can lead to emergent coexistence in\nmicrobial communities. This reveals a novel mechanism underpinning the\nformation of stable, coexisting microbe clusters, which is nonetheless\nincapable of promoting coexistence in the case of isolated pairs of species.\nThis study underscores the importance of considering spatial factors in\nunderstanding the dynamics of microbial ecosystems.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Mathematical model for hit phenomena as stochastic process of\n  interactions of human interactions: Mathematical model for hit phenomena in entertainments in the society is\npresented as stochastic process of interactions of human dynamics. The model\nuse only the time distribution of advertisement budget as input and the words\nof mouth (WOM) as posting in the social network system is used as the data to\ncompare with the calculated results. The unit of time is daily. The WOM\ndistribution in time is found to be very close to the residue distribution in\ntime. The calculations for Japanese motion picture market due to the\nmathematical model agree very well with the actual residue distribution in\ntime.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Betweenness centrality for temporal multiplexes: Betweenness centrality quantifies the importance of a vertex for the\ninformation flow in a network. We propose a flexible definition of betweenness\nfor temporal multiplexes, where geodesics are determined accounting for the\ntopological and temporal structure and the duration of paths. We propose an\nalgorithm to compute the new metric via a mapping to a static graph. We show\nthe importance of considering the temporal multiplex structure and an\nappropriate distance metric comparing the results with those obtained with\nstatic or single-layer metrics on a dataset of $\\sim 20$k European flights.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Wealth distribution and Pareto's law in the Hungarian medieval society: The distribution of wealth in the Hungarian medieval aristocratic society is\nreported and studied. The number of serf families belonging to a noble is taken\nas a measure of the corresponding wealth. Our results reveal the power-law\nnature of this distribution function, confirming the validity of the Pareto law\nfor such a society. The obtained Pareto index $\\alpha=0.92$ is however smaller\nthan the values currently reported in the literature. We argue that the value\nclose to 1, of the Pareto index is a consequence of the absence of a relevant\neconomic life in the targeted society, in agreement with the prediction of\nexisting wealth distribution models for the idealized case of independently\nacting agents. Models developed to explain city populations may also be adapted\nto justify our results.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The role of noise and initial conditions in the asymptotic solution of a\n  bounded confidence, continuous-opinion model: We study a model for continuous-opinion dynamics under bounded confidence. In\nparticular, we analyze the importance of the initial distribution of opinions\nin determining the asymptotic configuration. Thus, we sketch the structure of\nattractors of the dynamical system, by means of the numerical computation of\nthe time evolution of the agents density. We show that, for a given bound of\nconfidence, a consensus can be encouraged or prevented by certain initial\nconditions. Furthermore, a noisy perturbation is added to the system with the\npurpose of modeling the free will of the agents. As a consequence, the\nimportance of the initial condition is partially replaced by that of the\nstatistical distribution of the noise. Nevertheless, we still find evidence of\nthe influence of the initial state upon the final configuration for a short\nrange of the bound of confidence parameter.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Inferring human mobility using communication patterns: Understanding the patterns of mobility of individuals is crucial for a number\nof reasons, from city planning to disaster management. There are two common\nways of quantifying the amount of travel between locations: by direct\nobservations that often involve privacy issues, e.g., tracking mobile phone\nlocations, or by estimations from models. Typically, such models build on\naccurate knowledge of the population size at each location. However, when this\ninformation is not readily available, their applicability is rather limited. As\nmobile phones are ubiquitous, our aim is to investigate if mobility patterns\ncan be inferred from aggregated mobile phone call data alone. Using data\nreleased by Orange for Ivory Coast, we show that human mobility is well\npredicted by a simple model based on the frequency of mobile phone calls\nbetween two locations and their geographical distance. We argue that the\nstrength of the model comes from directly incorporating the social dimension of\nmobility. Furthermore, as only aggregated call data is required, the model\nhelps to avoid potential privacy problems.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Optimal transport with constraints: from mirror descent to classical\n  mechanics: Finding optimal trajectories for multiple traffic demands in a congested\nnetwork is a challenging task. Optimal transport theory is a principled\napproach that has been used successfully to study various transportation\nproblems. Its usage is limited by the lack of principled and flexible ways to\nincorporate realistic constraints. We propose a principled physics-based\napproach to impose constraints flexibly in such optimal transport problems.\nConstraints are included in mirror descent dynamics using the principle of\nD'Alembert-Lagrange from classical mechanics. This leads to a sparse, local and\nlinear approximation of the feasible set leading in many cases to closed-form\nupdates.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Identifying the temporal dynamics of densification and sparsification in\n  human contact networks: Temporal social networks of human interactions are preponderant in\nunderstanding the fundamental patterns of human behavior. In these networks,\ninteractions occur locally between individuals (i.e., nodes) who connect with\neach other at different times, culminating into a complex system-wide web that\nhas a dynamic composition. Dynamic behavior in networks occurs not only locally\nbut also at the global level, as systems expand or shrink due either to:\nchanges in the size of node population or variations in the chance of a\nconnection between two nodes. Here, we propose a numerical maximum-likelihood\nmethod to estimate population size and the probability of two nodes connecting\nat any given point in time. An advantage of the method is that it relies only\non aggregate quantities, which are easy to access and free from privacy issues.\nOur approach enables us to identify the simultaneous (rather than the\nasynchronous) contribution of each mechanism in the densification and\nsparsification of human contacts, providing a better understanding of how\nhumans collectively construct and deconstruct social networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Optimally Frugal Foraging: We introduce the \\emph{frugal foraging} model in which a forager performs a\ndiscrete-time random walk on a lattice, where each site initially contains\n$\\mathcal{S}$ food units. The forager metabolizes one unit of food at each step\nand starves to death when it last ate $\\mathcal{S}$ steps in the past. Whenever\nthe forager decides to eat, it consumes all food at its current site and this\nsite remains empty (no food replenishment). The crucial property of the forager\nis that it is \\emph{frugal} and eats only when encountering food within at most\n$k$ steps of starvation. We compute the average lifetime analytically as a\nfunction of frugality threshold and show that there exists an optimal strategy,\nnamely, a frugality threshold $k^*$ that maximizes the forager lifetime.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Collective Decision Dynamics in Group Evacuation: Modeling Tradeoffs and\n  Optimal Behavior: Quantifying uncertainties in collective human behavior and decision making is\ncrucial for ensuring public health and safety, enabling effective disaster\nresponse, informing the design of transportation and communication networks,\nand guiding the development of new technologies. However, modeling and\npredicting such behavior is notoriously difficult, due to the influence of a\nvariety of complex factors such as the availability and uncertainty of\ninformation, the interaction and influence of social groups and networks, the\ndegree of risk or time pressure involved in a situation, and differences in\nindividual personalities and preferences. Here, we develop a stochastic model\nof human decision making to describe the empirical behavior of subjects in a\ncontrolled experiment simulating a natural disaster scenario. We compare the\nobserved behavior to that of statistically optimal Bayesian decision makers,\nquantifying the extent to which human decisions are optimal and identifying the\nconditions in which sub-optimal decisions are made. Finally, we investigate how\nhuman evacuation strategies change when decisions are made in groups under a\nvariety of different rules, and whether these group strategy adjustments are\noptimal or beneficial.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Optimal Claiming of Social Security Benefits: Using a lifecycle framework with Epstein-Zin (1989) utility and a\nmixed-integer optimization approach, we compute the optimal age to claim Social\nSecurity benefits. Taking advantage of homogeneity, a sufficient statistic is\nthe ratio of wealth to the primary insurance amount (PIA). If the investor's\nwealth to PIA ratio exceeds a certain threshold, individuals should defer\nSocial Security for at least a year. The optimal threshold depends on mortality\nassumptions and an individual's utility preferences, but is less sensitive to\ncapital market assumptions. The threshold wealth to PIA ratio increases from\n5.5 for men and 5.2 for women at age 62 to 11.1 for men and 10.4 for women at\nage 69. Below the threshold wealth to PIA ratio, individuals claim Social\nSecurity to raise consumption. Above this level, investors can afford to fund\nconsumption out of wealth for at least one year, and then claim a higher\nbenefit.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Heterogeneous length of stay of hosts' movements and spatial epidemic\n  spread: Infectious diseases outbreaks are often characterized by a spatial component\ninduced by hosts' distribution, mobility, and interactions. Spatial models that\nincorporate hosts' movements are being used to describe these processes, to\ninvestigate the conditions for propagation, and to predict the spatial spread.\nSeveral assumptions are being considered to model hosts' movements, ranging\nfrom permanent movements to daily commuting, where the time spent at\ndestination is either infinite or assumes a homogeneous fixed value,\nrespectively. Prompted by empirical evidence, here we introduce a general\nmetapopulation approach to model the disease dynamics in a spatially structured\npopulation where the mobility process is characterized by a heterogeneous\nlength of stay. We show that large fluctuations of the length of stay, as\nobserved in reality, can have a significant impact on the threshold conditions\nfor the global epidemic invasion, thus altering model predictions based on\nsimple assumptions, and displaying important public health implications.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The influence of the network topology on epidemic spreading: The influence of the network's structure on the dynamics of spreading\nprocesses has been extensively studied in the last decade. Important results\nthat partially answer this question show a weak connection between the\nmacroscopic behavior of these processes and specific structural properties in\nthe network, such as the largest eigenvalue of a topology related matrix.\nHowever, little is known about the direct influence of the network topology on\nmicroscopic level, such as the influence of the (neighboring) network on the\nprobability of a particular node's infection. To answer this question, we\nderive both an upper and a lower bound for the probability that a particular\nnode is infective in a susceptible-infective-susceptible model for two cases of\nspreading processes: reactive and contact processes. The bounds are derived by\nconsidering the $n-$hop neighborhood of the node; the bounds are tighter as one\nuses a larger $n-$hop neighborhood to calculate them. Consequently, using local\ninformation for different neighborhood sizes, we assess the extent to which the\ntopology influences the spreading process, thus providing also a strong\nmacroscopic connection between the former and the latter. Our findings are\ncomplemented by numerical results for a real-world e-mail network. A very good\nestimate for the infection density $\\rho$ is obtained using only 2-hop\nneighborhoods which account for 0.4% of the entire network topology on average.",
        "category": "physics_soc-ph"
    },
    {
        "text": "N-body decomposition of bipartite networks: In this paper, we present a method to project co-authorship networks, that\naccounts in detail for the geometrical structure of scientists collaborations.\nBy restricting the scope to 3-body interactions, we focus on the number of\ntriangles in the system, and show the importance of multi-scientists (more than\n2) collaborations in the social network. This motivates the introduction of\ngeneralized networks, where basic connections are not binary, but involve\narbitrary number of components. We focus on the 3-body case, and study\nnumerically the percolation transition.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Democracy and polarization in the National Assembly of the Republic of\n  Korea: The median-voter hypothesis predicts convergence of party platforms across a\none-dimensional political spectrum during majoritarian elections. Assuming that\nthe convergence is reflected in legislative activity, we study the time\nevolution of political polarization in the National Assembly of the Republic of\nKorea for the past 70 years. By projecting the correlation of lawmakers onto\nthe first principal axis, we observe a high degree of polarization from the\nearly 1960's to the late 1980's before democratization. As predicted by the\nhypothesis, it showed a sharp decrease when party politics were revived in\n1987. Since then, the political landscape has become more and more\nmulti-dimensional under the action of party politics, which invalidates the\nassumption behind the hypothesis. For comparison, we also analyze\nco-sponsorship in the United States House of Representatives from 1979 to 2020,\nwhose correlation matrix has been constantly high-dimensional throughout the\nobservation period. Our analysis suggests a pattern of polarization evolving\nwith democratic development, from which we can argue the power and the\nlimitation of the median-voter hypothesis as an explanation of real politics.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Grand challenges in social physics: In pursuit of moral behavior: Methods of statistical physics have proven valuable for studying the\nevolution of cooperation in social dilemma games. However, recent empirical\nresearch shows that cooperative behavior in social dilemmas is only one kind of\na more general class of behavior, namely moral behavior, which includes\nreciprocity, respecting others' property, honesty, equity, efficiency, as well\nas many others. Inspired by these experimental works, we here open up the path\ntowards studying other forms of moral behavior with methods of statistical\nphysics. We argue that this is a far-reaching direction for future research\nthat can help us answer fundamental questions about human sociality. Why did\nour societies evolve as they did? What moral principles are more likely to\nemerge? What happens when different moral principles clash? Can we predict the\nbreak out of moral conflicts in advance and contribute to their solution? These\nare amongst the most important questions of our time, and methods of\nstatistical physics could lead to new insights and contribute towards finding\nanswers.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Topological Phase Transitions in Spatial Networks: Most social, technological and biological networks are embedded in a finite\ndimensional space, and the distance between two nodes influences the likelihood\nthat they link to each other. Indeed, in social systems, the chance that two\nindividuals know each other drops rapidly with the distance between them; in\nthe cell, proteins predominantly interact with proteins in the same cellular\ncompartment; in the brain, neurons mainly link to nearby neurons. Most modeling\nframeworks that aim to capture the empirically observed degree distributions\ntend to ignore these spatial constraints. In contrast, models that account for\nthe role of the physical distance often predict bounded degree distributions,\nin disagreement with the empirical data. Here we address a long-standing gap in\nthe spatial network literature by deriving several key network characteristics\nof spatial networks, from the analytical form of the degree distribution to\npath lengths and local clustering. The mathematically exact results predict the\nexistence of two distinct phases, each governed by a different dynamical\nequation, with distinct testable predictions. We use empirical data to offer\ndirect evidence for the practical relevance of each of these phases in real\nnetworks, helping better characterize the properties of spatial networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Universality, Correlations, and Rankings in the Brazilian Universities\n  National Admission Examinations: The scores obtained by students that have performed the ENEM exam, the\nBrazilian High School National Examination used to admit students at the\nBrazilian universities, is analyzed. The average high school's scores are\ncompared between different disciplines through the Pearson correlation\ncoefficient. The results show a very large correlation between the performance\nin the different subjects. Even thought the students' scores in the ENEM due to\nthe standardization form a Gaussian, we show that the high schools' scores form\na bimodal distribution that can not be used to evaluate and compare performance\nover time. We also show that this high schools distribution reflects the\ncorrelation between school performance and economic level of the students. The\nENEM's scores are compared with a Brazilian non standardized exam, the entrance\nexam at the Universidade Federal do Rio Grande do Sul. The comparison of the\nperformance of the same individuals in both tests is compared showing that the\ntwo tests not only select different abilities but chooses a different set of\nindividuals. Our results indicates that standardized exams might be an\ninteresting tool to compare performance over the years but only of individuals\nand not of institutions.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Noisy multistate voter model for flocking in finite dimensions: We study a model for the collective behavior of self-propelled particles\nsubject to pairwise copying interactions and noise. Particles move at a\nconstant speed $v$ on a two--dimensional space and, in a single step of the\ndynamics, each particle adopts the direction of motion of a randomly chosen\nneighboring particle, with the addition of a perturbation of amplitude $\\eta$\n(noise). We investigate how the global level of particles' alignment (order) is\naffected by their motion and the noise amplitude $\\eta$. In the static case\nscenario $v=0$ where particles are fixed at the sites of a square lattice and\ninteract with their first neighbors, we find that for any noise $\\eta_c>0$ the\nsystem reaches a steady state of complete disorder in the thermodynamic limit,\nwhile for $\\eta=0$ full order is eventually achieved for a system with any\nnumber of particles $N$. Therefore, the model displays a transition at zero\nnoise when particles are static, and thus there are no ordered steady states\nfor a finite noise ($\\eta>0$). We show that the finite-size transition noise\nvanishes with $N$ as $\\eta_c^{1D} \\sim N^{-1}$ and $\\eta_c^{2D} \\sim \\left(N\n\\ln N \\right)^{-1/2}$ in one and two--dimensional lattices, respectively, which\nis linked to known results on the behavior of a type of noisy voter model for\ncatalytic reactions. When particles are allowed to move in the space at a\nfinite speed $v>0$, an ordered phase emerges, characterized by a fraction of\nparticles moving in a similar direction. The system exhibits an order-disorder\nphase transition at a noise amplitude $\\eta_c>0$ that is proportional to $v$,\nand that scales approximately as $\\eta_c \\sim v \\, (-\\ln v)^{-1/2}$ for $v \\ll\n1$. These results show that the motion of particles is able to sustain a state\nof global order in a system with voter-like interactions.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Entropic measure unveils country competitiveness and product\n  specialization in the World trade web: We show how the Shannon entropy function can be used as a basis to set up\ncomplexity measures weighting the economic efficiency of countries and the\nspecialization of products beyond bare diversification. This entropy function\nguarantees the existence of a fixed point which is rapidly reached by an\niterative scheme converging to our self-consistent measures. Our approach\nnaturally allows to decompose into inter-sectorial and intra-sectorial\ncontributions the country competitivity measure if products are partitioned\ninto larger categories. Besides outlining the technical features and advantages\nof the method, we describe a wide range of results arising from the analysis of\nthe obtained rankings and we benchmark these observations against those\nestablished with other economical parameters. These comparisons allow to\npartition countries and products into various main typologies, with\nwell-revealed characterizing features. Our methods have wide applicability to\ngeneral problems of ranking in bipartite networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Imbalance of pairwise efficiency in urban street network: We investigate how efficient each area of urban street network is connected.\nUsing the detour index, geographic features of street network and pairwise\nefficiency is studied. To do so, the detour index of 1,832,118 travel route\npairs for 85 global cities are explored. We show that the detour index in urban\nstreet network is strongly dependent on the angular separation of a pair\nrespective to the city center and that it is a unique property introduced by\nthe intrinsic core-periphery structure of urban street networks. Lastly, a new\nway of mapping the street network to visualize the overview of efficiency level\nis proposed.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Structural Gender Imbalances in Ballet Collaboration Networks: Ballet, a mainstream performing art predominantly associated with women,\nexhibits significant gender imbalances in leading positions. However, the\ncollaboration's structural composition on gender representation in the field\nremains unexplored. Our study investigates the gendered labor force composition\nand collaboration patterns in ballet creations. Our findings reveal gender\ndisparities in ballet creations aligned with gendered collaboration patterns\nand women occupying more peripheral network positions respect to men.\nProductivity disparities show women accessing 20-25\\% of ballet creations\ncompared to men. Mathematically derived perception errors show the\nunderestimation of women artists' representation within ballet collaboration\nnetworks, potentially impacting women's careers in the field. Our study\nhighlights the structural disadvantages that women face in ballet and\nemphasizes the need for a more inclusive and equal professional environment to\nimprove the career development of women in the ballet industry. These insights\ncontribute to a broader understanding of structural gender imbalances in\nartistic domains and can inform cultural organizations about potential\naffirmative actions towards a better representation of women leaders in ballet.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Phase transitions in optimally robust network structures: If we add links to a network at random, a critical threshold can be crossed\nwhere a giant connected component forms. Conversely, if links or nodes are\nremoved at random, the giant component shrinks and eventually breaks. In this\npaper, we explore which can optimally withstand random removal of a known\nproportion of nodes. When optimizing the size of the giant component after the\nattack, the network undergoes an infinite sequence of continuous phase\ntransitions between different optimal structures as the removed proportion of\nnodes is increased. When optimizing the proportion of links in the giant\ncomponent, a similar infinite sequence is observed, but the transitions are now\ndiscontinuous.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Revealing role of Korean Physics Society with keyword co-occurrence\n  network: Science and society inevitably interact with each other and evolve together.\nStudying the trend of science helps recognize leading topics significant for\nresearch and establish better policies to allocate funds efficiently. Scholarly\nsocieties such as the Korean Physics Society (KPS) also play an important role\nin the history of science. Figuring out the role of these scholarly societies\nmotivate our research related with our society since societies pay attention to\nimprove our society. Although several studies try to capture the trend of\nscience leveraging scientific documents such as paper or patents, but these\nstudies limited their research scope only to the academic world, neglecting the\ninteraction with society. Here we try to understand the trend of science along\nwith society using a public magazine named \"Physics and High Technology,\"\npublished by the Korean Physics Society (KPS). We build keyword co-occurrence\nnetworks for each time period and applied community detection to capture the\nkeyword structure and tracked the structure's evolution. In the networks, a\nresearch-related cluster is consistently dominant over time, and sub-clusters\nof the research-related cluster divide into various fields of physics, implying\nspecialization of the physics discipline. Also, we found that education and\npolicy clusters appear consistently, revealing the KPS's contribution to\nscience and society. Furthermore, we applied PageRank algorithm to selected\nkeywords ('semiconductor', 'woman', 'evading'...) to investigate the temporal\nchange of the importance of keywords in the network. For example, the\nimportance of the keyword 'woman' increases as time goes by, indicating that\nacademia also pays attention to gender issues reflecting the social movement in\nrecent years.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Understanding the Vegetable Oil Debate and Its Implications for\n  Sustainability through Social Media: The global production and consumption of vegetable oils have sparked several\ndiscussions on sustainable development. This study analyzes over 20 million\ntweets related to vegetable oils to explore the key factors shaping public\nopinion. We found that coconut, olive, and palm oils dominate social media\ndiscourse despite their lower contribution to overall global vegetable\nproduction. The discussion about olive and palm oils remarkably correlates with\nTwitter's growth, while coconut increases more significantly with bursts of\nactivity. Discussions around coconut and olive oils primarily focus on health,\nbeauty, and food, while palm draws attention to pressing environmental\nconcerns. Overall, virality is related to environmental issues and negative\nconnotations. In the context of Sustainable Development Goals, this study\nhighlights the multifaceted nature of the vegetable oil debate and its\ndisconnection from scientific discussions. Our research sheds light on the\npower of social media in shaping public perception, providing insights into\nsustainable development strategies.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Word sense disambiguation via high order of learning in complex networks: Complex networks have been employed to model many real systems and as a\nmodeling tool in a myriad of applications. In this paper, we use the framework\nof complex networks to the problem of supervised classification in the word\ndisambiguation task, which consists in deriving a function from the supervised\n(or labeled) training data of ambiguous words. Traditional supervised data\nclassification takes into account only topological or physical features of the\ninput data. On the other hand, the human (animal) brain performs both low- and\nhigh-level orders of learning and it has facility to identify patterns\naccording to the semantic meaning of the input data. In this paper, we apply a\nhybrid technique which encompasses both types of learning in the field of word\nsense disambiguation and show that the high-level order of learning can really\nimprove the accuracy rate of the model. This evidence serves to demonstrate\nthat the internal structures formed by the words do present patterns that,\ngenerally, cannot be correctly unveiled by only traditional techniques.\nFinally, we exhibit the behavior of the model for different weights of the low-\nand high-level classifiers by plotting decision boundaries. This study helps\none to better understand the effectiveness of the model.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Small Bots, Big Impact: Solving the Conundrum of Cooperation in Optional\n  Prisoner's Dilemma Game through Simple Strategies: Cooperation plays a crucial role in both nature and human society, and the\nconundrum of cooperation attracts the attention from interdisciplinary\nresearch. In this study, we investigated the evolution of cooperation in\noptional prisoner's dilemma games by introducing simple bots. We focused on\none-shot and anonymous games, where the bots could be programmed to always\ncooperate, always defect, never participate, or choose each action with equal\nprobability. Our results show that cooperative bots facilitate the emergence of\ncooperation among ordinary players in both well-mixed populations and a regular\nlattice under weak imitation scenarios. Introducing loner bots has no impact on\nthe emergence of cooperation in well-mixed populations, but it facilitates the\ndominance of cooperation in regular lattices under strong imitation scenarios.\nHowever, too many loner bots on a regular lattice inhibit the spread of\ncooperation and can eventually result in a breakdown of cooperation. Our\nfindings emphasize the significance of bot design in promoting cooperation and\noffer useful insights for encouraging cooperation in real-world scenarios.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The comfortable driving model revisited: Traffic phases and phase\n  transitions: We study the spatiotemporal patterns resulting from different boundary\nconditions for a microscopic traffic model and contrast it with empirical\nresults. By evaluating the time series of local measurements, the local traffic\nstates are assigned to the different traffic phases of Kerner's three-phase\ntraffic theory. For this classification we use the rule-based FOTO-method,\nwhich provides `hard' rules for this assignment. Using this approach, our\nanalysis shows that the model is indeed able to reproduce three qualitatively\ndifferent traffic phases: free flow (F), synchronized traffic (S), and wide\nmoving jams (J). In addition, we investigate the likelihood of transitions\nbetween the three traffic phases. We show that a transition from free flow (F)\nto a wide moving jam (J) often involves an intermediate transition; first from\nfree flow F to synchronized flow S and then from synchronized flow to a wide\nmoving jam. This is supported by the fact that the so called F->S transition\n(from free flow to synchronized traffic) is much more likely than a direct F->J\ntransition.\n  The model under consideration has a functional relation between traffic flow\nand traffic density. The fundamental hypothesis of the three-phase traffic\ntheory, however, postulates that the steady states of synchronized flow occupy\na two-dimensional region in the flow-density plane. Due to the obvious\ndiscrepancy between the model investigated here and the postulate of the\nthree-phase traffic theory, the good agreement that we found could not be\nexpected. For a more detailed analysis, we also studied the vehicle dynamics at\na microscopic level and provide a comparison of real detector data with\nsimulated data of the identical highway segment.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Detecting communities using asymptotical Surprise: Nodes in real-world networks are repeatedly observed to form dense clusters,\noften referred to as communities. Methods to detect these groups of nodes\nusually maximize an objective function, which implicitly contains the\ndefinition of a community. We here analyze a recently proposed measure called\nsurprise, which assesses the quality of the partition of a network into\ncommunities. In its current form, the formulation of surprise is rather\ndifficult to analyze. We here therefore develop an accurate asymptotic\napproximation. This allows for the development of an efficient algorithm for\noptimizing surprise. Incidentally, this leads to a straightforward extension of\nsurprise to weighted graphs. Additionally, the approximation makes it possible\nto analyze surprise more closely and compare it to other methods, especially\nmodularity. We show that surprise is (nearly) unaffected by the well known\nresolution limit, a particular problem for modularity. However, surprise may\ntend to overestimate the number of communities, whereas they may be\nunderestimated by modularity. In short, surprise works well in the limit of\nmany small communities, whereas modularity works better in the limit of few\nlarge communities. In this sense, surprise is more discriminative than\nmodularity, and may find communities where modularity fails to discern any\nstructure.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Reckoning with the wicked problems of nuclear technology: Philosophy,\n  design, and pedagogical method underlying a course on Nuclear Technology,\n  Policy, and Society: This paper describes the underlying philosophy, design, and implementation of\na course on \"Nuclear Technology, Policy, and Society\" taught in the Department\nof Nuclear Engineering and Radiological Sciences at the University of Michigan.\nThe course explores some of nuclear technology's most pressing challenges or\nits 'wicked problems'. Through this course students explore the origins of\nthese problems be they social or technical and, they are offered tools, both\nconceptual and methodological to make sense of these problems, and guided\nthrough a semester-long exploration of how scientists engineers can work\ntowards their resolution, and to what degree these problems can be solved\nthrough institutional transformation or a transformation in our own practices\nand norms as a field. The underlying pedagogical philosophy, implementation,\nand response to the course are described here for other instructors who might\nwish to create a similar course, or for non-academic nuclear scientists and\nengineers, who might perhaps, in these pages, find a vocabulary for\narticulating and reflecting on the nature of these problems as encountered in\ntheir praxis.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Quantifying and identifying the overlapping community structure in\n  networks: It has been shown that the communities of complex networks often overlap with\neach other. However, there is no effective method to quantify the overlapping\ncommunity structure. In this paper, we propose a metric to address this\nproblem. Instead of assuming that one node can only belong to one community,\nour metric assumes that a maximal clique only belongs to one community. In this\nway, the overlaps between communities are allowed. To identify the overlapping\ncommunity structure, we construct a maximal clique network from the original\nnetwork, and prove that the optimization of our metric on the original network\nis equivalent to the optimization of Newman's modularity on the maximal clique\nnetwork. Thus the overlapping community structure can be identified through\npartitioning the maximal clique network using any modularity optimization\nmethod. The effectiveness of our metric is demonstrated by extensive tests on\nboth the artificial networks and the real world networks with known community\nstructure. The application to the word association network also reproduces\nexcellent results.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Influence of Urban Morphology on Water Scarcity: Cities play a vital role in providing water to vast populations. Due to an\nincreasing population and the urbanization process, urban water demand is\nexpected to double by 2050. However, some cities are expanding in locations\nwith limited water retention capacity, scarce rainfall and unfavourable shape.\nThis study quantifies the impact of urban form on water scarcity across more\nthan 100 cities in Latin America, Asia, and Africa. The analysis integrates\nindicators of urban morphology, satellite imagery for natural resources, and\nterrain metrics to model cities' influence on water accessibility. Water\ntariffs, proximity to critical infrastructure, and access to piped water are\nused to assess scarcity in urban areas. Results show that locations that are\nfar from the centre, with smaller buildings and fewer constructed surfaces, are\nless likely to have water access. Urban sprawl negatively impacts water\navailability within a city, decreases proximity to critical infrastructure and\nincreases water tariffs. Keeping everything else constant, a location that is\ntwice as far from the city centre has 38% less proximity to critical\ninfrastructure. Also, water tariffs are up to 75% higher and the capacity of\ncities to provide water drops by half in more sparse cities.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Multichannel social signatures and persistent features of ego networks: The structure of egocentric networks reflects the way people balance their\nneed for strong, emotionally intense relationships and a diversity of weaker\nties. Egocentric network structure can be quantified with 'social signatures',\nwhich describe how people distribute their communication effort across the\nmembers (alters) of their personal networks. Social signatures based on call\ndata have indicated that people mostly communicate with a few close alters;\nthey also have persistent, distinct signatures. To examine if these results\nhold for other channels of communication, here we compare social signatures\nbuilt from call and text message data, and develop a way of constructing mixed\nsocial signatures using both channels. We observe that all types of signatures\ndisplay persistent individual differences that remain stable despite the\nturnover in individual alters. We also show that call, text, and mixed\nsignatures resemble one another both at the population level and at the level\nof individuals. The consistency of social signatures across individuals for\ndifferent channels of communication is surprising because the choice of channel\nappears to be alter-specific with no clear overall pattern, and ego networks\nconstructed from calls and texts overlap only partially in terms of alters.\nThese results demonstrate individuals vary in how they allocate their\ncommunication effort across their personal networks and this variation is\npersistent over time and across different channels of communication.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Trends in condensed matter physics: is research going faster and faster?: In this paper we study research trends in condensed matter physics. Trends\nare analyzed by means of the the number of publications in the different\nsub-fields as function of the years. We found that many research topics have a\nsimilar behavior with an initial fast growth and a next slower exponential\ndecay. We derived a simple model to describe this behavior and built up some\npredictions for future trends.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Critical Time Windows for Renewable Resource Complementarity Assessment: This paper proposes a systematic framework to assess the complementarity of\nrenewable resources over arbitrary geographical scopes and temporal scales\nwhich is particularly well-suited to exploit very large data sets of\nclimatological data. The concept of critical time windows is introduced, and a\nspatio-temporal criticality indicator is proposed, consisting in a parametrised\nfamily of scalar indicators quantifying the complementarity between renewable\nresources in both space and time. The criticality indicator is leveraged to\ndevise a family of optimisation problems identifying sets of locations with\nmaximum complementarity under arbitrary geographical deployment constraints.\nThe applicability of the framework is shown in a case study investigating the\ncomplementarity between the wind regimes in continental western Europe and\nsouthern Greenland, and its usefulness in a power system planning context is\ndemonstrated. Besides showing that the occurrence of low wind power production\nevents can be significantly reduced on a regional scale by exploiting diversity\nin local wind patterns, results highlight the fact that aggregating wind power\nproduction sites located on different continents may result in a lower\noccurrence of system-wide low wind power production events and indicate\npotential benefits of intercontinental electrical interconnections.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Standardizing Representation for Equality with a Population Seat Index: Proportional representation (PR) has long been believed the ideal system for\nthe equality of individuals in apportioning the seats of a legislature body to\nsubgroups. We observe that PR implicitly assumes the (standard) number of\nrepresentatives is proportional to the population, a situation no longer\nobserved since 1820s. To address this issue, we suggest to formulate the\napportionment problem in a broader context by explicitly specifying a standard\nfunction $f$ such that $f(p)$ is the standard, possibly fractional number of\nrepresentatives for population $p$, where PR assumes $f(p)\\propto p$. For this\ngeneralized apportionment problem, we give a population seat index (PSI)\n$\\frac{f^{-1}(s)}{p}$ for quantifying the contribution of an individual in\nassigning $s$ seats to a population $p$, where $f^{-1}$ is the inverse of $f$.\nWith the PSI, we derive apportioning schemes with absolute and relative\nindividual equality. Particularly, for $s$ seats, populations $p_1, \\ldots,\np_k$, and a standard function $f(p) = a + b p^\\gamma$ with constants $a, b,\n\\gamma \\ge 0$, the ideal, possibly fractional number of seats for subgroup $i$\nis $a + \\frac{(S-ka)p_i^{\\gamma}}{\\sum p_j^{\\gamma}}$, not $\\frac{Sp_i}{\\sum\np_j}$ calculated by PR which works only for $a=0$, $\\gamma=1$. Finally, since\nreal-world observations indicate a standard function $f \\propto p^\\gamma$ with\n$\\gamma < 1$, we conclude that PR represents individuals in less populous\nsubgroups less than individuals in more populous subgroups.",
        "category": "physics_soc-ph"
    },
    {
        "text": "What do leaders know?: The ability of a society to make the right decisions on relevant matters\nrelies on its capability to properly aggregate the noisy information spread\nacross the individuals it is made of. In this paper we study the information\naggregation performance of a stylized model of a society whose most influential\nindividuals - the leaders - are highly connected among themselves and\nuninformed. Agents update their state of knowledge in a Bayesian manner by\nlistening to their neighbors. We find analytical and numerical evidence of a\ntransition, as a function of the noise level in the information initially\navailable to agents, from a regime where information is correctly aggregated to\none where the population reaches consensus on the wrong outcome with finite\nprobability. Furthermore, information aggregation depends in a non-trivial\nmanner on the relative size of the clique of leaders, with the limit of a\nvanishingly small clique being singular.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Stock mechanics: predicting recession in S&P500, DJIA, and NASDAQ: An original method, assuming potential and kinetic energy for prices and\nconservation of their sum is developed for forecasting exchanges. Connections\nwith power law are shown. Semiempirical applications on S&P500, DJIA, and\nNASDAQ predict a coming recession in them. An emerging market, Istanbul Stock\nExchange index ISE-100 is found involving a potential to continue to rise.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Boosting cooperation by involving extortion in spatial Prisoner's\n  dilemma: We study the evolution of cooperation in spatial Prisoner's dilemma games\nwith and without extortion by adopting aspiration-driven strategy updating\nrule. We focus explicitly on how the strategy updating manner (whether\nsynchronous or asynchronous) and also the introduction of extortion strategy\naffect the collective outcome of the games. By means of Monte Carlo (MC)\nsimulations as well as dynamical cluster techniques, we find that the\ninvolvement of extortioners facilitates the boom of cooperators in the\npopulation (and whom can always dominate the population if the temptation to\ndefect is not too large) for both synchronous and asynchronous strategy\nupdating, in stark contrast to the otherwise case, where cooperation is\npromoted for intermediate aspiration level with synchronous strategy updating,\nbut is remarkably inhibited if the strategy updating is implemented\nasynchronously. We explain the results by configurational analysis and find\nthat the presence of extortion leads to the checkerboard-like ordering of\ncooperators and extortioners, which enable cooperators to prevail in the\npopulation with both strategy updating manners. Moreover, extortion itself is\nevolutionary stable, and therefore acts as the incubator for the evolution of\ncooperation.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Coevolution of teaching activity promotes cooperation: Evolutionary games are studied where the teaching activity of players can\nevolve in time. Initially all players following either the cooperative or\ndefecting strategy are distributed on a square lattice. The rate of strategy\nadoption is determined by the payoff difference and a teaching activity\ncharacterizing the donor's capability to enforce its strategy on the opponent.\nEach successful strategy adoption process is accompanied with an increase in\nthe donor's teaching activity. By applying an optimum value of the increment\nthis simple mechanism spontaneously creates relevant inhomogeneities in the\nteaching activities that support the maintenance of cooperation for both the\nprisoner's dilemma and the snowdrift game.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Fundamental Factors versus Herding in the 2000-2005 US Stock Market and\n  Prediction: We present a general methodology to incorporate fundamental economic factors\nto our previous theory of herding to describe bubbles and antibubbles. We start\nfrom the strong form of Rational Expectation and derive the general method to\nincorporate factors in addition to the log-periodic power law (LPPL) signature\nof herding developed in ours and others' works. These factors include interest\nrate, interest spread, historical volatility, implied volatility and exchange\nrates. Standard statistical AIC and Wilks tests allow us to compare the\nexplanatory power of the different proposed factor models. We find that the\nhistorical volatility played the key role before August of 2002. Around October\n2002, the interest rate dominated. In the first six months of 2003, the foreign\nexchange rate became the key factor. Since the end of 2003, all factors have\nplayed an increasingly large role. However, the most surprising result is that\nthe best model is the second-order LPPL without any factor. We thus present a\nscenario for the future evolution of the US stock market based on the\nextrapolation of the fit of the second-order LPPL formula, which suggests that\nherding is still the dominating force and that the unraveling of the US stock\nmarket antibubble since 2000 is still qualitatively similar to (but\nquantitatively different from) the Japanese Nikkei case after 1990.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Computer-Simulation des Wettbewerbs zwischen Sprachen: Recent computer simulations of the competition between thousands of languages\nare reviewed, and some new results on language families and language\nsimilarities are presented.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Flow-based analysis of storage usage in a low-carbon European\n  electricity scenario: The application of the flow tracing method to power flows in and out of\nstorage units allows to analyse the usage of this technology option in\nlarge-scale interconnected electricity systems. We apply this method to a\ndata-driven model of the European electricity network, which uses a\ntechno-economic optimisation to determine generation and storage capacities and\ndispatch, assuming a 95% reduction of CO2 emission compared to 1990 levels. A\nflow-based analysis of the power inflow into the different storage technologies\nconfirms the intuition that longer-term hydrogen storage is mainly utilised for\nwind, whereas short-term battery storage mostly receives inflow from solar\npower generation. The usage of storage technologies in general shows a\nlocal-but-global behaviour: Whereas on average the power outflow from these\ncapacities is predominantly consumed locally inside the same node, when\nexported it is also transmitted over long distances as a global flexibility\noption for the entire system.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Epidemics scenarios in the \"Romantic network\": The structure of sexual contacts, its contacts network and its temporal\ninteractions, play an important role in the spread of sexually transmitted\ninfections. Unfortunately, that kind of data is very hard to obtain. One of the\nfew exceptions is the \"Romantic network\" which is a complete structure of a\nreal sexual network of a high school. In terms of topology, unlike other sexual\nnetworks classified as scale-free network. Regarding the temporal structure,\nseveral studies indicate that relationship timing can have effects on diffusion\nthrough networks, as relationship order determines transmission routes.With the\naim to check if the particular structure, static and dynamic, of the Romantic\nnetwork is determinant for the propagation of an STI in it, we perform\nsimulations in two scenarios: the static network where all contacts are\navailable and the dynamic case where contacts evolve in time. In the static\ncase, we compare the epidemic results in the Romantic network with some\nparadigmatic topologies. We further study the behavior of the epidemic on the\nRomantic network in response to the effect of any individual, belonging to the\nnetwork, having a contact with an external infected subject, the influence of\nthe degree of the initial infected, and the effect of the variability of\ncontacts per unit time. We also consider the dynamics of formation of pairs in\nand we study the propagation of the diseases in this dynamic scenario. Our\nresults suggest that while the Romantic network can not be labeled as a\nWatts-Strogatz network, it is, regarding the propagation of an STI, very close\nto one with high disorder. Our simulations confirm that relationship timing\naffects, but strongly lowering, the final outbreak size. Besides, shows a clear\ncorrelation between the average degree and the outbreak size over time.",
        "category": "physics_soc-ph"
    },
    {
        "text": "An interactive city choice model and its application for measuring the\n  intercity interaction: Measuring the interaction between cities is an important research topic in\nmany disciplines, such as sociology, geography, economics and transportation\nscience. The traditional and most widely used spatial interaction model is the\ngravity model, but it requires the parameters to be artificially set. In this\npaper, we propose a parameter-free interactive city choice (ICC) model that\nmeasures intercity interaction from the perspective of individual choice\nbehavior. The ICC model assumes that the probability of an individual choosing\nto interact with a city is proportional to the number of opportunities in the\ndestination city and inversely proportional to the number of intervening\nopportunities between the origin city and the destination city, calculated\nusing the travel time in the transportation network. The intercity interaction\nintensity can be obtained by calculating the product of this probability and\nthe origin city's population. We apply the ICC model to measure the interaction\nintensity among 339 cities in China and analyze the impact of changes in the\nChinese land transportation network from 2005 to 2018 on the intercity and city\ninteraction intensity. Compared with the previously widely used spatial\ninteraction models, the measurement results of the ICC model are more\nconsistent with the actual situation.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Green power grids: how energy from renewable sources affects network and\n  markets: The increasing attention to environmental issues is forcing the\nimplementation of novel energy models based on renewable sources, fundamentally\nchanging the configuration of energy management and introducing new\ncriticalities that are only partly understood. In particular, renewable\nenergies introduce fluctuations causing an increased request of conventional\nenergy sources oriented to balance energy requests on short notices. In order\nto develop an effective usage of low-carbon sources, such fluctuations must be\nunderstood and tamed. In this paper we present a microscopic model for the\ndescription and the forecast of short time fluctuations related to renewable\nsources and to their effects on the electricity market. To account for the\ninter-dependencies among the energy market and the physical power dispatch\nnetwork, we use a statistical mechanics approach to sample stochastic\nperturbations on the power system and an agent based approach for the\nprediction of the market players behavior. Our model is a data-driven; it\nbuilds on one day ahead real market transactions to train agents behaviour and\nallows to infer the market share of different energy sources. We benchmark our\napproach on the Italian market finding a good accordance with real data.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Fault Induced Delayed Voltage Recovery in a Long Inhomogeneous Power\n  Distribution Feeder: We analyze the dynamics of a distribution circuit loaded with many induction\nmotor and subjected to sudden changes in voltage at the beginning of the\ncircuit. As opposed to earlier work \\cite{13DCB}, the motors are disordered,\ni.e. the mechanical torque applied to the motors varies in a random manner\nalong the circuit. In spite of the disorder, many of the qualitative features\nof a homogenous circuit persist, e.g. long-range motor-motor interactions\nmediated by circuit voltage and electrical power flows result in coexistence of\nthe spatially-extended and propagating normal and stalled phases. We also\nobserved a new phenomenon absent in the case without inhomogeneity/disorder.\nSpecifically, transition front between the normal and stalled phases becomes\nsomewhat random, even when the front is moving very slowly or is even\nstationary. Motors within the blurred domain appears in a normal or stalled\nstate depending on the local configuration of the disorder. We quantify effects\nof the disorder and discuss statistics of distribution dynamics, e.g. the front\nposition and width, total active/reactive consumption of the feeder and maximum\nclearing time.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Dynamic Behavior of Interacting between Epidemics and Cascades on\n  Heterogeneous Networks: Epidemic spreading and cascading failure are two important dynamical\nprocesses over complex networks. They have been investigated separately for a\nlong history. But in the real world, these two dynamics sometimes may interact\nwith each other. In this paper, we explore a model combined with SIR epidemic\nspreading model and local loads sharing cascading failure model. There exists a\ncritical value of tolerance parameter that whether the epidemic with high\ninfection probability can spread out and infect a fraction of the network in\nthis model. When the tolerance parameter is smaller than the critical value,\ncascading failure cuts off abundant of paths and blocks the spreading of\nepidemic locally. While the tolerance parameter is larger than the critical\nvalue, epidemic spreads out and infects a fraction of the network. A method for\nestimating the critical value is proposed. In simulation, we verify the\neffectiveness of this method in Barab\\'asi-Albert (BA) networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Quantum Readiness in Healthcare and Public Health: Building a Quantum\n  Literate Workforce: Quantum technologies, including quantum computing, cryptography, and sensing,\namong others, are set to revolutionize sectors ranging from materials science\nto drug discovery. Despite their significant potential, the implications for\npublic health have been largely overlooked, highlighting a critical gap in\nrecognition and preparation. This oversight necessitates immediate action, as\npublic health remains largely unaware of quantum technologies as a tool for\nadvancement. The application of quantum principles to epidemiology and health\ninformatics, termed quantum health epidemiology and quantum health informatics,\nhas the potential to radically transform disease surveillance, prediction,\nmodeling, and analysis of health data. However, there is a notable lack of\nquantum expertise within the public health workforce and educational pipelines.\nThis gap underscores the urgent need for the development of quantum literacy\namong public health practitioners, leaders, and students to leverage emerging\nopportunities while addressing risks and ethical considerations. Innovative\nteaching methods, such as interactive simulations, games, visual models, and\nother tailored platforms, offer viable solutions for bridging knowledge gaps\nwithout the need for advanced physics or mathematics. However, the opportunity\nto adapt is fleeting as the quantum era in healthcare looms near. It is\nimperative that public health urgently focuses on updating its educational\napproaches, workforce strategies, data governance, and organizational culture\nto proactively meet the challenges of quantum disruption thereby becoming\nquantum ready.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The diffusion dynamics of choice: From durable goods markets to fashion\n  first names: Goods, styles, ideologies are adopted by society through various mechanisms.\nIn particular, adoption driven by innovation is extensively studied by\nmarketing economics. Mathematical models are currently used to forecast the\nsales of innovative goods. Inspired by the theory of diffusion processes\ndeveloped for marketing economics, we propose, for the first time, a predictive\nframework for the mechanism of fashion, which we apply to first names. Analyses\nof French, Dutch and US national databases validate our modelling approach for\nthousands of first names, covering, on average, more than 50% of the yearly\nincidence in each database. In these cases, it is thus possible to forecast how\npopular the first names will become and when they will run out of fashion.\nFurthermore, we uncover a clear distinction between popularity and fashion:\nless popular names, typically not included in studies of fashion, may be driven\nby fashion, as well.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Evidence for a Conserved Quantity in Human Mobility: Recent seminal works on human mobility have shown that individuals constantly\nexploit a small set of repeatedly visited locations. A concurrent literature\nhas emphasized the explorative nature of human behavior, showing that the\nnumber of visited places grows steadily over time. How to reconcile these\nseemingly contradicting facts remains an open question. Here, we analyze\nhigh-resolution multi-year traces of $\\sim$40,000 individuals from 4 datasets\nand show that this tension vanishes when the long-term evolution of mobility\npatterns is considered. We reveal that mobility patterns evolve significantly\nyet smoothly, and that the number of familiar locations an individual visits at\nany point is a conserved quantity with a typical size of $\\sim$25 locations. We\nuse this finding to improve state-of-the-art modeling of human mobility.\nFurthermore, shifting the attention from aggregated quantities to individual\nbehavior, we show that the size of an individual's set of preferred locations\ncorrelates with the number of her social interactions. This result suggests a\nconnection between the conserved quantity we identify, which as we show can not\nbe understood purely on the basis of time constraints, and the `Dunbar number'\ndescribing a cognitive upper limit to an individual's number of social\nrelations. We anticipate that our work will spark further research linking the\nstudy of Human Mobility and the Cognitive and Behavioral Sciences.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The path most travelled: Mining road usage patterns from massive call\n  data: Rapid urbanization places increasing stress on already burdened\ntransportation systems, resulting in delays and poor levels of service.\nBillions of spatiotemporal call detail records (CDRs) collected from mobile\ndevices create new opportunities to quantify and solve these problems. However,\nthere is a need for tools to map new data onto existing transportation\ninfrastructure. In this work, we propose a system that leverages this data to\nidentify patterns in road usage. First, we develop an algorithm to mine\nbillions of calls and learn location transition probabilities of callers. These\ntransition probabilities are then upscaled with demographic data to estimate\norigin-destination (OD) flows of residents between any two intersections of a\ncity. Next, we implement a distributed incremental traffic assignment algorithm\nto route these flows on road networks and estimate congestion and level of\nservice for each roadway. From this assignment, we construct a bipartite usage\nnetwork by connecting census tracts to the roads used by their inhabitants.\nComparing the topologies of the physical road network and bipartite usage\nnetwork allows us to classify each road's role in a city's transportation\nnetwork and detect causes of local bottlenecks. Finally, we demonstrate an\ninteractive, web-based visualization platform that allows researchers,\npolicymakers, and drivers to explore road congestion and usage in a new\ndimension. To demonstrate the flexibility of this system, we perform these\nanalyses in multiple cities across the globe with diverse geographical and\nsociodemographic qualities. This platform provides a foundation to build\ncongestion mitigation solutions and generate new insights into urban mobility.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Scaling of degree correlations and the influence on diffusion in\n  scale-free networks: Connectivity correlations play an important role in the structure of\nscale-free networks. While several empirical studies exist, there is no general\ntheoretical analysis that can explain the largely varying behavior of real\nnetworks. Here, we use scaling theory to quantify the degree of correlations in\nthe particular case of networks with a power-law degree distribution. These\nnetworks are classified in terms of their correlation properties, revealing\nadditional information on their structure. For instance, the studied social\nnetworks and the Internet at the router level are clustered around the line of\nrandom networks, implying a strongly connected core of hubs. On the contrary,\nsome biological networks and the WWW exhibit strong anti-correlations. The\npresent approach can be used to study robustness or diffusion, where we find\nthat anti-correlations tend to accelerate the diffusion process.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Quantifying Networked Resilience via Multi-Scale Feedback Loops in Water\n  Distribution Networks: Water distribution networks (WDNs) are one of the most important man-made\ninfrastructures. Resilience, the ability to respond to disturbances and recover\nto a desirable state, is of vital importance to our society. There is\nincreasing evidence that the resilience of networked infrastructures with\ndynamic signals depends on their network topological properties. Whilst\nexisting literature has examined a variety of complex network centrality and\nspectral properties, very little attention has been given to understand\nmulti-scale flow-based feedback loops and its impact on overall stability.\nHere, resilience arising from multi-scale feedback loops is inferred using a\nproxy measure called trophic coherence. This is hypothesised to have a strong\nimpact on both the pressure deficit and the water age. Our results show that\ntrophic coherence is positively correlated with the time to recovery (0.62 to\n0.52), while it is negatively correlated with the diffusion of a disruption\n(-0.66 to -0.52). Finally, we apply random forest analysis to combine\nresilience measures, showing that the new resilience ensemble provides a more\naccurate measure for networked resilience.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Time allocation in social networks: correlation between social structure\n  and human communication dynamics: Recent research has shown the deep impact of the dynamics of human\ninteractions (or temporal social networks) on the spreading of information,\nopinion formation, etc. In general, the bursty nature of human interactions\nlowers the interaction between people to the extent that both the speed and\nreach of information diffusion are diminished. Using a large database of 20\nmillion users of mobile phone calls we show evidence this effect is not\nhomogeneous in the social network but in fact, there is a large correlation\nbetween this effect and the social topological structure around a given\nindividual. In particular, we show that social relations of hubs in a network\nare relatively weaker from the dynamical point than those that are poorer\nconnected in the information diffusion process. Our results show the importance\nof the temporal patterns of communication when analyzing and modeling dynamical\nprocess on social networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Control of cascading failures using protective measures: Cascading failures, triggered by a local perturbation, can be catastrophic\nand cause irreparable damages in a wide area. Hence, blocking the devastating\ncascades is an important issue in real world networks. One of the ways to\ncontrol the cascade is to use protective measures, so that the agents decide to\nbe protected against failure. Here, we consider a coevolution of the linear\nthreshold model for the spread of cascading failures and a decision-making game\nbased on the perceived risk of failure. Protected agents are less vulnerable to\nfailure and in return the size of the cascade affects the agent's decision to\nget insured. We find at what range of protection efficiency and cost of\nfailure, the global cascades stop. Also we observe that in some range of\nprotection efficiency, a bistable region emerges for the size of cascade and\nthe prevalence of protected agents. Moreover, we show how savings or the\nability of agents to repair can prevent cascades from occurring",
        "category": "physics_soc-ph"
    },
    {
        "text": "Move ordering and communities in complex networks describing the game of\n  go: We analyze the game of go from the point of view of complex networks. We\nconstruct three different directed networks of increasing complexity, defining\nnodes as local patterns on plaquettes of increasing sizes, and links as actual\nsuccessions of these patterns in databases of real games. We discuss the\npeculiarities of these networks compared to other types of networks. We explore\nthe ranking vectors and community structure of the networks and show that this\napproach enables to extract groups of moves with common strategic properties.\nWe also investigate different networks built from games with players of\ndifferent levels or from different phases of the game. We discuss how the study\nof the community structure of these networks may help to improve the computer\nsimulations of the game. More generally, we believe such studies may help to\nimprove the understanding of human decision process.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Modular networks emerge from multiconstraint optimization: Modular structure is ubiquitous among complex networks. We note that most\nsuch systems are subject to multiple structural and functional constraints,\ne.g., minimizing the average path length and the total number of links, while\nmaximizing robustness against perturbations in node activity. We show that the\noptimal networks satisfying these three constraints are characterized by the\nexistence of multiple subnetworks (modules) sparsely connected to each other.\nIn addition, these modules have distinct hubs, resulting in an overall\nheterogeneous degree distribution.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Unnecessary risks created by uncontrolled rocket reentries: In 2020, over 60% of launches to low Earth orbit resulted in one or more\nrocket bodies being abandoned in orbit and eventually returning to Earth in an\nuncontrolled manner. When they do so, between 20 and 40% of their mass survives\nthe heat of atmospheric reentry. Many of the surviving pieces are heavy enough\nto pose serious risks to people, on land, at sea, and in airplanes.\n  There is no international consensus on the acceptable level of risk from\nreentering space objects. This is sometimes a point of contention, such as when\na 20 tonne Long March 5B core stage made an uncontrolled reentry in May 2021.\nSome regulators, including the US, France, and ESA, have implemented a 1 in\n10,000 acceptable casualty risk (i.e., statistical threat to human life)\nthreshold from reentering space objects. We argue that this threshold ignores\nthe cumulative effect of the rapidly increasing number of rocket launches. It\nalso fails to address low risk, high consequence outcomes, such as a rocket\nstage crashing into a high-density city or a large passenger aircraft. In the\nlatter case, even a small piece could cause hundreds of casualties. Compounding\nthis, the threshold is frequently ignored or waived when the costs of adherence\nare deemed excessive.\n  We analyse the rocket bodies that reentered the atmosphere from 1992 - 2021\nand model the associated cumulative casualty expectation. We then extrapolate\nthis trend into the near future (2022 - 2032), modelling the potential risk to\nthe global population from uncontrolled rocket body reentries. We also analyse\nthe population of rocket bodies that are currently in orbit and expected to\ndeorbit soon, and find that the risk distribution is significantly weighted to\nlatitudes close to the equator. This represents a disproportionate burden of\ncasualty risk imposed on the countries of the Global South by major spacefaring\ncountries.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Poker as a Skill Game: Rational vs Irrational Behaviors: In many countries poker is one of the most popular card games. Although each\nvariant of poker has its own rules, all involve the use of money to make the\nchallenge meaningful. Nowadays, in the collective consciousness, some variants\nof poker are referred to as games of skill, others as gambling. A poker table\ncan be viewed as a psychology lab, where human behavior can be observed and\nquantified. This work provides a preliminary analysis of the role of\nrationality in poker games, using a stylized version of Texas Hold'em. In\nparticular, we compare the performance of two different kinds of players, i.e.,\nrational vs irrational players, during a poker tournament. Results show that\nthese behaviors (i.e., rationality and irrationality) affect both the outcomes\nof challenges and the way poker should be classified.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Optimal heterogeneity in a simplified highly renewable European\n  electricity system: The resource quality and the temporal generation pattern of variable\nrenewable energy sources vary significantly across Europe. In this paper\nspatial distributions of renewable assets are explored which exploit this\nheterogeneity to lower the total system costs for a high level of renewable\nelectricity in Europe. Several intuitive heuristic algorithms, optimal\nportfolio theory and a local search algorithm are used to find optimal\ndistributions of renewable generation capacities that minimise the total costs\nof backup, transmission and renewable capacity simultaneously. Using current\ncost projections, an optimal heterogeneous distribution favours onshore wind,\nparticularly in countries bordering the North Sea, which results in average\nelectricity costs that are up to 11% lower than for a homogeneous reference\ndistribution of renewables proportional to each country's mean load. The\nreduction becomes even larger, namely 18%, once the transmission capacities are\nput to zero in the homogeneous reference distribution. Heuristic algorithms to\ndistribute renewable capacity based on each country's wind and solar capacity\nfactors are shown to provide a satisfactory approximation to fully optimised\nrenewable distributions, while maintaining the benefits of transparency and\ncomprehensibility. The sensitivities of the results to changing costs of solar\ngeneration and gas supply as well as to the possible cross-sectoral usage of\nunavoidable curtailment energy are also examined.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Insights from exact social contagion dynamics on networks with\n  higher-order structures: Recently there has been an increasing interest in studying dynamical\nprocesses on networks exhibiting higher-order structures, such as simplicial\ncomplexes, where the dynamics acts above and beyond dyadic interactions. Using\nsimulations or heuristically derived epidemic spreading models it was shown\nthat new phenomena can emerge, such as bi-stability/multistability. Here, we\nshow that such new emerging phenomena do not require complex contact patterns,\nsuch as community structures, but naturally result from the higher-order\ncontagion mechanisms. We show this by deriving an exact higher-order SIS model\nand its limiting mean-field equivalent for fully connected simplicial\ncomplexes. Going beyond previous results, we also give the global bifurcation\npicture for networks with 3- and 4-body interactions, with the latter allowing\nfor two non-trivial stable endemic steady states. Differently from previous\napproaches, we are able to study systems featuring interactions of arbitrary\norder. In addition, we characterise the contributions from higher-order\ninfections to the endemic equilibrium as perturbations of the pairwise\nbaseline, finding that these diminish as the pairwise rate of infection\nincreases. Our approach represents a first step towards a principled\nunderstanding of higher-order contagion processes beyond triads and opens up\nfurther directions for analytical investigations.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Collective Influence Algorithm to find influencers via optimal\n  percolation in massively large social media: We elaborate on a linear time implementation of the Collective Influence (CI)\nalgorithm introduced by Morone, Makse, Nature 524, 65 (2015) to find the\nminimal set of influencers in a network via optimal percolation. We show that\nthe computational complexity of CI is O(N log N) when removing nodes\none-by-one, with N the number of nodes. This is made possible by using an\nappropriate data structure to process the CI values, and by the finite radius l\nof the CI sphere. Furthermore, we introduce a simple extension of CI when l is\ninfinite, the CI propagation (CI_P) algorithm, that considers the global\noptimization of influence via message passing in the whole network and\nidentifies a slightly smaller fraction of influencers than CI. Remarkably, CI_P\nis able to reproduce the exact analytical optimal percolation threshold\nobtained by Bau, Wormald, Random Struct. Alg. 21, 397 (2002) for cubic random\nregular graphs, leaving little improvement left for random graphs. We also\nintroduce the Collective Immunization Belief Propagation algorithm (CI_BP), a\nbelief-propagation (BP) variant of CI based on optimal immunization, which has\nthe same performance as CI_P. However, this small augmented performance of the\norder of 1-2 % in the low influencers tail comes at the expense of increasing\nthe computational complexity from O(N log N) to O(N^2 log N), rendering both,\nCI_P and CI_BP, prohibitive for finding influencers in modern-day big-data. The\nsame nonlinear running time drawback pertains to a recently introduced\nBP-decimation (BPD) algorithm by Mugisha, Zhou, arXiv:1603.05781. For instance,\nwe show that for big-data social networks of typically 200 million users (eg,\nactive Twitter users sending 500 million tweets per day), CI finds the\ninfluencers in less than 3 hours running on a single CPU, while the BP\nalgorithms (CI_P, CI_BP and BDP) would take more than 3,000 years to accomplish\nthe same task.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Social Echo Chambers in Quantum Field Theory: Exploring Faddeev-Popov\n  Ghosts Phenomena, Loop Diagrams, and Cut-off Energy Theory: This paper presents an interdisciplinary approach to analyze the emergence\nand impact of filter bubbles in social phenomena, especially in both digital\nand offline environments, by applying the concepts of quantum field theory.\nFilter bubbles tend to occur in digital and offline environments, targeting\ndigital natives with extremely low media literacy and information immunity. In\naddition, in the aftermath of stealth marketing, fake news, \"inspirational\nmarketing,\" and other forms of stealth marketing that never exist are rampant\nand can lead to major social disruption and exploitation. These are the causes\nof various social risks, including declining information literacy and knowledge\nlevels and academic achievement. By exploring quantum mechanical principles\nsuch as remote interaction, proximity interaction, Feynman diagrams, and loop\ndiagrams, we aim to gain a better understanding of information dissemination\nand opinion formation in social contexts. Our model incorporates key parameters\nsuch as agents' opinions, interaction probabilities, and flexibility in\nchanging opinions, facilitating the observation of opinion distributions,\ncluster formation, and polarization under a variety of conditions. The purpose\nof this paper is to mathematically model the filter bubble phenomenon using the\nconcepts of quantum field theory and to analyze its social consequences. This\nis a discussion paper and the proposed approach offers an innovative\nperspective for understanding social phenomena, but its interpretation and\napplication require careful consideration.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Pessimism and Cynicism as Physical Entities: Choosing pessimism makes one cynical to and necessitates their destruction of\ninformation.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Developing a Resilient, Robust and Efficient Supply Network in Africa: Supply chains need to balance competing objectives; in addition to efficiency\nthey need to be resilient to adversarial and environmental interference, and\nrobust to uncertainties in long term demand. Significant research has been\nconducted designing efficient supply chains, and recent research has focused on\nresilient supply chain design. However, the integration of resilient and robust\nsupply chain design is less well studied. This paper develops a method to\ninclude resilience and robustness into supply chain design. Using the region of\nWest Africa, which is plagued with persisting logistical issues, we develop a\nregional risk assessment framework, then apply categorical risk to the\ncountries of West Africa using publicly available data. Next, we develop a\nmathematical model leveraging this framework to design a resilient supply\nnetwork that minimizes cost while ensuring the network functions following a\ndisruption. Finally, we examine the network's robustness to demand uncertainty\nvia several plausible emergency scenarios.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Transportation Planning and Traffic Flow Models: In this paper, we focus on the different traffic flow models that exist in\nliterature. Due to our frequently encountered confusion among traffic engineers\nand policy makers, this paper goes into more detail about transportation\nplanning models on the one hand, and traffic flow models on the other hand. The\nformer deal with households that make certain decisions which lead to\ntransportation and the use of infrastructure, as opposed to the latter which\nexplicitly describe the physical propagation of traffic flows in a road\nnetwork. Our goal is not to give a full account (as that would be a\ndissertation of its own, given the broadness of the field), but rather to\nimpose upon the reader a thorough feeling for the differences between\ntransportation planning and traffic flow models. Because of the high course of\nprogress over the last decade (or even during the last five years), this paper\ntries to chronicle both past models, as well as some of the latest developments\nin this area.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Decelerated invasion and waning moon patterns in public goods games with\n  delayed distribution: We study the evolution of cooperation in the spatial public goods game,\nfocusing on the effects that are brought about by the delayed distribution of\ngoods that accumulate in groups due to the continuous investments of\ncooperators. We find that intermediate delays enhance network reciprocity\nbecause of a decelerated invasion of defectors, who are unable to reap the same\nhigh short-term benefits as they do in the absence of delayed distribution.\nLong delays, however, introduce a risk because the large accumulated wealth\nmight fall into the wrong hands. Indeed, as soon as the curvature of a\ncooperative cluster turns negative, the engulfed defectors can collect the\nheritage of many generations of cooperators, and by doing so start a waning\nmoon pattern that nullifies the benefits of decelerated invasion. Accidental\nmeeting points of growing cooperative clusters may also act as triggers for the\nwaning moon effect, thus linking the success of cooperators with their\npropensity to fail in a rather bizarre way. Our results highlight that\n\"investing into the future\" is a good idea only if that future is sufficiently\nnear and not likely to be burdened by inflation.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Noisy kinetic-exchange opinion model with aging: We study the critical behavior of a noisy kinetic opinion model subject to\nresilience to change depending on aging, defined as the time spent on the\ncurrent opinion state. In this model, the opinion of each agent can take the\nthree discrete values, the extreme ones $\\pm 1$, and also the intermediate\nvalue 0, and it can evolve through kinetic exchange when interacting with\nanother agent, or independently, by stochastic choice (noise). The probability\nof change by pairwise interactions depends on the age that the agent has\nremained in the same state, according to a given kernel. We particularly\ndevelop the cases where the probability decays either algebraically or\nexponentially with the age, and we also consider the anti-aging scenario where\nthe probability increases with the age, meaning that it is more likely to\nchange mind the longer the persistence in the current state. For the opinion\ndynamics in a complete graph, we obtain analytical predictions for the critical\ncurves of the order parameters, in perfect agreement with agent-based\nsimulations. We observe that sufficiently weak aging (slow change of the kernel\nwith the age) favors partial consensus with respect to the aging-insensitive\nscenario, while for sufficiently strong aging, as well as for anti-aging, the\nopposite trend is observed.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Braess paradox in a network with stochastic dynamics and fixed\n  strategies: The Braess paradox can be observed in road networks used by selfish users. It\ndescribes the counterintuitive situation in which adding a new, per se faster,\norigin-destination connection to a road network results in increased travel\ntimes for all network users. We study the network as originally proposed by\nBraess but introduce microscopic particle dynamics based on the totally\nasymmetric exclusion processes. In contrast to our previous work\n[10.1103/PhysRevE.94.062312], where routes were chosen randomly according to\nturning rates, here we study the case of drivers with fixed route choices. We\nfind that travel time reduction due to the new road only happens at really low\ndensities and Braess' paradox dominates the largest part of the phase diagram.\nFurthermore, the domain wall phase observed in [10.1103/PhysRevE.94.062312]\nvanishes. In the present model gridlock states are observed in a large part of\nphase space. We conclude that the construcion of a new road can often be very\ncritical and should be considered carefully.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Recovery of Interdependent Networks: Recent network research has focused on the cascading failures in a system of\ninterdependent networks and the necessary preconditions for system collapse. An\nimportant question that has not been addressed is how to repair a failing\nsystem before it suffers total breakdown. Here we introduce a recovery strategy\nof nodes and develop an analytic and numerical framework for studying the\nconcurrent failure and recovery of a system of interdependent networks based on\nan efficient and practically reasonable strategy. Our strategy consists of\nrepairing a fraction of failed nodes, with probability of recovery $\\gamma$,\nthat are neighbors of the largest connected component of each constituent\nnetwork. We find that, for a given initial failure of a fraction $1-p$ of\nnodes, there is a critical probability of recovery above which the cascade is\nhalted and the system fully restores to its initial state and below which the\nsystem abruptly collapses. As a consequence we find in the plane $\\gamma-p$ of\nthe phase diagram three distinct phases. A phase in which the system never\ncollapses without being restored, another phase in which the recovery strategy\navoids the breakdown, and a phase in which even the repairing process cannot\navoid the system collapse.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Agent-based Versus Macroscopic Modeling of Competition and Business\n  Processes in Economics: Simulation serves as a third way of doing science, in contrast to both\ninduction and deduction. The web based modeling may considerably facilitate the\nexecution of simulations by other people. We present examples of agent-based\nand stochastic models of competition and business processes in economics. We\nstart from as simple as possible models, which have microscopic, agent-based,\nversions and macroscopic treatment in behavior. Microscopic and macroscopic\nversions of herding model proposed by Kirman and Bass diffusion of new products\nare considered in this contribution as two basic ideas.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Competition of Languages and their Hamming Distance: We consider the spreading and competition of languages that are spoken by a\npopulation of individuals. The individuals can change their mother tongue\nduring their lifespan, pass on their language to their offspring and finally\ndie. The languages are described by bitstrings, their mutual difference is\nexpressed in terms of their Hamming distance. Language evolution is determined\nby mutation and adaptation rates. In particular we consider the case where the\nreplacement of a language by another one is determined by their mutual Hamming\ndistance. As a function of the mutation rate we find a sharp transition between\na scenario with one dominant language and fragmentation into many language\nclusters. The transition is also reflected in the Hamming distance between the\ntwo languages with the largest and second to largest number of speakers. We\nalso consider the case where the population is localized on a square lattice\nand the interaction of individuals is restricted to a certain geometrical\ndomain. Here it is again the Hamming distance that plays an essential role in\nthe final fate of a language of either surviving or being extinct.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Regionalised heat demand and power-to-heat capacities in Germany -- An\n  open data set for assessing renewable energy integration: Higher shares of fluctuating generation from renewable energy sources in the\npower system lead to an increase in grid balancing demand. One approach for\navoiding curtailment of renewable energies is to use excess electricity feed-in\nfor heating applications. To assess in which regions power-to-heat technologies\ncan contribute to renewable energy integration, detailed data on the spatial\ndistribution of the heat demand are needed. We determine the overall heat load\nin the residential building sector and the share covered by electric heating\ntechnologies for each administrative district in Germany, with a temporal\nresolution of 15 minutes. Using a special evaluation of German census data, we\ndefined 729 building categories and assigned individual heat demand values.\nFurthermore, heating types and different classes of installed heating capacity\nwere defined. Our analysis showed that the share of small-scale single-storey\nheating and large-scale central heating is higher in cities, whereas there is\nmore medium-scale central heating in rural areas. This results from the\ndifferent shares of single and multi-family houses in the respective regions.\nTo determine the electrically-covered heat demand, we took into account heat\npumps and resistive heating technologies. All results, as well as the developed\ncode, are published under open source licenses and can thus also be used by\nother researchers for the assessment of power-to-heat for renewable energy\nintegration.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Three-state opinion dynamics in modular networks: In this work we study the opinion evolution in a community-based population\nwith intergroup interactions. We address two issues. First, we consider that\nsuch intergroup interactions can be negative with some probability $p$. We\ndevelop a coupled mean-field approximation that still preserves the community\nstructure and it is able to capture the richness of the results arising from\nour Monte Carlo simulations: continuous and discontinuous order-disorder\ntransitions as well as nonmonotonic ordering for an intermediate community\nstrength. In the second part, we consider only positive interactions, but with\nthe presence of inflexible agents holding a minority opinion. We also consider\nan indecision noise: a probability $q$ that allows the spontaneous change of\nopinions to the neutral state. Our results show that the modular structure\nleads to a nonmonotonic global ordering as $q$ increases. This inclination\ntoward neutrality plays a dual role: a moderated propensity to neutrality helps\nthe initial minority to become a majority, but this noise-driven opinion\nswitching becomes less pronounced if the agents are too susceptible to become\nneutral.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Language Time Series Analysis: We use the Detrended Fluctuation Analysis (DFA) and the Grassberger-Proccacia\nanalysis (GP) methods in order to study language characteristics. Despite that\nwe construct our signals using only word lengths or word frequencies, excluding\nin this way huge amount of information from language, the application of\nGrassberger- Proccacia (GP) analysis indicates that linguistic signals may be\nconsidered as the manifestation of a complex system of high dimensionality,\ndifferent from random signals or systems of low dimensionality such as the\nearth climate. The DFA method is additionally able to distinguish a natural\nlanguage signal from a computer code signal. This last result may be useful in\nthe field of cryptography.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Daily rhythms in mobile telephone communication: Circadian rhythms are known to be important drivers of human activity and the\nrecent availability of electronic records of human behaviour has provided\nfine-grained data of temporal patterns of activity on a large scale. Further,\nquestionnaire studies have identified important individual differences in\ncircadian rhythms, with people broadly categorised into morning-like or\nevening-like individuals. However, little is known about the social aspects of\nthese circadian rhythms, or how they vary across individuals. In this study we\nuse a unique 18-month dataset that combines mobile phone calls and\nquestionnaire data to examine individual differences in the daily rhythms of\nmobile phone activity. We demonstrate clear individual differences in daily\npatterns of phone calls, and show that these individual differences are\npersistent despite a high degree of turnover in the individuals' social\nnetworks. Further, women's calls were longer than men's calls, especially\nduring the evening and at night, and these calls were typically focused on a\nsmall number of emotionally intense relationships. These results demonstrate\nthat individual differences in circadian rhythms are not just related to broad\npatterns of morningness and eveningness, but have a strong social component, in\ndirecting phone calls to specific individuals at specific times of day.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Evolution of a large online social network: Although recently there are extensive research on the collaborative networks\nand online communities, there is very limited knowledge about the actual\nevolution of the online social networks (OSN). In the Letter, we study the\nstructural evolution of a large online virtual community. We find that the\nscale growth of the OSN shows non-trivial S shape which may provide a proper\nexemplification for Bass diffusion model. We reveal that the evolutions of many\nnetwork properties, such as density, clustering, heterogeneity and modularity,\nshow non-monotone feature, and shrink phenomenon occurs for the path length and\ndiameter of the network. Furthermore, the OSN underwent a transition from\ndegree assortativity characteristic of collaborative networks to degree\ndisassortativity characteristic of many OSNs. Our study has revealed the\nevolutionary pattern of interpersonal interactions in a specific population and\nprovided a valuable platform for theoretical modeling and further analysis.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Predicting success in the worldwide start-up network: By drawing on large-scale online data we construct and analyze the\ntime-varying worldwide network of professional relationships among start-ups.\nThe nodes of this network represent companies, while the links model the flow\nof employees and the associated transfer of know-how across companies. We use\nnetwork centrality measures to assess, at an early stage, the likelihood of the\nlong-term positive performance of a start-up, showing that the start-up network\nhas predictive power and provides valuable recommendations doubling the current\nstate of the art performance of venture funds. Our network-based approach not\nonly offers an effective alternative to the labour-intensive screening\nprocesses of venture capital firms, but can also enable entrepreneurs and\npolicy-makers to conduct a more objective assessment of the long-term\npotentials of innovation ecosystems and to target interventions accordingly.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Local Empathy provides Global Minimization of Congestion in\n  Communication Networks: We present a novel mechanism to avoid congestion in complex networks based on\nlocal knowledge of traffic conditions and the ability of routers to\nself-coordinate their dynamical behavior. In particular, routers make use of\nlocal information about traffic conditions to either reject or accept\ninformation packets from their neighbors. We show that when nodes are only\naware of their own congestion state they self-organize into a hierarchical\nconfiguration that delays remarkably the onset of congestion although, leading\nto a sharp first-order like congestion transition. We also consider the case\nwhen nodes are aware of the congestion state of their neighbors. In this case,\nwe show that empathy between nodes is strongly beneficial to the overall\nperformance of the system and it is possible to achieve larger values for the\ncritical load together with a smooth, second-order like, transition. Finally,\nwe show how local empathy minimize the impact of congestion as much as global\nminimization. Therefore, here we present an outstanding example of how local\ndynamical rules can optimize the system's functioning up to the levels reached\nusing global knowledge.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Exponential random graph models for the Japanese bipartite network of\n  banks and firms: We use the exponential random graph models to understand the network\nstructure and its generative process for the Japanese bipartite network of\nbanks and firms. One of the well known and simple model of exponential random\ngraph is the Bernoulli model which shows the links in the bank-firm network are\nnot independent from each other. Another popular exponential random graph\nmodel, the two star model, indicates that the bank-firms are in a state where\nmacroscopic variables of the system can show large fluctuations. Moreover, the\npresence of high fluctuations reflect a fragile nature of the bank-firm\nnetwork.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Coevolution spreading in complex networks: The propagations of diseases, behaviors and information in real systems are\nrarely independent of each other, but they are coevolving with strong\ninteractions. To uncover the dynamical mechanisms, the evolving spatiotemporal\npatterns and critical phenomena of networked coevolution spreading are\nextremely important, which provide theoretical foundations for us to control\nepidemic spreading, predict collective behaviors in social systems, and so on.\nThe coevolution spreading dynamics in complex networks has thus attracted much\nattention in many disciplines. In this review, we introduce recent progress in\nthe study of coevolution spreading dynamics, emphasizing the contributions from\nthe perspectives of statistical mechanics and network science. The theoretical\nmethods, critical phenomena, phase transitions, interacting mechanisms, and\neffects of network topology for four representative types of coevolution\nspreading mechanisms, including the coevolution of biological contagions,\nsocial contagions, epidemic-awareness, and epidemic-resources, are presented in\ndetail, and the challenges in this field as well as open issues for future\nstudies are also discussed.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Mathematical models to explain the origin of urban scaling laws: a\n  synthetic review: The quest for a theory of cities that could offer a quantitative and\nsystematic approach to manage cities is at the top priority, given the\nchallenges humanity faces due to the increasing urbanization and densification\nof cities. If such a theory is feasible, then its formulation must be in a\nmathematical way. As a contribution to organizing the mathematical ideas that\ndeal with such a systematic way of understanding urban phenomena, we present\nthis material, concentrating on one important aspect of what recently has been\ncalled the new science of cities. In this paper, we review the main\nmathematical models present in the literature that aim at explaining the origin\nand emergence of urban scaling. We intend to present the models, identify\nsimilarities and connections between them, and find situations in which\ndifferent models lead to the same output. In addition, we report situations in\nwhich some ideas initially introduced in a particular model can also be\nintroduced in another model, generating more diversification and increasing the\nscope of the models. The models treated in this paper explain urban scaling\nfrom different premises: from gravity ideas, passing through densification\nideas and cites' geometry, to a hierarchical organization and socio-network\nproperties. We also investigate scenarios in which these different fundamental\nideas could be interpreted as similar -- where the similarity is likely but not\nobvious. Furthermore, in what concerns the gravity idea, we propose a general\nframework that includes all gravity models analyzed as a particular case.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Ranking Edges by their Impact on the Spectral Complexity of Information\n  Diffusion over Networks: Despite the numerous ways now available to quantify which parts or subsystems\nof a network are most important, there remains a lack of centrality measures\nthat are related to the complexity of information flows and are derived\ndirectly from entropy measures. Here, we introduce a ranking of edges based on\nhow each edge's removal would change a system's von Neumann entropy (VNE),\nwhich is a spectral-entropy measure that has been adapted from quantum\ninformation theory to quantify the complexity of information dynamics over\nnetworks. We show that a direct calculation of such rankings is computationally\ninefficient (or unfeasible) for large networks: e.g.\\ the scaling is\n$\\mathcal{O}(N^3)$ per edge for networks with $N$ nodes. To overcome this\nlimitation, we employ spectral perturbation theory to estimate VNE\nperturbations and derive an approximate edge-ranking algorithm that is accurate\nand fast to compute, scaling as $\\mathcal{O}(N)$ per edge. Focusing on a form\nof VNE that is associated with a transport operator $e^{-\\beta{ L}}$, where ${\nL}$ is a graph Laplacian matrix and $\\beta>0$ is a diffusion timescale\nparameter, we apply this approach to diverse applications including a network\nencoding polarized voting patterns of the 117th U.S. Senate, a multimodal\ntransportation system including roads and metro lines in London, and a\nmultiplex brain network encoding correlated human brain activity. Our\nexperiments highlight situations where the edges that are considered to be most\nimportant for information diffusion complexity can dramatically change as one\nconsiders short, intermediate and long timescales $\\beta$ for diffusion.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Belief Propagation and Bethe approximation for Traffic Prediction: We define and study an inference algorithm based on \"belief propagation\" (BP)\nand the Bethe approximation. The idea is to encode into a graph an a priori\ninformation composed of correlations or marginal probabilities of variables,\nand to use a message passing procedure to estimate the actual state from some\nextra real-time information. This method is originally designed for traffic\nprediction and is particularly suitable in settings where the only information\navailable is floating car data. We propose a discretized traffic description,\nbased on the Ising model of statistical physics, in order to both reconstruct\nand predict the traffic in real time. General properties of BP are addressed in\nthis context. In particular, a detailed study of stability is proposed with\nrespect to the a priori data and the graph topology. The behavior of the\nalgorithm is illustrated by numerical studies on a simple traffic toy model.\nHow this approach can be generalized to encode superposition of many traffic\npatterns is discussed.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Correlations between political party size and voter memory: A\n  statistical analysis of opinion polls: This paper describes the application of statistical methods to political\npolling data in order to look for correlations and memory effects. We propose\nmeasures for quantifying the political memory using the correlation function\nand scaling analysis. These methods reveal time correlations and self-affine\nscaling properties respectively, and they have been applied to polling data\nfrom Norway. Power-law dependencies have been found between correlation\nmeasures and party size, and different scaling behaviour has been found for\nlarge and small parties.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Social structure formation in a network of agents playing a hybrid of\n  ultimatum and dictator games: Here we present an agent-based model where agents interact with other agents\nby playing a hybrid of dictator and ultimatum games in a co-evolving social\nnetwork. The basic assumption about the behaviour of the agents in both games\nis that they try to attain superior socioeconomic positions relative to other\nagents. As the model parameters we have chosen the relative proportions of the\ndictator and ultimatum game strategies being played between a pair of agents in\na single social transaction and a parameter depicting the living costs of the\nagents. The motivation of the study is to examine how different types of social\ninteractions affect the formation of social structures and networks, when the\nagents have a tendency to maximize their socioeconomic standing. We find that\nsuch social networks of agents invariably undergo a community formation process\nfrom simple chain-like structure to more complex networks as the living cost\nparameter is increased. The point where this occurs, depends also on the\nrelative proportion of the dictator and ultimatum games being played. We find\nthat it is harder for complex social structures to form when the dictator game\nstrategy in social transactions of agents becomes more dominant over that of\nthe ultimatum game.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Participation costs dismiss the advantage of heterogeneous networks in\n  evolution of cooperation: Real social interactions occur on networks in which each individual is\nconnected to some, but not all, of others. In social dilemma games with a fixed\npopulation size, heterogeneity in the number of contacts per player is known to\npromote evolution of cooperation. Under a common assumption of positively\nbiased payoff structure, well-connected players earn much by playing\nfrequently, and cooperation once adopted by well-connected players is\nunbeatable and spreads to others. However, maintaining a social contact can be\ncostly, which would prevent local payoffs from being positively biased. In\nreplicator-type evolutionary dynamics, it is shown that even a relatively small\nparticipation cost extinguishes the merit of heterogeneous networks in terms of\ncooperation. In this situation, more connected players earn less so that they\nare no longer spreaders of cooperation. Instead, those with fewer contacts win\nand guide the evolution. The participation cost, or the baseline payoff, is\nirrelevant in homogeneous populations but is essential for evolutionary games\non heterogeneous networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Self-Control of Traffic Lights and Vehicle Flows in Urban Road Networks: Based on fluid-dynamic and many-particle (car-following) simulations of\ntraffic flows in (urban) networks, we study the problem of coordinating\nincompatible traffic flows at intersections. Inspired by the observation of\nself-organized oscillations of pedestrian flows at bottlenecks [D. Helbing and\nP. Moln\\'ar, Phys. Eev. E 51 (1995) 4282--4286], we propose a self-organization\napproach to traffic light control. The problem can be treated as multi-agent\nproblem with interactions between vehicles and traffic lights. Specifically,\nour approach assumes a priority-based control of traffic lights by the vehicle\nflows themselves, taking into account short-sighted anticipation of vehicle\nflows and platoons. The considered local interactions lead to emergent\ncoordination patterns such as ``green waves'' and achieve an efficient,\ndecentralized traffic light control. While the proposed self-control adapts\nflexibly to local flow conditions and often leads to non-cyclical switching\npatterns with changing service sequences of different traffic flows, an almost\nperiodic service may evolve under certain conditions and suggests the existence\nof a spontaneous synchronization of traffic lights despite the varying delays\ndue to variable vehicle queues and travel times. The self-organized traffic\nlight control is based on an optimization and a stabilization rule, each of\nwhich performs poorly at high utilizations of the road network, while their\nproper combination reaches a superior performance. The result is a considerable\nreduction not only in the average travel times, but also of their variation.\nSimilar control approaches could be applied to the coordination of logistic and\nproduction processes.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Stability of pairwise social dilemma games: destructive agents,\n  constructive agents, and their joint effects: Destructive agents, who opt out of the game and indiscriminately harm others,\nparadoxically foster cooperation, representing an intriguing variant of the\nvoluntary participation strategy. Yet, their impact on cooperation remains\ninadequately understood, particularly in the context of pairwise social dilemma\ngames and in comparison to their counterparts, constructive agents, who opt out\nof the game but indiscriminately benefit others. Furthermore, little is known\nabout the combined effects of both agent types on cooperation dynamics. Using\nreplicator dynamics in infinite and well-mixed populations, we find that,\ncontrary to their role in facilitating cooperation in multiplayer games,\ndestructive agents fail to encourage cooperation in pairwise social dilemmas.\nInstead, they destabilize and may even replace defection in the prisoners'\ndilemma and stag-hunt games. Similarly, in the chicken game, they can\ndestabilize or replace the mixed equilibrium of cooperation and defection, and\nthey undermine cooperation in the harmony game. Conversely, constructive\nagents, when their payoffs exceed their contributions to opponents, can exhibit\neffects similar to destructive agents. However, if their payoffs are lower,\nwhile they destabilize defection in prisoners' dilemma and stag-hunt games,\nthey do not disrupt the cooperation equilibrium in harmony games and have a\nnegligible impact on the coexistence of cooperation in chicken games. The\ncombination of destructive and constructive agents does not facilitate\ncooperation but instead generates complex evolutionary dynamics, including\nbi-stable, tri-stable, and quad-stable states, with outcomes contingent on\ntheir relative payoffs and game types. These results, taken together, enhance\nour understanding of the impact of the voluntary participation mechanism on\ncooperation, contributing to a more comprehensive understanding of its\ninfluence.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Voting power in the Council of the European Union: A comprehensive\n  sensitivity analysis: The Council of the European Union (EU) is one of the main decision-making\nbodies of the EU. Many decisions require a qualified majority: the support of\n55% of the member states (currently 15) that represent at least 65% of the\ntotal population. We investigate how the power distribution, based on the\nShapley-Shubik index, and the proportion of winning coalitions change if these\ncriteria are modified within reasonable bounds. The influence of the two\ncountries with about 4% of the total population each is found to be almost\nflat. The level of decisiveness decreases if the population criterion is above\n68% or the states criterion is at least 17. The proportion of winning\ncoalitions can be increased from 13.2% to 20.8% (30.1%) such that the maximal\nrelative change in the Shapley--Shubik indices remains below 3.5% (5.5%). Our\nresults are indispensable to evaluate any proposal for reforming the qualified\nmajority voting system.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Social applications of two-dimensional Ising models: I review three socio-economic models of economic opinions, urban segregation,\nand language change and show that the well known two-dimensional Ising model\ngives about the same results in each case.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Robustness and modular structure in networks: Complex networks have recently attracted much interest due to their\nprevalence in nature and our daily lives [1, 2]. A critical property of a\nnetwork is its resilience to random breakdown and failure [3-6], typically\nstudied as a percolation problem [7-9] or by modeling cascading failures\n[10-12]. Many complex systems, from power grids and the Internet to the brain\nand society [13-15], can be modeled using modular networks comprised of small,\ndensely connected groups of nodes [16, 17]. These modules often overlap, with\nnetwork elements belonging to multiple modules [18, 19]. Yet existing work on\nrobustness has not considered the role of overlapping, modular structure. Here\nwe study the robustness of these systems to the failure of elements. We show\nanalytically and empirically that it is possible for the modules themselves to\nbecome uncoupled or non-overlapping well before the network disintegrates. If\noverlapping modular organization plays a role in overall functionality,\nnetworks may be far more vulnerable than predicted by conventional percolation\ntheory.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Imperfect spreading on temporal networks: We study spreading on networks where the contact dynamics between the nodes\nis governed by a random process and where the inter-contact time distribution\nmay differ from the exponential. We consider a process of imperfect spreading,\nwhere transmission is successful with a determined probability at each contact.\nWe first derive an expression for the inter-success time distribution,\ndetermining the speed of the propagation, and then focus on a problem related\nto epidemic spreading, by estimating the epidemic threshold in a system where\nnodes remain infectious during a finite, random period of time. Finally, we\ndiscuss the implications of our work to design an efficient strategy to enhance\nspreading on temporal networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Evolutionary dynamics of group cooperation with asymmetrical\n  environmental feedback: In recent years, there has been growing interest in studying evolutionary\ngames with environmental feedback. Previous studies exclusively focus on\ntwo-player games. However, extension to multi-player game is needed to study\nproblems such as microbial cooperation and crowdsourcing collaborations. Here,\nwe study coevolutionary public goods games where strategies coevolve with the\nmultiplication factors of group cooperation. Asymmetry can arise in such\nenvironmental feedback, where games organized by focal cooperators may have a\ndifferent efficiency than the ones by defectors. Our analysis shows that\nco-evolutionary dynamics with asymmetrical environmental feedback can yield\noscillatory convergence to persistent cooperation, if the relative changing\nspeed of cooperators' multiplication factor is above a certain threshold. Our\nwork provides useful insights into sustaining group cooperation in a changing\nworld.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Public Transport Networks under Random Failure and Directed Attack: The behaviour of complex networks under failure or attack depends strongly on\nthe specific scenario. Of special interest are scale-free networks, which are\nusually seen as robust under random failure but appear to be especially\nvulnerable to targeted attacks. In a recent study of public transport networks\nof 14 major cities of the world we have shown that these systems when\nrepresented by appropriate graphs may exhibit scale-free behaviour. In this\npaper we briefly review some of the recent results about the effects that\ndefunct or removed nodes have on the properties of public transport networks.\nSimulating different directed attack strategies, we derive vulnerability\ncriteria that result in minimal strategies with high impact on these systems.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Evolution of family systems and resultant socio-economic structures: Families form the basis of society, and anthropologists have characterised\nvarious family systems. This study developed a multi-level evolutionary model\nof pre-industrial agricultural societies to simulate the evolution of family\nsystems and determine how each of them adapts to environmental conditions and\nforms a characteristic socio-economic structure. In the model, competing\nsocieties evolve, which themselves comprise multiple evolving families that\ngrow through family labour. Each family has two strategy parameters: the time\nchildren leave the parental home and the distribution of inheritance among\nsiblings. The evolution of these parameters demonstrates that four basic family\nsystems emerge; families can become either nuclear or extended, and have either\nan equal or unequal inheritance distribution. Nuclear families emerge where\nland resources are sufficient, whereas extended families emerge where land\nresources are limited. Equal inheritance emerges where the amount of wealth\nrequired for a family to survive is large, whereas unequal inheritance emerges\nwhere the required wealth is small. Analyses on the wealth distribution of\nfamilies demonstrate a higher level of poverty in extended families, and that\nthe accumulation of wealth is accelerated for unequal inheritance. By comparing\nwealth distributions in the model with historical data, family systems are\nassociated with characteristic economic structures and modern social\nideologies. Empirical data analyses using the cross-cultural ethnographic\ndatabase verify the theoretical relationship between the environmental\nconditions, family systems, and socio-economic structures. Theoretical studies\nby this simple constructive model, as presented here, will integrate the\nunderstandings of family systems in evolutionary anthropology, demography, and\nsocioeconomic histories.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Multiobjective Direct Policy Search Using Physically Based Operating\n  Rules in Multireservoir Systems: This study explores the ways to introduce physical interpretability into the\nprocess of optimizing operating rules for multireservoir systems with multiple\nobjectives. Prior studies applied the concept of direct policy search (DPS), in\nwhich the release policy is expressed as a set of parameterized functions\n(e.g., neural networks) that are optimized by simulating the performance of\ndifferent parameter value combinations over a testing period. The problem with\nthis approach is that the operators generally avoid adopting such artificial\nblack-box functions for the direct real-time control of their systems,\npreferring simpler tools with a clear connection to the system physics. This\nstudy addresses this mismatch by replacing the black-box functions in DPS with\nphysically based parameterized operating rules, for example by directly using\ntarget levels in dams as decision variables. This leads to results that are\nphysically interpretable and may be more acceptable to operators. The\nmethodology proposed in this work is applied to a network of five reservoirs\nand four power plants in the Nechi catchment in Colombia, with four interests\ninvolved: average energy generation, firm energy generation, flood hazard, and\nflow regime alteration. The release policy is expressed depending on only 12\nparameters, which significantly reduces the computational complexity compared\nto existing approaches of multiobjective DPS. The resulting four-dimensional\nPareto-approximate set offers a variety of operational strategies from which\noperators may choose one that corresponds best to their preferences. For\ndemonstration purposes, one particular optimized policy is selected and its\nparameter values are analyzed to illustrate how the physically based operating\nrules can be directly interpreted by the operators.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Principles of the Battery Data Genome: Electrochemical energy storage is central to modern society -- from consumer\nelectronics to electrified transportation and the power grid. It is no longer\njust a convenience but a critical enabler of the transition to a resilient,\nlow-carbon economy. The large pluralistic battery research and development\ncommunity serving these needs has evolved into diverse specialties spanning\nmaterials discovery, battery chemistry, design innovation, scale-up,\nmanufacturing and deployment. Despite the maturity and the impact of battery\nscience and technology, the data and software practices among these disparate\ngroups are far behind the state-of-the-art in other fields (e.g. drug\ndiscovery), which have enjoyed significant increases in the rate of innovation.\nIncremental performance gains and lost research productivity, which are the\nconsequences, retard innovation and societal progress. Examples span every\nfield of battery research , from the slow and iterative nature of materials\ndiscovery, to the repeated and time-consuming performance testing of cells and\nthe mitigation of degradation and failures. The fundamental issue is that\nmodern data science methods require large amounts of data and the battery\ncommunity lacks the requisite scalable, standardized data hubs required for\nimmediate use of these approaches. Lack of uniform data practices is a central\nbarrier to the scale problem. In this perspective we identify the data- and\nsoftware-sharing gaps and propose the unifying principles and tools needed to\nbuild a robust community of data hubs, which provide flexible sharing formats\nto address diverse needs. The Battery Data Genome is offered as a data-centric\ninitiative that will enable the transformative acceleration of battery science\nand technology, and will ultimately serve as a catalyst to revolutionize our\napproach to innovation.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Investor-patent networks as mutualistic networks: Venture capital investments in startups have come to represent an important\ndriver of technological innovation, in parallel to corporate- and\ngovernment-directed efforts. Part of the future of artificial intelligence,\nmedicine and quantum computing now depends upon a large number of venture\ninvestment decisions whose robustness against increasingly frequent crises has\ntherefore become crucial. To shed light on this issue, and by combining\nlarge-scale financial, startup and patent datasets, we analyze the interactions\nbetween venture capitalists and technologies as an explicit bipartite\npatent-investor network. Our results reveal that this network is topologically\nmutualistic because of the prevalence of links between generalist investors,\nwhose portfolios are technologically diversified, and general-purpose\ntechnologies, characterized by a broad spectrum of use. As a consequence, the\nrobustness of venture-funded technological innovation against different types\nof crises is affected by the high nestedness and low modularity, with high\nconnectance, associated with mutualistic networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The role of voting intention in public opinion polarization: We introduce and study a simple model for the dynamics of voting intention in\na population of agents that have to choose between two candidates. The level of\nindecision of a given agent is modeled by its propensity to vote for one of the\ntwo alternatives, represented by a variable $p \\in [0,1]$. When an agent $i$\ninteracts with another agent $j$ with propensity $p_j$, then $i$ either\nincreases its propensity $p_i$ by $h$ with probability $P_{ij}=\\omega\np_i+(1-\\omega)p_j$, or decreases $p_i$ by $h$ with probability $1-P_{ij}$,\nwhere $h$ is a fixed step. We analyze the system by a rate equation approach\nand contrast the results with Monte Carlo simulations. We found that the\ndynamics of propensities depends on the weight $\\omega$ that an agent assigns\nto its own propensity. When all the weight is assigned to the interacting\npartner ($\\omega=0$), agents' propensities are quickly driven to one of the\nextreme values $p=0$ or $p=1$, until an extremist absorbing consensus is\nachieved. However, for $\\omega>0$ the system first reaches a quasi-stationary\nstate of symmetric polarization where the distribution of propensities has the\nshape of an inverted Gaussian with a minimum at the center $p=1/2$ and two\nmaxima at the extreme values $p=0,1$, until the symmetry is broken and the\nsystem is driven to an extremist consensus. A linear stability analysis shows\nthat the lifetime of the polarized state, estimated by the mean consensus time\n$\\tau$, diverges as $\\tau \\sim (1-\\omega)^{-2} \\ln N$ when $\\omega$ approaches\n$1$, where $N$ is the system size. Finally, a continuous approximation allows\nto derive a transport equation whose convection term is compatible with a drift\nof particles from the center towards the extremes.",
        "category": "physics_soc-ph"
    },
    {
        "text": "\"Illusion of control\" in Minority and Parrondo Games: Human beings like to believe they are in control of their destiny. This\nubiquitous trait seems to increase motivation and persistence, and is probably\nevolutionarily adaptive. But how good really is our ability to control? How\nsuccessful is our track record in these areas? There is little understanding of\nwhen and under what circumstances we may over-estimate or even lose our ability\nto control and optimize outcomes, especially when they are the result of\naggregations of individual optimization processes. Here, we demonstrate\nanalytically using the theory of Markov Chains and by numerical simulations in\ntwo classes of games, the Minority game and the Parrondo Games, that agents who\noptimize their strategy based on past information actually perform worse than\nnon-optimizing agents. In other words, low-entropy (more informative)\nstrategies under-perform high-entropy (or random) strategies. This provides a\nprecise definition of the \"illusion of control\" in set-ups a priori defined to\nemphasize the importance of optimization.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Future of Nuclear Energy: Facts and Fiction Chapter I: Nuclear\n  Fission Energy Today: Nuclear fission energy is considered to be somewhere between the holy grail,\nrequired to solve all energy worries of the human industrialized civilization,\nand a fast path directly to hell. Discussions about future energy sources and\nthe possible contribution from nuclear energy are often dominated by variations\nof fundamentalists and often irrational approaches. As a result, very little is\nknown by the general public and even by decision makers about the contribution\nof nuclear energy today, about uranium supplies, uranium resources and current\nand future technological challenges and limitations.\n  This analysis about nuclear energy and its contribution for tomorrow tries to\nshed light on the nuclear reality and its limitations in the near and long term\nfuture. The report, presented in four chapters, is based essentially on the\ndata provided in the documents from the IAEA (International Atomic Energy\nAdministration) and the NEA (the Nuclear Energy Agency from the OECD countries,\nthe WNA (World Nuclear Association) and the IEA (International Energy Agency).",
        "category": "physics_soc-ph"
    },
    {
        "text": "Individual and Group Dynamics in Purchasing Activity: As a major part of the daily operation in an enterprise, purchasing frequency\nis of constant change. Recent approaches on the human dynamics can provide some\nnew insights into the economic behaviors of companies in the supply chain. This\npaper captures the attributes of creation times of purchase orders to an\nindividual vendor, as well as to all vendors, and further investigates whether\nthey have some kind of dynamics by applying logarithmic binning to the\nconstruction of distribution plot. It's found that the former displays a\npower-law distribution with approximate exponent 2.0, while the latter is\nfitted by a mixture distribution with both power-law and exponential\ncharacteristics. Obviously, two distinctive characteristics are presented for\nthe interval time distribution from the perspective of individual dynamics and\ngroup dynamics. Actually, this mixing feature can be attributed to the fitting\ndeviations as they are negligible for individual dynamics, but those of\ndifferent vendors are cumulated and then lead to an exponential factor for\ngroup dynamics. To better describe the mechanism generating the heterogeneity\nof purchase order assignment process from the objective company to all its\nvendors, a model driven by product life cycle is introduced, and then the\nanalytical distribution and the simulation result are obtained, which are in\ngood line with the empirical data.",
        "category": "physics_soc-ph"
    },
    {
        "text": "ASTROMOVES: Astrophysics, Diversity, Mobility: The US astronomy/astrophysics community comes together to create a decadal\nreport that summarizes grant funding priorities, observatory & instrumental\npriorities as well as community accomplishments and community goals such as\nincreasing the number of women and the number of people from underrepresented\ngroups. In the 2010 US National Academies Decadal Survey of Astronomy (National\nResearch Council, 2010), it was suggested that having to move so frequently\nwhich is a career necessity may be unattractive to people wanting to start a\nfamily, especially impacting women. Whether in Europe or elsewhere, as\npostdocs, astrophysicists will relocate every two to three years, until they\nsecure a permanent position or leave research altogether. Astrophysicists do\nperceive working abroad as important and positive for their careers (Parenti,\n2002); however, it was found that the men at equal rank had not had to spend as\nmuch time abroad to further their careers (Fohlmeister & Helling, 2012). By\nimplication, women need to work abroad longer or have more positions abroad to\nachieve the same rank as men. Astrophysicists living in the United Kingdom\nprefer to work in their country of origin, but many did not do so because of\nworse working conditions or difficultly finding a job for their spouse\n(Fohlmeister & Helling, 2014). In sum, mobility and moving is necessary for a\ncareer in astrophysics, and even more necessary for women, but astrophysicists\nprefer not to move as frequently as needed to maintain a research career. To\ngather more data on these issues and to broaden the discourse beyond\nmale/female to include the gender diverse as well as to include other forms of\ndiversity, I designed the ASTROMOVES project which is funded through a Marie\nCurie Individual Fellowship. Though slowed down by COVID-19, several interviews\nhave been conducted and some preliminary results will be presented.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The management of scientific and technological infrastructures: the case\n  of the Mexican National Laboratories: The effectiveness of research units is assessed on the basis of their\nperformance in relation to scientific, technological and innovation production,\nthe quality of their results and their contribution to the solution of\nscientific and social problems. This paper examines the management practices\nemployed in some Mexican National Laboratories in order to identify those that\ncould explain their effectiveness in meeting their objectives. The results of\nother works that propose common elements among laboratories with outstanding\nperformance are used and verified directly in the field. Considering the\ninherent complexity of each field of knowledge and the socio-spatial\ncharacteristics in which the laboratories operate, we report which management\npractices are relevant for their effectiveness, how they contribute to their\nconsolidation as fundamental scientific and technological infrastructures, how\nthese can be translated into indicators that support the evaluation of their\nperformance, and still pending.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A call for scientists to halt the spoiling of the night sky with\n  artificial light and satellites: Unfettered access to dark night skies is rapidly diminishing, due to light\npollution and satellite mega-constellations tracks. Scientists should wake up\nand do more to stand up to Big Light and Big Space and preserve this natural\nresource.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A dataset to assess mobility changes in Chile following local\n  quarantines: Fighting the COVID-19 pandemic, most countries have implemented\nnon-pharmaceutical interventions like wearing masks, physical distancing,\nlockdown, and travel restrictions. Because of their economic and logistical\neffects, tracking mobility changes during quarantines is crucial in assessing\ntheir efficacy and predicting the virus spread. Chile, one of the worst-hit\ncountries in the world, unlike many other countries, implemented quarantines at\na more localized level, shutting down small administrative zones, rather than\nthe whole country or large regions. Given the non-obvious effects of these\nlocalized quarantines, tracking mobility becomes even more critical in Chile.\nTo assess the impact on human mobility of the localized quarantines in Chile,\nwe analyze a mobile phone dataset made available by Telef\\'onica Chile, which\ncomprises 31 billion eXtended Detail Records and 5.4 million users covering the\nperiod February 26th to September 20th, 2020. From these records, we derive\nthree epidemiologically relevant metrics describing the mobility within and\nbetween comunas. The datasets made available can be used to fight the COVID-19\nepidemics, particularly for localized quarantines' less understood effect.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A gentle introduction to the minimal Naming Game: Social conventions govern countless behaviors all of us engage in every day,\nfrom how we greet each other to the languages we speak. But how can shared\nconventions emerge spontaneously in the absence of a central coordinating\nauthority? The Naming Game model shows that networks of locally interacting\nindividuals can spontaneously self-organize to produce global coordination.\nHere, we provide a gentle introduction to the main features of the model, from\nthe dynamics observed in homogeneously mixing populations to the role played by\nmore complex social networks, and to how slight modifications of the basic\ninteraction rules give origin to a richer phenomenology in which more\nconventions can co-exist indefinitely.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Noctalgia (sky grief): Our Brightening Night Skies and Loss of\n  Environment for Astronomy and Sky Traditions: Fifty years after the first mention of light pollution in Science, the\njournal recently elevated this topic to the cover of its 16 June 2023 issue,\nhighlighting the large impact on human and ecological health, circadian\nrhythms, migratory patterns, and more. We offer here the term noctalgia to\nexpress \"sky grief\" for the accelerating loss of the home environment of our\nshared skies - representing loss of science, heritage, millennia-old sky\ntraditions, place-based language, and more - and summarize next steps to\naddress the protection of our nighttime and daytime skies.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Application of SIR epidemiological model: new trends: The simplest epidemiologic model composed by mutually exclusive compartments\nSIR (susceptible-infected-susceptible) is presented to describe a reality. From\nhealth concerns to situations related with marketing, informatics or even\nsociology, several are the fields that are using this epidemiological model as\na first approach to better understand a situation. In this paper, the basic\ntransmission model is analyzed, as well as simple tools that allows us to\nextract a great deal of information about possible solutions. A set of\napplications - traditional and new ones - is described to show the importance\nof this model.",
        "category": "physics_soc-ph"
    },
    {
        "text": "SIS Epidemics in Multilayer-based Temporal Networks: To improve the accuracy of network-based SIS models we introduce and study a\nmultilayer representation of a time-dependent network. In particular, we assume\nthat individuals have their long-term (permanent) contacts that are always\npresent, identifying in this way the first network layer. A second network\nlayer also exists, where the same set of nodes can be connected by occasional\nlinks, created with a given probability. While links of the first layer are\npermanent, a link of the second layer is only activated with some probability\nand under the condition that the two nodes, connected by this link, are\nsimultaneously participating to the temporary link. We develop a model for the\nSIS epidemic on this time-dependent network, analyze equilibrium and stability\nof the corresponding mean-field equations, and shed some light on the role of\nthe temporal layer on the spreading process.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Robustness on distributed coupling networks with multiple dependent\n  links from finite functional components: The rapid advancement of technology underscores the critical importance of\nrobustness in complex network systems. This paper presents a framework for\ninvestigating the structural robustness of interconnected network models. This\npaper presents a framework for investigating the structural robustness of\ninterconnected network models. In this context, we define functional nodes\nwithin interconnected networks as those belonging to clusters of size greater\nthan or equal to $s$ in the local network, while maintaining at least $M$\nsignificant dependency links. This model presents precise analytical\nexpressions for the cascading failure process, the proportion of functional\nnodes in the stable state, and a methodology for calculating the critical\nthreshold. The findings reveal an abrupt phase transition behavior in the\nsystem following the initial failure. Additionally, we observe that the system\nnecessitates higher internal connection densities to avert collapse, especially\nwhen more effective support links are required. These results are validated\nthrough simulations using both Poisson and power-law network models, which\nalign closely with the theoretical outcomes. The method proposed in this study\ncan assist decision-makers in designing more resilient reality-dependent\nsystems and formulating optimal protection strategies.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Approaches to Open Access in Scientific Publishing: Approaches to scientific journal publishing that provide free access to all\nreaders are challenging the standard subscription-based model. But in domains\nthat have a well-functioning system of publicly accessible preprint\nrepositories like arXiv, Open Access is already effectively available. In\nphysics, such repositories have long coexisted constructively with refereed,\nsubscription based journals. Trying to replace this by a system based on\njournals whose revenue is derived primarily from fees charged to authors is\nunlikely to provide a better guarantee of Open Access, and may be in conflict\nwith the maintenance of high quality standards.",
        "category": "physics_soc-ph"
    },
    {
        "text": "On the inefficiency of ride-sourcing services towards urban congestion: The advent of shared-economy and smartphones made on-demand transportation\nservices possible, which created additional opportunities, but also more\ncomplexity to urban mobility. Companies that offer these services are called\nTransportation Network Companies (TNCs) due to their internet-based nature.\nAlthough ride-sourcing is the most notorious service TNCs provide, little is\nknown about to what degree its operations can interfere in traffic conditions,\nwhile replacing other transportation modes, or when a large number of idle\nvehicles is cruising for passengers. We experimentally analyze the efficiency\nof TNCs using taxi trip data from a Chinese megacity and a agent-based\nsimulation with a trip-based MFD model for determining the speed. We\ninvestigate the effect of expanding fleet sizes for TNCs, passengers'\ninclination towards sharing rides, and strategies to alleviate urban\ncongestion. We show that the lack of coordination of objectives between TNCs\nand society can create 37% longer travel times and significant congestion.\nMoreover, allowing shared rides is not capable of decreasing total distance\ntraveled due to higher empty kilometers traveled. Elegant parking management\nstrategies can prevent idle vehicles from cruising without assigned passengers\nand lower to 7% the impacts of the absence of coordination.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Optimizing vaccine distribution networks in low and middle-income\n  countries: Vaccination has been proven to be the most effective method to prevent\ninfectious diseases. However, there are still millions of children in low and\nmiddle-income countries who are not covered by routine vaccines and remain at\nrisk. The World Health Organization - Expanded Programme on Immunization\n(WHO-EPI) was designed to provide universal childhood vaccine access for\nchildren across the world and in this work, we address the design of the\ndistribution network for WHO-EPI vaccines. In particular, we formulate the\nnetwork design problem as a mixed integer program (MIP) and present a new\nalgorithm for typical problems that are too large to be solved using commercial\nMIP software. We test the algorithm using data derived from four different\ncountries in sub-Saharan Africa and show that the algorithm is able to obtain\nhigh-quality solutions for even the largest problems within a few minutes.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Studying complex tourism systems: a novel approach based on networks\n  derived from a time series: A tourism destination is a complex dynamic system. As such it requires\nspecific methods and tools to be analyzed and understood in order to better\ntailor governance and policy measures for steering the destination along an\nevolutionary growth path. Many proposals have been put forward for the\ninvestigation of complex systems and some have been successfully applied to\ntourism destinations. This paper uses a recent suggestion, that of transforming\na time series into a network and analyzes it with the objective of uncovering\nthe structural and dynamic features of a tourism destination. The algorithm,\ncalled visibility graph, is simple and its implementation straightforward, yet\nit is able to provide a number of interesting insights. An example is worked\nout using data from two destinations: Italy as a country and the island of\nElba, one of its most known areas.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Using reinforcement learning to minimize taxi idle times: Taxis spend a significant amount of time idle, searching for passengers. The\nroutes vacant taxis should follow in order to minimize their idle times are\nhard to calculate; they depend on complex quantities like passenger demand,\ntraffic conditions, and inter-taxi competition. Here we explore if\nreinforcement learning (RL) can be used for this purpose. Using real-world data\nto characterize passenger demand, we show RL-taxis indeed learn to how to\nreduce their idle time in many environments. In particular, a single RL-taxi\noperating in a population of regular taxis learns to out-perform its rivals by\na significant margin.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Robustness of Network of Networks with Interdependent and Interconnected\n  links: Robustness of network of networks (NON) has been studied only for dependency\ncoupling (J.X. Gao et. al., Nature Physics, 2012) and only for connectivity\ncoupling (E.A. Leicht and R.M. D Souza, arxiv:0907.0894). The case of network\nof n networks with both interdependent and interconnected links is more\ncomplicated, and also more closely to real-life coupled network systems. Here\nwe develop a framework to study analytically and numerically the robustness of\nthis system. For the case of starlike network of n ER networks, we find that\nthe system undergoes from second order to first order phase transition as\ncoupling strength q increases. We find that increasing intra-connectivity links\nor inter-connectivity links can increase the robustness of the system, while\nthe interdependency links decrease its robustness. Especially when q=1, we find\nexact analytical solutions of the giant component and the first order\ntransition point. Understanding the robustness of network of networks with\ninterdependent and interconnected links is helpful to design resilient\ninfrastructures.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The semantics of constellation line figures: We answer the question whether, when forming constellations in the night sky,\npeople in astronomical cultures around the world consistently imagine and\nassign the same symbolism to the same (type of) star cluster. Evidence of\nsemantic universality has so far been anecdotal. We use two complementary\ndefinitions for a star cluster: (1) a star group in a particular sky region\n(regardless of its exact shape), and (2) a star group with a particular shape\nand brightness (regardless of its location in the sky). Over a dataset of 1903\nconstellations from 75 astronomical cultures, we find semantic parallels which\nare likely culturally induced: body parts in the sky region delineated by the\nInternational Astronomical Union (IAU) as Ori, fish in Cru and Sco, geometric\nsymbols in Cru, groups in UMa, mammals in UMa, and reptiles in Sco.\nSurprisingly, we find many more significant semantic parallels which can only\nbe naturally induced by the shape and composition of the star pattern\nunderlying a constellation (or, are endogenous to the sky rather than\nculture-dependent): arthropods in IAU Sco, body parts in Tau, geometric and\ngroup symbols in star clusters (regardless of sky region) with a small number\nof bright stars comparable in magnitude, humanoids and mammals naturalistically\ndrawn in star clusters with large spatial diameter and many stars, landscapes\nin IAU Eri, man-made objects of various types in many IAU regions, and reptiles\nconsistently drawn in star clusters with low aspect ratio or low branching in\nthe minimum spanning tree drawn over the stars. These naturally induced\nsemantics show that there are universal (rather than only cultural) thought\npatterns behind forming and naming constellations.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Robust criticality of Ising model on rewired directed networks: We show that preferential rewiring, which is supposed to mimick the behaviour\nof financial agents, changes a directed-network Ising ferromagnet with a single\ncritical point into a model with robust critical behaviour. For the non-rewired\nrandom graph version, due to a constant number of links out-going from each\nsite, we write a simple mean-field-like equation describing the behaviour of\nmagnetization; we argue that it is exact and support the claim with extensive\nMonte Carlo simulations. For the rewired version, this equation is obeyed only\nat low temperatures. At higher temperatures, rewiring leads to strong\nheterogeneities, which apparently invalidates mean-field arguments and induces\nlarge fluctuations and divergent susceptibility. Such behaviour is traced back\nto the formation of a relatively small core of agents which influence the\nentire system.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Multilevel compression of random walks on networks reveals hierarchical\n  organization in large integrated systems: To comprehend the hierarchical organization of large integrated systems, we\nintroduce the hierarchical map equation, which reveals multilevel structures in\nnetworks. In this information-theoretic approach, we exploit the duality\nbetween compression and pattern detection; by compressing a description of a\nrandom walker as a proxy for real flow on a network, we find regularities in\nthe network that induce this system-wide flow. Finding the shortest multilevel\ndescription of the random walker therefore gives us the best hierarchical\nclustering of the network, the optimal number of levels and modular partition\nat each level, with respect to the dynamics on the network. With a novel search\nalgorithm, we extract and illustrate the rich multilevel organization of\nseveral large social and biological networks. For example, from the global air\ntraffic network we uncover countries and continents, and from the pattern of\nscientific communication we reveal more than 100 scientific fields organized in\nfour major disciplines: life sciences, physical sciences, ecology and earth\nsciences, and social sciences. In general, we find shallow hierarchical\nstructures in globally interconnected systems, such as neural networks, and\nrich multilevel organizations in systems with highly separated regions, such as\nroad networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Delineating Intra-Urban Spatial Connectivity Patterns by\n  Travel-Activities: A Case Study of Beijing, China: Travel activities have been widely applied to quantify spatial interactions\nbetween places, regions and nations. In this paper, we model the spatial\nconnectivities between 652 Traffic Analysis Zones (TAZs) in Beijing by a taxi\nOD dataset. First, we unveil the gravitational structure of intra-urban spatial\nconnectivities of Beijing. On overall, the inter-TAZ interactions are well\ngoverned by the Gravity Model $G_{ij} = {\\lambda}p_{i}p_{j}/d_{ij}$, where\n$p_{i}$, $p_{j}$ are degrees of TAZ $i$, $j$ and $d_{ij}$ the distance between\nthem, with a goodness-of-fit around 0.8. Second, the network based analysis\nwell reveals the polycentric form of Beijing. Last, we detect the semantics of\ninter-TAZ connectivities based on their spatiotemporal patterns. We further\nfind that inter-TAZ connections deviating from the Gravity Model can be well\nexplained by link semantics.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Third State of the Schelling Model of Residential Dynamics: The Schelling model of segregation between two groups of residential agents\n(Schelling 1971; Schelling 1978) reflects the most abstract view of the\nnon-economic forces of residential migrations: be close to people of 'your\nown'. The model assumes that the residential agent, located in the neighborhood\nwhere the fraction of 'friends' is less than a predefined threshold value F,\ntries to relocate to a neighborhood for which this fraction is above F. It is\nwell known that for the equal groups, depending on F, Schelling's residential\npattern converges either to complete integration (random pattern) or\nsegregation. We investigate Schelling model pattern dynamics as dependent on F,\nthe ratio of the group numbers and the size of the neighborhood and demonstrate\nthat the traditional integrate-segregate dichotomy is incomplete. In case of\nunequal groups, there exists the wide interval of the F-values that entails the\nthird persistent residential pattern, in which part of the majority population\nsegregates, while the rest remains integrated with the minority. We also\ndemonstrate that Schelling model dynamics essentially depends on the\ndescription of agents' residential behavior. To obtain sociologically\nmeaningful results, the agents should be satisficers, and the fraction of the\nagents who relocate irrespective of the neighborhood state should be non-zero.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Microscopic modeling and analysis of collective decision making:\n  equality bias leads suboptimal solutions: We discuss a novel microscopic model for collective decision-making\ninteracting multi-agent systems. In particular we are interested in modeling a\nwell known phenomena in the experimental literature called equality bias, where\nagents tend to behave in the same way as if they were as good, or as bad, as\ntheir partner. We analyze the introduced problem and we prove the suboptimality\nof the collective decision-making in the presence of equality bias. Numerical\nexperiments are addressed in the last section.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Mining Google and Apple mobility data: Temporal Anatomy for COVID-19\n  Social Distancing: We employ the Google and Apple mobility data to identify, quantify and\nclassify different degrees of social distancing and characterise their imprint\non the first wave of the COVID-19 pandemic in Europe and in the United States.\nWe identify the period of enacted social distancing via Google and Apple data,\nindependently from the political decisions. Interestingly we observe a general\ndecrease in the infection rate occurring two to five weeks after the onset of\nmobility reduction for the European countries and the American states.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Model of knowledge transfer within an organisation: Many studies show that the acquisition of knowledge is the key to build\ncompetitive advantage of companies. We propose a simple model of knowledge\ntransfer within the organization and we implement the proposed model using\ncellular automata technique. In this paper the organisation is considered in\nthe context of complex systems. In this perspective, the main role in\norganisation is played by the network of informal contacts and the distributed\nleadership. The goal of this paper is to check which factors influence the\nefficiency and effectiveness of knowledge transfer. Our studies indicate a\nsignificant role of initial concentration of chunks of knowledge for knowledge\ntransfer process, and the results suggest taking action in the organisation to\nshorten the distance (social distance) between people with different levels of\nknowledge, or working out incentives to share knowledge.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Strategies for COVID-19 vaccination under a shortage scenario: a\n  geo-stochastic modelling approach: In a world being hit by waves of COVID-19, vaccination is a light on the\nhorizon. However, the roll-out of vaccination strategies and their influence on\nthe pandemic are still open problems. In order to compare the effect of various\nstrategies proposed by the World Health Organization and other authorities, a\npreviously developed SEIRS stochastic model of geographical spreading of the\nvirus is extended by adding a compartment for vaccinated people. The parameters\nof the model were fitted to describe the pandemic evolution in Argentina,\nMexico and Spain to analyze the effect of the proposed vaccination strategies.\nThe mobility parameters allow to simulate different social behaviors (e.g.\nlock-down interventions). Schemes in which vaccines are applied homogeneously\nin all the country, or limited to the most densely-populated areas, are\nsimulated and compared. The second strategy is found to be more effective.\nMoreover, under the current global shortage of vaccines, it should be remarked\nthat immunization is enhanced when mobility is reduced. Additionally,\nrepetition of vaccination campaigns should be timed considering the immunity\nlapse of the vaccinated (and recovered) people. Finally, the model is extended\nto include the effect of isolation of detected positive cases, shown to be\nimportant to reduce infections.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Properties of pedestrians walking in line: Stepping behavior: In human crowds, interactions among individuals give rise to a variety of\nself-organized collective motions that help the group to effectively solve the\nproblem of coordination. However, it is still not known exactly how humans\nadjust their behavior locally, nor what are the direct consequences on the\nemergent organization. One of the underlying mechanisms of adjusting individual\nmotions is the stepping dynamics. In this paper, we present first quantitative\nanalysis on the stepping behavior in a one-dimensional pedestrian flow studied\nunder controlled laboratory conditions. We find that the step length is\nproportional to the velocity of the pedestrian, and is directly related to the\nspace available in front of him, while the variations of the step duration are\nmuch smaller. This is in contrast with locomotion studies performed on isolated\npedestrians and shows that the local density has a direct influence on the\nstepping characteristics. Furthermore, we study the phenomena of\nsynchronization -walking in lockstep- and show its dependence on flow\ndensities. We show that the synchronization of steps is particularly important\nat high densities, which has direct impact on the studies of optimizing\npedestrians flow in congested situations. However, small synchronization and\nantisynchronization effects are found also at very low densities, for which no\nsteric constraints exist between successive pedestrians, showing the natural\ntendency to synchronize according to perceived visual signals.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Response to the paper: Theoretical understanding of evolutionary\n  dynamics on inhomogeneous networks: As a co-author of the paper Theoretical understanding of evolutionary\ndynamics on inhomogeneous networks, I would like to express my disagreement\nwith the conclusion of the paper. In this response, I present a thorough\nexamination of the assertions and methods in the paper. Although I may disagree\nwith several practices in the research group, I will confine my discussion to\nthe academic analysis of the paper in this response.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Layered social influence promotes multiculturality in the Axelrod model: Why is our society multicultural? Based on the two mechanisms of homophily\nand social influence, the classical model for the dissemination of cultures\nproposed by Axelrod predicts the existence of a fragmented regime where\ndifferent cultures can coexist in a social network. However, in such model the\nmulticultural regime is achievable only when a high number of cultural traits\nis present, and is not robust against cultural drift, i.e. the spontaneous\nmutations of agents' traits. In real systems, social influence is inherently\norganised in layers, meaning that individuals tend to diversify their\nconnections according to the topic on which they interact. In this work we show\nthat the observed persistence of multiculturality in real-world social systems\nis a natural consequence of the layered organisation of social influence. We\nfind that the critical number of cultural traits that separates the\nmonocultural and the multicultural regimes depends on the redundancy of\npairwise connections across layers. Surprisingly, for low values of structural\nredundancy the system is always in a multicultural state, independently on the\nnumber of traits, and is robust to the presence of cultural drift. Moreover, we\nshow that layered social influence allows the coexistence of different levels\nof consensus on different topics. The insight obtained from simulations on\nsynthetic graphs are confirmed by the analysis of two real-world social\nnetworks, where the multicultural regime persists even for a very small number\nof cultural traits, suggesting that the layered organisation of social\ninteractions might indeed be at the heart of multicultural societies.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A homoclinic route to asymptotic full cooperation in adaptive networks\n  and its failure: We consider the evolutionary dynamics of a cooperative game on an adaptive\nnetwork, where the strategies of agents (cooperation or defection) feed back on\ntheir local interaction topology. While mutual cooperation is the social\noptimum, unilateral defection yields a higher payoff and undermines the\nevolution of cooperation. Although no a priori advantage is given to\ncooperators, an intrinsic dynamical mechanism can lead asymptotically to a\nstate of full cooperation. In finite systems, this state is characterized by\nlong periods of strong cooperation interrupted by sudden episodes of\npredominant defection, suggesting a possible mechanism for the systemic failure\nof cooperation in real-world systems.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Opinion formation models based on game theory: A way to simulate the basic interactions between two individuals with\ndifferent opinions, in the context of strategic game theory, is proposed.\nVarious games are considered, which produce different kinds of opinion\nformation dynamics. First, by assuming that all individuals (players) are\nequals, we obtain the bounded confidence model of continuous opinion dynamics\nproposed by Deffuant et al. In such a model a tolerance threshold is defined,\nsuch that individuals with difference in opinion larger than the threshold can\nnot interact. Then, we consider that the individuals have different\ninclinations to change opinion and different abilities in convincing the\nothers. In this way, we obtain the so-called ``Stubborn individuals and\nOrators'' (SO) model, a generalization of the Deffuant et al. model, in which\nthe threshold tolerance is different for every couple of individuals. We\nexplore, by numerical simulations, the dynamics of the SO model, and we propose\nfurther generalizations that can be implemented.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Entropy and the Predictability of Online Life: Using mobile phone records and information theory measures, our daily lives\nhave been recently shown to follow strict statistical regularities, and our\nmovement patterns are to a large extent predictable. Here, we apply entropy and\npredictability measures to two data sets of the behavioral actions and the\nmobility of a large number of players in the virtual universe of a massive\nmultiplayer online game. We find that movements in virtual human lives follow\nthe same high levels of predictability as offline mobility, where future\nmovements can to some extent be predicted well if the temporal correlations of\nvisited places are accounted for. Time series of behavioral actions show\nsimilar high levels of predictability, even when temporal correlations are\nneglected. Entropy conditional on specific behavioral actions reveals that in\nterms of predictability negative behavior has a wider variety than positive\nactions. The actions which contain information to best predict an individual's\nsubsequent action are negative, such as attacks or enemy markings, while\npositive actions of friendship marking, trade and communication contain the\nleast amount of predictive information. These observations show that predicting\nbehavioral actions requires less information than predicting the mobility\npatterns of humans for which the additional knowledge of past visited locations\nis crucial, and that the type and sign of a social relation has an essential\nimpact on the ability to determine future behavior.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Directedness of information flow in mobile phone communication networks: Without having direct access to the information that is being exchanged,\ntraces of information flow can be obtained by looking at temporal sequences of\nuser interactions. These sequences can be represented as causality trees whose\nstatistics result from a complex interplay between the topology of the\nunderlying (social) network and the time correlations among the communications.\nHere, we study causality trees in mobile-phone data, which can be represented\nas a dynamical directed network. This representation of the data reveals the\nexistence of super-spreaders and super-receivers. We show that the tree\nstatistics, respectively the information spreading process, are extremely\nsensitive to the in-out degree correlation exhibited by the users. We also\nlearn that a given information, e.g., a rumor, would require users to\nretransmit it for more than 30 hours in order to cover a macroscopic fraction\nof the system. Our analysis indicates that topological node-node correlations\nof the underlying social network, while allowing the existence of information\nloops, they also promote information spreading. Temporal correlations, and\ntherefore causality effects, are only visible as local phenomena and during\nshort time scales. These results are obtained through a combination of theory\nand data analysis techniques.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Consensus and Polarisation in Competing Complex Contagion Processes: The rate of adoption of new information depends on reinforcement from\nmultiple sources in a way that often cannot be described by simple contagion\nprocesses. In such cases, contagion is said to be complex. Complex contagion\nhappens in the diffusion of human behaviours, innovations, and knowledge. Based\non that evidence, we propose a model that considers multiple, potentially\nasymmetric, and competing contagion processes and analyse its respective\npopulation-wide dynamics, bringing together ideas from complex contagion,\nopinion dynamics, evolutionary game theory, and language competition by\nshifting the focus from individuals to the properties of the diffusing\nprocesses. We show that our model spans a dynamical space in which the\npopulation exhibits patterns of consensus, dominance, and, importantly,\ndifferent types of polarisation, a more diverse dynamical environment that\ncontrasts with single simple contagion processes. We show how these patterns\nemerge and how different population structures modify them through a natural\ndevelopment of spatial correlations: structured interactions increase the range\nof the dominance regime by reducing that of dynamic polarisation, tight modular\nstructures can generate structural polarisation, depending on the interplay\nbetween fundamental properties of the processes and the modularity of the\ninteraction network.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Temporal evolution of adherents of the major religions in Mexico: Avrami\n  model application-Kolgomorov solid training: It applies a mathematical model of solid formation, the model of\nAvrami-Kolgomorov [Ausloos & Petroni, 2007] to model the time evolution of\npercentage of adherents of the major religions practiced in Mexico, adjusting\nthe corresponding parameters with available records in the period from 1950 to\n2000 [Molina-Hernandez, 2003; INEGI, 2005]. A comparison is made with the\napplication of the model to global trends and concludes that Catholicism is in\na marked disaggregation and trends of Christianity in Mexico are similar to\nglobal.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The long term impact of Daylight Saving Time regulations in daily life\n  at several circles of latitude: We analyze large scale (N~10000) time use surveys in United States, Spain,\nItaly, France and Great Britain to ascertain seasonal variations in the\nsleep/wake cycle and the labor cycle after daylight saving time -- summer time\narrangements -- regulations have stood up for at least forty years. That is,\nnot the usual search for short-term effects of the biannual transitions of DST\nbut a search for how industrialized societies have responded to DST\nregulations. Results show that the labor cycle is equally distributed through\nseasons, an everyday experience which is a major outcome of DST. The sleep/wake\ncycle displays disturbance punctuated by solar events: sunrise, sunset and\nnoon. Sleep onset in non-employees in a week-end -- the group more prone to\nfree choices -- delays in summer opposing to the regulation. Yet, sleep offset\nin this group advances in summer, despite clock time already advanced in the\nspring transition. The best explanation for these findings is that size and\ndirectionality of DST regulations are close to an optimum. The role of latitude\nis discussed after sleep/wake cycle in Spain, Italy and United States exhibit\nfewer statistically significant excursions that the sleep/wake cycle in Great\nBritain and France, which could be indicating preference for time\nsynchronization as latitude increases. The consequences of a seasonal\nregulation of clocks vs the choice of permanent summer time or permanent winter\ntime is sketched from a previous report on human activity.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A network-based microfoundation of Granovetter's threshold model for\n  social tipping: Social tipping, where minorities trigger larger populations to engage in\ncollective action, has been suggested as one key aspect in addressing\ncontemporary global challenges. Here, we refine Granovetter's widely\nacknowledged theoretical threshold model of collective behavior as a numerical\nmodelling tool for understanding social tipping processes and resolve issues\nthat so far have hindered such applications. Based on real-world observations\nand social movement theory, we group the population into certain or potential\nactors, such that -- in contrast to its original formulation -- the model\npredicts non-trivial final shares of acting individuals. Then, we use a network\ncascade model to explain and analytically derive that previously hypothesized\nbroad threshold distributions emerge if individuals become active via social\ninteraction. Thus, through intuitive parameters and low dimensionality our\nrefined model is adaptable to explain the likelihood of engaging in collective\nbehavior where social tipping like processes emerge as saddle-node bifurcations\nand hysteresis.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Improving the accuracy of the k-shell method by removing redundant\n  links-from a perspective of spreading dynamics: Recent study shows that the accuracy of the k-shell method in determining\nnode coreness in a spreading process is largely impacted due to the existence\nof core-like group, which has a large k-shell index but a low spreading\nefficiency. Based on analysis of the structure of core-like groups in\nreal-world networks, we discover that nodes in the core-like group are mutually\ndensely connected with very few out-leaving links from the group. By defining a\nmeasure of diffusion importance for each edge based on the number of\nout-leaving links of its both ends, we are able to identify redundant links in\nthe spreading process, which have a relatively low diffusion importance but\nlead to form the locally densely connected core-like group. After filtering out\nthe redundant links and applying the k-shell method to the residual network, we\nobtain a renewed coreness for each node which is a more accurate index to\nindicate its location importance and spreading influence in the original\nnetwork. Moreover, we find that the performance of the ranking algorithms based\non the renewed coreness are also greatly enhanced. Our findings help to more\naccurately decompose the network core structure and identify influential nodes\nin spreading processes.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Temporal motifs reveal homophily, gender-specific patterns and group\n  talk in mobile communication networks: Electronic communication records provide detailed information about temporal\naspects of human interaction. Previous studies have shown that individuals'\ncommunication patterns have complex temporal structure, and that this structure\nhas system-wide effects. In this paper we use mobile phone records to show that\ninteraction patterns involving multiple individuals have non-trivial temporal\nstructure that cannot be deduced from a network presentation where only\ninteraction frequencies are taken into account. We apply a recently introduced\nmethod, temporal motifs, to identify interaction patterns in a temporal network\nwhere nodes have additional attributes such as gender and age. We then develop\na null model that allows identifying differences between various types of nodes\nso that these differences are independent of the network based on interaction\nfrequencies. We find gender-related differences in communication patters, and\nshow the existence of temporal homophily, the tendency of similar individuals\nto participate in interaction patterns beyond what would be expected on the\nbasis of the network structure alone. We also show that temporal patterns\ndiffer between dense and sparse parts of the network. Because this result is\nindependent of edge weights, it can be considered as an extension of\nGranovetter's hypothesis to temporal networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Women in academia: a warning on selection bias in gender studies from\n  the astronomical perspective: The recent paper by AlShebli et al. (2020) investigates the impact of\nmentorship in young scientists. Among their conclusions, they state that female\nprot\\'eg\\'es benefit more from male than female mentorship. We herein expose a\ncritical flaw in their methodological design that is a common issue in\nAstronomy, namely \"selection biases\". An effect that if not treated properly\nmay lead to unwarranted causality claims. In their analysis, selection biases\nseem to be present in the response rate of their survey (8.35%), the choice of\ndatabase, success criterion, and the overlook of the numerous drawbacks female\nresearchers face in academia. We discuss these issues and their implications --\none of them being the potential increase in obstacles for women in academia.\nFinally, we reinforce the dangers of not considering selection bias effects in\nstudies aimed at retrieving causal relations.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Dynamics of conflicts in Wikipedia: In this work we study the dynamical features of editorial wars in Wikipedia\n(WP). Based on our previously established algorithm, we build up samples of\ncontroversial and peaceful articles and analyze the temporal characteristics of\nthe activity in these samples. On short time scales, we show that there is a\nclear correspondence between conflict and burstiness of activity patterns, and\nthat memory effects play an important role in controversies. On long time\nscales, we identify three distinct developmental patterns for the overall\nbehavior of the articles. We are able to distinguish cases eventually leading\nto consensus from those cases where a compromise is far from achievable.\nFinally, we analyze discussion networks and conclude that edit wars are mainly\nfought by few editors only.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Inversion method for content-based networks: In this paper, we generalize a recently introduced Expectation Maximization\n(EM) method for graphs and apply it to content-based networks. The EM method\nprovides a classification of the nodes of a graph, and allows to infer\nrelations between the different classes. Content-based networks are ideal\nmodels for graphs displaying any kind of community or/and multipartite\nstructure. We show both numerically and analytically that the generalized EM\nmethod is able to recover the process that led to the generation of such\nnetworks. We also investigate the conditions under which our generalized EM\nmethod can recover the underlying contents-based structure in the presence of\nrandomness in the connections. Two entropies, Sq and Sc, are defined to measure\nthe quality of the node classification and to what extent the connectivity of a\ngiven network is content-based. Sq and Sc are also useful in determining the\nnumber of classes for which the classification is optimal.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Spatial Correlation Analysis of Traffic Flow on Parallel Motorways in\n  Germany: With the widely used method of correlation matrix analysis, this study\nreveals the change of traffic states on parallel motorways in North\nRhine-Westphalia, Germany. In terms of the time series of traffic flow and\nvelocity, we carry out a quantitative analysis in correlations and reveal a\nhigh level of strongly positive traffic flow correlation and rich structural\nfeatures in the corresponding correlation matrices. The strong correlation is\nmainly ascribed to the daily time evolution of traffic flow during the periods\nof rush hours and non-rush hours. In terms of free flow and congestion, the\nstructural features are able to capture the average traffic situation we derive\nfrom our data. Furthermore, the structural features in correlation matrices for\nindividual time periods corroborate our results from the correlation matrices\nregarding a whole day. The average correlations in traffic flows and velocities\nover all pairwise sections disclose the traffic behavior during each individual\ntime period. Our contribution uncovers the potential application of correlation\nanalysis on the study of traffic networks as a complex system.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Asymmetric simple exclusion process describing conflicting traffic flows: We use the asymmetric simple exclusion process for describing vehicular\ntraffic flow at the intersection of two streets. No traffic lights control the\ntraffic flow. The approaching cars to the intersection point yield to each\nother to avoid collision. This yielding dynamics is model by implementing\nexclusion process to the intersection point of the two streets. Closed boundary\ncondition is applied to the streets. We utilize both mean-field approach and\nextensive simulations to find the model characteristics. In particular, we\nobtain the fundamental diagrams and show that the effect of interaction between\nchains can be regarded as a dynamic impurity at the intersection point.",
        "category": "physics_soc-ph"
    },
    {
        "text": "MATCASC: A tool to analyse cascading line outages in power grids: Blackouts in power grids typically result from cascading failures. The key\nimportance of the electric power grid to society encourages further research\ninto sustaining power system reliability and developing new methods to manage\nthe risks of cascading blackouts. Adequate software tools are required to\nbetter analyze, understand, and assess the consequences of the cascading\nfailures. This paper presents MATCASC, an open source MATLAB based tool to\nanalyse cascading failures in power grids. Cascading effects due to line\noverload outages are considered. The applicability of the MATCASC tool is\ndemonstrated by assessing the robustness of IEEE test systems and real-world\npower grids with respect to cascading failures.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Google as God? Opportunities and Risks of the Information Age: If God did not exist - people would invent one. The development of human\ncivilization requires mechanisms promoting cooperation and social order. One of\nthese mechanisms is based on the idea that everything we do is seen and judged\nby God - bad deeds will be punished, while good ones will be rewarded. The\nInformation Age has now fueled the dream that God-like omniscience and\nomnipotence can be created by man.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Ranking in evolving complex networks: Complex networks have emerged as a simple yet powerful framework to represent\nand analyze a wide range of complex systems. The problem of ranking the nodes\nand the edges in complex networks is critical for a broad range of real-world\nproblems because it affects how we access online information and products, how\nsuccess and talent are evaluated in human activities, and how scarce resources\nare allocated by companies and policymakers, among others. This calls for a\ndeep understanding of how existing ranking algorithms perform, and which are\ntheir possible biases that may impair their effectiveness. Well-established\nranking algorithms (such as the popular Google's PageRank) are static in nature\nand, as a consequence, they exhibit important shortcomings when applied to real\nnetworks that rapidly evolve in time. The recent advances in the understanding\nand modeling of evolving networks have enabled the development of a wide and\ndiverse range of ranking algorithms that take the temporal dimension into\naccount. The aim of this review is to survey the existing ranking algorithms,\nboth static and time-aware, and their applications to evolving networks. We\nemphasize both the impact of network evolution on well-established static\nalgorithms and the benefits from including the temporal dimension for tasks\nsuch as prediction of real network traffic, prediction of future links, and\nidentification of highly-significant nodes.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Timing uncertainty in collective risk dilemmas encourages group\n  reciprocation and polarization: Human social dilemmas are often shaped by actions involving uncertain goals\nand returns that may only be achieved in the future. Climate action, voluntary\nvaccination and other prospective choices stand as paramount examples of this\nsetting. In this context, as well as in many other social dilemmas, uncertainty\nmay produce non-trivial effects. Whereas uncertainty about collective targets\nand their impact were shown to negatively affect group coordination and\nsuccess, no information is available about timing uncertainty, i.e. how\nuncertainty about when the target needs to be reached affects the outcome as\nwell as the decision-making. Here we show experimentally, through a collective\ndilemma wherein groups of participants need to avoid a tipping point under the\nrisk of collective loss, that timing uncertainty prompts not only early\ngenerosity but also polarized contributions, in which participants' total\ncontributions are distributed more unfairly than when there is no uncertainty.\nAnalyzing participant behavior reveals, under uncertainty, an increase in\nreciprocal strategies wherein contributions are conditional on the previous\ndonations of the other participants, a group analogue of the well-known\nTit-for-Tat strategy. Although large timing uncertainty appears to reduce\ncollective success, groups that successfully collect the required amount show\nstrong reciprocal coordination. This conclusion is supported by a game\ntheoretic model examining the dominance of behaviors in case of timing\nuncertainty. In general, timing uncertainty casts a shadow on the future that\nleads participants to respond early, encouraging reciprocal behaviors, and\nunequal contributions.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Inefficiency of voting in Parrondo games: We study a modification of the so-called Parrondo's paradox where a large\nnumber of individuals choose the game they want to play by voting. We show that\nit can be better for the players to vote randomly than to vote according to\ntheir own benefit in one turn. The former yields a winning tendency while the\nlatter results in steady losses. An explanation of this behaviour is given by\nnoting that selfish voting prevents the switching between games that is\nessential for the total capital to grow. Results for both finite and infinite\nnumber of players are presented. It is shown that the extension of the model to\nthe history-dependent Parrondo's paradox also displays the same effect.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A new model for competition between many languages: Time evolutions of number of cities, population of cities, world population,\nand size distribution of present languages are studied in terms of a new model,\nwhere population of each city increases by a random rate and decreases by a\nrandom division. World population and size distribution of languages come out\nin good agreement with the available empirical data.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Utility Rate Equations of Group Population Dynamics in Biological and\n  Social Systems: We present a novel system of equations to describe the evolution of\nself-organized structured societies (biological or human) composed of several\ntrait groups. The suggested approach is based on the combination of ideas\nemployed in the theory of biological populations, system theory, and utility\ntheory. The evolution equations are defined as utility rate equations, whose\nparameters are characterized by the utility of each group with respect to the\nsociety as a whole and by the mutual utilities of groups with respect to each\nother. We analyze in detail the cases of two groups (cooperators and defectors)\nand of three groups (cooperators, defectors, and regulators) and find that, in\na self-organized society, neither defectors nor regulators can overpass the\nmaximal fractions of about 10% each. This is in agreement with the data for bee\nand ant colonies. The classification of societies by their distance from\nequilibrium is proposed. We apply the formalism to rank the countries according\nto the introduced metric quantifying their relative stability, which depends on\nthe cost of defectors and regulators as well as their respective population\nfractions. We find a remarkable concordance with more standard economic ranking\nbased, for instance, on GDP per capita.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Fluid-Based Analysis of Pedestrian Crowd at Bottlenecks: In emergency egress crowd behavior critically affects egress efficiency and\npublic safety. By integrating psychological principles to Newtonian motion of\ncrowd, a fluid-based equation is derived in this paper to explore how energy in\ndifferent forms is balanced when pedestrian crowd pass through a bottleneck.\nSuch fluid-based analysis helps to bridge a gap among psychological findings,\npedestrian models and simulation results, and it further provides a new\nperspective to understand how the faster-is-slower effect is caused and how\ndisastrous events (e.g., jamming and stampede) occur at a bottleneck passage.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Modelling railway delay propagation as diffusion-like spreading: Railway systems form an important means of transport across the world.\nHowever, congestions or disruptions may significantly decrease these systems'\nefficiencies, making predicting and understanding the resulting train delays a\npriority for railway organisations. Delays are studied in a wide variety of\nmodels, which usually simulate trains as discrete agents carrying delays. In\ncontrast, in this paper, we define a novel model for studying delays, where\nthey spread across the railway network via a diffusion-like process. This type\nof modelling has various advantages such as quick computation and ease of\napplying various statistical tools like spectral methods, but it also comes\nwith limitations related to the directional and discrete nature of delays and\nthe trains carrying them. We apply the model to the Belgian railways and study\nits performance in simulating the delay propagation in severely disrupted\nrailway situations. In particular, we discuss the role of spatial aggregation\nby proposing to cluster the Belgian railway system into sets of stations and\nadapt the model accordingly. We find that such aggregation significantly\nincreases the model's performance. For some particular situations, a\nnon-trivial optimal level of spatial resolution is found on which the model\nperforms best. Our results show the potential of this type of delay modelling\nto understand large-scale properties of railway systems.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Spatial Interaction Modelling of Cross-Region R&D Collaborations\n  Empirical Evidence from the EU Framework Programmes: The focus of this study is on cross-region R&D collaboration networks in the\nEU Framework Programmes (FP's). In contrast to most other empirical studies in\nthis field, we shift attention to regions as units of analysis, i.e. we use\naggregated data on research collaborations at the regional level. The objective\nis to identify determinants of cross-region collaboration patterns. In\nparticular, we are interested whether geographical and technological distances\nare significant determinants of interregional cooperation. Further we\ninvestigate differences between intra-industry networks and public research\nnetworks (i.e. universities and research organisations). The European coverage\nis achieved by using data on 255 NUTS-2 regions of the 25 pre-2007 EU\nmember-states, as well as Norway and Switzerland. We adopt a Poisson spatial\ninteraction modelling perspective to analyse these questions. The dependent\nvariable is the intensity of collaborative interactions between two regions,\nthe independent variables are region-specific characteristics and variables\nthat measure the separation between two regions such as geographical or\ntechnological distance. The results provide striking evidence that geographical\nfactors are important determinants of cross-region collaboration intensities,\nbut the effect of technological proximity is stronger. R&D collaborations occur\nmost often between organisations that are located close to each other in\ntechnological space. Moreover geographical distance effects are significantly\nhigher for intra-industry than for public research collaborations.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Coevolutionary Dynamics with Global Fields: We investigate the effects of external and autonomous global interaction\nfields on an adaptive network of social agents with an opinion formation\ndynamics based on a simple imitation rule. We study the competition between\nglobal fields and adaptive rewiring on the space of parameters of the system.\nThe model represents an adaptive society subject to global mass media such as a\ndirected opinion influence or feedback of endogenous cultural trends. We show\nthat, in both situations, global mass media contribute to consensus and to\nprevent the fragmentation of the social network induced by the coevolutionary\ndynamics. We present a discussion of these results in the context of dynamical\nsystems and opinion formation dynamics.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A model for the competition between political mono-polarization and\n  bi-polarization: We investigate the phenomena of political bi-polarization in a population of\ninteracting agents by means of a generalized version of the model introduced in\nPRE E 101, 012101 (2020) for the dynamics of voting intention. Each agent has a\npropensity $p$ in $[0,1]$ to vote for one of two political candidates. In an\niteration step, two agents $i$ and $j$ with respective propensities $p_i$ and\n$p_j$ interact, and then $p_i$ either increases by an amount $h>0$ with a\nprobability that is a nonlinear function of $p_i$ and $p_j$ or decreases by $h$\nwith the complementary probability. We study the behavior of the system under\nvariations of a parameter $q \\ge 0$ that measures the nonlinearity of the\npropensity update rule. We focus on the stability properties of the two\ndistinct stationary states: mono-polarization in which all agents share the\nsame extreme propensity ($0$ or $1$), and bi-polarization where the population\nis divided into two groups with opposite and extreme propensities. We find that\nthe bi-polarized state is stable for $q<q_c$, while the mono-polarized state is\nstable for $q>q_c$, where $q_c$ is a transition value that decreases as $h$\ndecreases. We develop a rate equation approach whose stability analysis reveals\nthat $q_c$ vanishes when $h$ becomes infinitesimally small. This result is\nsupported by the analysis of a transport equation derived in the continuum $h\n\\to 0$ limit. We also show by Monte Carlo simulations that the mean time $\\tau$\nto reach mono-polarization in a system of size $N$ scales as $\\tau \\sim\nN^{\\alpha}$ at $q_c$ , where $\\alpha(h)$ is a non-universal exponent.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Interplay between $k$-core and community structure in complex networks: The organisation of a network in a maximal set of nodes having at least $k$\nneighbours within the set, known as $k$-core decomposition, has been used for\nstudying various phenomena. It has been shown that nodes in the innermost\n$k$-shells play a crucial role in contagion processes, emergence of consensus,\nand resilience of the system. It is known that the $k$-core decomposition of\nmany empirical networks cannot be explained by the degree of each node alone,\nor equivalently, random graph models that preserve the degree of each node\n(i.e., configuration model). Here we study the $k$-core decomposition of some\nempirical networks as well as that of some randomised counterparts, and examine\nthe extent to which the $k$-shell structure of the networks can be accounted\nfor by the community structure. We find that preserving the community structure\nin the randomisation process is crucial for generating networks whose $k$-core\ndecomposition is close to the empirical one. We also highlight the existence,\nin some networks, of a concentration of the nodes in the innermost $k$-shells\ninto a small number of communities.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A Markovian influence graph formed from utility line outage data to\n  mitigate large cascades: We use observed transmission line outage data to make a Markov influence\ngraph that describes the probabilities of transitions between generations of\ncascading line outages, where each generation of a cascade consists of a single\nline outage or multiple line outages. The new influence graph defines a Markov\nchain and generalizes previous influence graphs by including multiple line\noutages as Markov chain states. The generalized influence graph can reproduce\nthe distribution of cascade size in the utility data. In particular, it can\nestimate the probabilities of small, medium and large cascades. The influence\ngraph has the key advantage of allowing the effect of mitigations to be\nanalyzed and readily tested, which is not available from the observed data. We\nexploit the asymptotic properties of the Markov chain to find the lines most\ninvolved in large cascades and show how upgrades to these critical lines can\nreduce the probability of large cascades.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Structure and Evolution of the World Trade Network: The \\emph{World Trade Web} (WTW), the network defined by the international\nimport/export trade relationships, has been recently shown to display some\nimportant topological properties which are tightly related to the Gross\nDomestic Product of world countries. While our previous analysis focused on the\nstatic, undirected version of the WTW, here we address its full evolving,\ndirected description. This is accomplished by exploiting the peculiar\nreciprocity structure of the WTW to recover the directed nature of\ninternational trade channels, and by studying the temporal dependence of the\nparameters describing the WTW topology.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Will Scale-free Popularity Develop Scale-free Geo-social Networks?: Empirical results show that spatial factors such as distance, population\ndensity and communication range affect our social activities, also reflected by\nthe development of ties in social networks. This motivates the need for social\nnetwork models that take these spatial factors into account. Therefore, in this\npaper we propose a gravity-low-based geo-social network model, where\nconnections develop according to the popularity of the individuals, but are\nconstrained through their geographic distance and the surrounding population\ndensity. Specifically, we consider a power-law distributed popularity, and\nrandom node positions governed by a Poisson point process. We evaluate the\ncharacteristics of the emerging networks, considering the degree distribution,\nthe average degree of neighbors and the local clustering coefficient. These\nlocal metrics reflect the robustness of the network, the information\ndissemination speed and the communication locality. We show that unless the\ncommunication range is strictly limited, the emerging networks are scale-free,\nwith a rank exponent affected by the spatial factors. Even the average neighbor\ndegree and the local clustering coefficient show tendencies known in\nnon-geographic scale-free networks, at least when considering individuals with\nlow popularity. At high-popularity values, however, the spatial constraints\nlead to popularity-independent average neighbor degrees and clustering\ncoefficients.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A deterministic mathematical model for the spread of two rumors: In this paper we propose a deterministic mathematical model that attempts to\nexplain the propagation of a rumor using SIRS type epidemiological models with\ntemporary immunity and nonlinear incidence rate. In particular, we speculate\nabout the dissemination of information when the so-called \"complex networks\"\nare used. The effect of introducing a second rumor, inspired by a vaccination\nmodel, in the same population of individuals, which will try to counteract the\neffect of the original rumor, is studied. That is a situation that occurs\nfrequently in communities, when a rumor is counteracted by a contrary\ninformation or news, which behaves in the same way as a rumor. Furthermore,\nqualitative analysis and numerical experimentation of the dynamic model are\nperformed. We corroborate that the dynamics of spreading rumors show similar\nbehavior to that found in the dynamics of an infectious disease.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Deep decarbonization and the Supergrid, prospects for electricity\n  transmission between Europe and China: Long distance transmission within continents has been shown to be one of the\nmost important variation management strategies in renewable energy systems,\nwhere allowing for transmission expansion will reduce system cost by around\n20%. In this paper, we test whether the system cost further decreases when\ntransmission is extended to intercontinental connections. We analyze a Eurasian\ninterconnection between China, Mid-Asia and Europe, using a capacity expansion\nmodel with hourly time resolution. The model is constrained by an increasingly\ntighter global cap on CO2 emissions in order to investigate the effect of\ndifferent levels of reliance on variable sources. Our results show that a\nsupergrid option decreases total system cost by a maximum of 5%, compared to\ncontinental grid integration. This maximum effect is achieved when (i) the\ngeneration is constrained to be made up almost entirely by renewables, (ii) the\nland available for VRE farms is relatively limited and the demand is relatively\nhigh and (iii) the cost for solar PV and storage is high. The importance of\nthese two factors is explained by that a super grid allows for harnessing of\nremote wind-, solar- and hydro resources as well as management of variations,\nboth of which are consequential only in cases where dispatchable resources are\nlimited or very costly. As for the importance of the cost for storage, it\nrepresents a competing variation management option, and when it has low cost,\nit substitutes part of the role of the supergrid, which is to manage variations\nthrough long-distance trade. The cost decrease from a Eurasian supergrid was\nfound to be between 0% and 5%, compared to the cost in the case of\ncontinental-scale grids. We conclude that the benefits of a supergrid from a\ntechno-economic perspective are in most cases negligible, or modest at best.17\npages, f",
        "category": "physics_soc-ph"
    },
    {
        "text": "Systemic Interbank Network Risks in Russia: Modelling of contagion in interbank networks is discussed. A model taking\ninto account bow-tie structure and dissasortativity of interbank networks is\ndeveloped. The model is shown to provide a good quantitative description of the\nRussian interbank market. Detailed arguments favoring the non-percolative\nnature of contagion-related risks in the Russian interbank market are given.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Modeling collective human mobility: Understanding exponential law of\n  intra-urban movement: It is very important to understand urban mobility patterns because most trips\nare concentrated in urban areas. In the paper, a new model is proposed to model\ncollective human mobility in urban areas. The model can be applied to predict\nindividual flows not only in intra-city but also in countries or a larger\nrange. Based on the model, it can be concluded that the exponential law of\ndistance distribution is attributed to decreasing exponentially of average\ndensity of human travel demands. Since the distribution of human travel demands\nonly depends on urban planning, population distribution, regional functions and\nso on, it illustrates that these inherent properties of cities are impetus to\ndrive collective human movements.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The agents' preferences and the topology of networks: In this paper, a new framework to study weighed networks is introduced. The\nidea behind this methodology is to consider that each node of the network is an\nagent that desires to satisfy his/her preferences in an economic sense.\nMoreover, the formation of a link between two agents depends on the benefits\nand costs associated to this link. Therefore, an edge between two given nodes\nwill only arise if the tradeoff between satisfaction and cost for building it\nis jointly positive. Using a computational framework, I intend to show that\ndepending on the agents combination of benefits and costs, some very well known\nnetworks can naturally arise.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Characterizing interactions in online social networks during exceptional\n  events: Nowadays, millions of people interact on a daily basis on online social media\nlike Facebook and Twitter, where they share and discuss information about a\nwide variety of topics. In this paper, we focus on a specific online social\nnetwork, Twitter, and we analyze multiple datasets each one consisting of\nindividuals' online activity before, during and after an exceptional event in\nterms of volume of the communications registered. We consider important events\nthat occurred in different arenas that range from policy to culture or science.\nFor each dataset, the users' online activities are modeled by a multilayer\nnetwork in which each layer conveys a different kind of interaction,\nspecifically: retweeting, mentioning and replying. This representation allows\nus to unveil that these distinct types of interaction produce networks with\ndifferent statistical properties, in particular concerning the degree\ndistribution and the clustering structure. These results suggests that models\nof online activity cannot discard the information carried by this multilayer\nrepresentation of the system, and should account for the different processes\ngenerated by the different kinds of interactions. Secondly, our analysis\nunveils the presence of statistical regularities among the different events,\nsuggesting that the non-trivial topological patterns that we observe may\nrepresent universal features of the social dynamics on online social networks\nduring exceptional events.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Humans make best use of social heuristics when confronting hard problems\n  in large groups: We report the results of a game-theoretic experiment with human players who\nsolve the problems of increasing complexity by cooperating in groups of\nincreasing size. Our experimental environment is set up to make it complicated\nfor players to use rational calculation for making the cooperative decisions.\nThis environment is directly translated into a computer simulation, from which\nwe extract the collaboration strategy that leads to the maximal attainable\nscore. Based on this, we measure the error that players make when estimating\nthe benefits of collaboration, and find that humans massively underestimate\nthese benefits when facing easy problems or working alone or in small groups.\nIn contrast, when confronting hard problems or collaborating in large groups,\nhumans accurately judge the best level of collaboration and easily achieve the\nmaximal score. Our findings are independent on groups' composition and players'\npersonal traits. We interpret them as varying degrees of usefulness of social\nheuristics, which seems to depend on the size of the involved group and the\ncomplexity of the situation.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The roundtable: an abstract model of conversation dynamics: Is it possible to abstract a formal mechanism originating schisms and\ngoverning the size evolution of social conversations? In this work a\nconstructive solution to such problem is proposed: an abstract model of a\ngeneric N-party turn-taking conversation. The model develops from simple yet\nrealistic assumptions derived from experimental evidence, abstracts from\nconversation content and semantics while including topological information, and\nis driven by stochastic dynamics. We find that a single mechanism - namely the\ndynamics of conversational party's individual fitness, as related to\nconversation size - controls the development of the self-organized schisming\nphenomenon. Potential generalizations of the model - including individual\ntraits and preferences, memory effects and more elaborated conversational\ntopologies - may find important applications also in other fields of research,\nwhere dynamically-interacting and networked agents play a fundamental role.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The meta book and size-dependent properties of written language: Evidence is given for a systematic text-length dependence of the power-law\nindex gamma of a single book. The estimated gamma values are consistent with a\nmonotonic decrease from 2 to 1 with increasing length of a text. A direct\nconnection to an extended Heap's law is explored. The infinite book limit is,\nas a consequence, proposed to be given by gamma = 1 instead of the value\ngamma=2 expected if the Zipf's law was ubiquitously applicable. In addition we\nexplore the idea that the systematic text-length dependence can be described by\na meta book concept, which is an abstract representation reflecting the\nword-frequency structure of a text. According to this concept the\nword-frequency distribution of a text, with a certain length written by a\nsingle author, has the same characteristics as a text of the same length pulled\nout from an imaginary complete infinite corpus written by the same author.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Block size dependence of coarse graining in discrete opinion dynamics\n  model: Application to the US presidential elections: The electoral college of voting system for the US presidential election is\nanalogous to a coarse graining procedure commonly used to study phase\ntransitions in physical systems. In a recent paper, opinion dynamics models\nmanifesting a phase transition, were shown to be able to explain the cases when\na candidate winning more number of popular votes could still lose the general\nelection on the basis of the electoral college system. We explore the\ndependence of such possibilities on various factors like the number of states\nand total population (i.e., system sizes) and get an interesting scaling\nbehavior. In comparison with the real data, it is shown that the probability of\nthe minority win, calculated within the model assumptions, is indeed near the\nhighest possible value. In addition, we also implement a two step coarse\ngraining procedure, relevant for both opinion dynamics and information theory.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Analysis of size distributions of fictional characters: from Pok\u00e9mon\n  to Godzilla: In this letter, the size (height and weight) of fictional characters in\nanimations, superhero series, movies, and other media is studied. We find that\nthe distributions of character height and weight approximately follow lognormal\ndistributions in common to five selected works. We propose a mechanism\ngoverning this lognormal behavior based on the principle of maximum entropy and\nthe Weber-Fechner law. Moreover, we provide a comparison to the size\ndistributions of real animals. Although the size distributions of fictional\ncharacters and real animals are both lognormal, the distributions are\nessentially different, particularly in the scaling between height and weight.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Role of social environment and social clustering in spread of opinions\n  in co-evolving networks: Taking a pragmatic approach to the processes involved in the phenomena of\ncollective opinion formation, we investigate two specific modifications to the\nco-evolving network voter model of opinion formation, studied by Holme and\nNewman [1]. First, we replace the rewiring probability parameter by a\ndistribution of probability of accepting or rejecting opinions between\nindividuals, accounting for the asymmetric influences in relationships among\nindividuals in a social group. Second, we modify the rewiring step by a\npath-length-based preference for rewiring that reinforces local clustering. We\nhave investigated the influences of these modifications on the outcomes of the\nsimulations of this model. We found that varying the shape of the distribution\nof probability of accepting or rejecting opinions can lead to the emergence of\ntwo qualitatively distinct final states, one having several isolated connected\ncomponents each in internal consensus leading to the existence of diverse set\nof opinions and the other having one single dominant connected component with\neach node within it having the same opinion. Furthermore, and more importantly,\nwe found that the initial clustering in network can also induce similar\ntransitions. Our investigation also brings forward that these transitions are\ngoverned by a weak and complex dependence on system size. We found that the\nnetworks in the final states of the model have rich structural properties\nincluding the small world property for some parameter regimes. [1] P. Holme and\nM. Newman, Phys. Rev. E 74, 056108 (2006).",
        "category": "physics_soc-ph"
    },
    {
        "text": "The extensive nature of group quality: We consider groups of interacting nodes engaged in an activity as many-body,\ncomplex systems and analyse their cooperative behaviour from a mean-field point\nof view. We show that inter-nodal interactions rather than accumulated\nindividual node strengths dominate the quality of group activity, and give rise\nto phenomena akin to phase transitions, where the extensive relationship\nbetween group quality and quantity reduces. The theory is tested using\nempirical data on quantity and quality of scientific research groups, for which\ncritical masses are determined.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Graph Hierarchy: A novel approach to understanding hierarchical\n  structures in complex networks: Trophic coherence, a measure of a graph's hierarchical organisation, has been\nshown to be linked to a graph's structural and dynamical aspects such as\ncyclicity, stability and normality. Trophic levels of vertices can reveal their\nfunctional properties and partition and rank the vertices accordingly. Yet\ntrophic levels and hence trophic coherence can only be defined on graphs with\nbasal vertices, vertices with zero in-degree. Consequently, trophic analysis of\ngraphs had been restricted until now. In this paper we introduce a novel\nframework, a generalisation of trophic levels, which we call hierarchical\nlevels, that can be defined on any simple graph. Within this general framework,\nwe develop additional metrics named influence centrality, a measure of a\nvertices ability to influence dynamics, and democracy coefficient, a measure of\noverall feedback in the system, both of which have implications for the\ncontrollability of complex systems. We discuss how our generalisation relates\nto previous attempts and what new insights are illuminated on the topological\nand dynamical aspects of graphs. Finally, we show how the hierarchical\nstructure of a network relates to the incidence rate in a SIS epidemic model.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Detectability of Macroscopic Structures in Directed Asymmetric\n  Stochastic Block Model: We study the problem of identifying macroscopic structures in networks,\ncharacterizing the impact of introducing link directions on the detectability\nphase transition. To this end, building on the stochastic block model, we\nconstruct a class of hardly detectable directed networks. We find closed form\nsolutions by using belief propagation method showing how the transition line\ndepends on the assortativity and the asymmetry of the network. Finally, we\nnumerically identify the existence of a hard phase for detection close to the\ntransition point.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Characterizing Curtailed and Uneconomic Renewable Power in the\n  Mid-continent Independent System Operator: As power grids incorporate increased renewable generation such as wind and\nsolar, their variability creates growing challenges for grid stability and\nefficiency. We study two facets: power the grid is unable to accept\n(curtailment), and power that is assigned zero economic value by the grid\n(negative or zero price). Collectively we term these \"stranded power\" or SP.\n  We study stranded power in the Midcontinent Independent System Operator\n(MISO), characterizing quantity and temporal structure. First, stranded power\nis available in the MISO grid 99% of the time, and often in intervals >100\nhours, with characteristic seasonal and time-of-day patterns. Average stranded\npower often exceeds 1 GW, with duty factors as high as 30%. About 30% of all\nwind generation in MISO is stranded. Examination of the top 10 individual sites\nshows stranded power can be as high as 70% duty factor and 250MW. Trends over\nthe past 3.5 years suggest stranded power is a persistent phenomenon. The study\ncharacterizes opportunities to exploit stranded power.\n  We consider using energy storage to increase the utility of stranded power.\nFor a range of power levels and uniformly-distributed storage, adding 5 hours\nof storage doubles duty factor to 30% at 4MW, but another 95 hours is required\nfor the next 15% increase. At 4MW with 50 hours of storage, only 3 of 200 sites\nreach 100% duty factor, and with 100 hours required for the next 10 sites.\nHigher power levels require 100's of hours. Storage at the top 10 sites is more\nproductive, 5 hours increases duty factor to 70% at 4MW, but further storage\nhas diminishing benefits. Studies of the amount of power served by storage show\nthat distribution to the best sites provides 2 to 3.7-fold advantages over\nuniform distribution.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Context-aware multi-head self-attentional neural network model for next\n  location prediction: Accurate activity location prediction is a crucial component of many mobility\napplications and is particularly required to develop personalized, sustainable\ntransportation systems. Despite the widespread adoption of deep learning\nmodels, next location prediction models lack a comprehensive discussion and\nintegration of mobility-related spatio-temporal contexts. Here, we utilize a\nmulti-head self-attentional (MHSA) neural network that learns location\ntransition patterns from historical location visits, their visit time and\nactivity duration, as well as their surrounding land use functions, to infer an\nindividual's next location. Specifically, we adopt point-of-interest data and\nlatent Dirichlet allocation for representing locations' land use contexts at\nmultiple spatial scales, generate embedding vectors of the spatio-temporal\nfeatures, and learn to predict the next location with an MHSA network. Through\nexperiments on two large-scale GNSS tracking datasets, we demonstrate that the\nproposed model outperforms other state-of-the-art prediction models, and reveal\nthe contribution of various spatio-temporal contexts to the model's\nperformance. Moreover, we find that the model trained on population data\nachieves higher prediction performance with fewer parameters than\nindividual-level models due to learning from collective movement patterns. We\nalso reveal mobility conducted in the recent past and one week before has the\nlargest influence on the current prediction, showing that learning from a\nsubset of the historical mobility is sufficient to obtain an accurate location\nprediction result. We believe that the proposed model is vital for\ncontext-aware mobility prediction. The gained insights will help to understand\nlocation prediction models and promote their implementation for mobility\napplications.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Covid-19 spread: Reproduction of data and prediction using a SIR model\n  on Euclidean network: We study the datafor the cumulative as well as daily number of cases in the\nCovid-19 outbreak in China. The cumulative data can be fit to an empirical form\nobtained from a Susceptible-Infected-Removed (SIR) model studied on an\nEuclidean network previously. Plotting the number of cases against the distance\nfrom the epicenter for both China and Italy, we find an approximate power law\nvariation with an exponent $\\sim 1.85$ showing strongly that the spatial\ndependence plays a key role, a factor included in the model. We report here\nthat the SIR model on the Eucledean network can reproduce with a high accuracy\nthe data for China for given parameter values, and can also predict when the\nepidemic, at least locally, can be expected to be over.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A Dynamic Model of Cascades on Random Networks with a Threshold Rule: Cascades on random networks are typically analyzed by assuming they map onto\npercolation processes and then are solved using generating function\nformulations. This approach assumes that the network is infinite and weakly\nconnected, yet furthermore approximates a dynamic cascading process as a static\npercolation event. In this paper we propose a dynamic Markov model formulation\nthat assumes a finite network with arbitrary average nodal degree. We apply it\nto the case where cascades follow a threshold rule, that is, that a node will\nchange state (\"flip\") only if a fraction, exceeding a given threshold, of its\nneighbors has changed state previously. The corresponding state transition\nmatrix, recalculated after each step, records the probability that a node of\ndegree k has i flipped neighbors after j steps in the cascade's evolution. This\ntheoretical model reproduces a number of behaviors observed in simulations but\nnot yet reported in the literature. These include the ability to predict\ncascades in a domain previously predicted to forbid cascades without assuming\nthat the network is locally tree-like, and, due to the dynamic nature of the\nmodel, a \"near death\" behavior in which cascades initially appear about to die\nbut later explode. Cascades in the \"no cascades\" region require a sufficiently\nlarge seed of initially flipped nodes whose size scales with the size of the\nnetwork or else the cascade will die out. Our theory also predicts the well\nknown properties of cascades, for instance that a single node seed can start a\nglobal cascade in the appropriate regime regardless of the (finite) size of the\nnetwork. The theory and simulations developed here are compared with a\nfoundational paper by Watts which used generating function theory.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Multi-topic belief formation through bifurcations over signed social\n  networks: We propose and analyze a nonlinear dynamic model of continuous-time\nmulti-dimensional belief formation over signed social networks. Our model\naccounts for the effects of a structured belief system, self-appraisal,\ninternal biases, and various sources of cognitive dissonance posited by recent\ntheories in social psychology. We prove that strong beliefs emerge on the\nnetwork as a consequence of a bifurcation. We analyze how the balance of social\nnetwork effects in the model controls the nature of the bifurcation and,\ntherefore, the belief-forming limit-set solutions. Our analysis provides\nconstructive conditions on how multi-stable network belief equilibria and\nbelief oscillations emerging at a belief-forming bifurcation depend on the\ncommunication network graph and belief system network graph. Our model and\nanalysis provide new theoretical insights on the dynamics of social systems and\na new principled framework for designing decentralized decision-making on\nengineered networks in the presence of structured relationships among\nalternatives.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A Model for Innovation Diffusion with Intergroup Suppression: We present a new model for the diffusion of innovation. Here, the population\nis segmented into distinct groups. Adoption by a particular group of some\ncultural product may be inhibited both by large numbers of its own members\nalready having adopted but also, in particular, by members of another group\nhaving adopted. Intergroup migration is also permitted. We determine the\nequilibrium points and carry out stability analysis for the model for a\ntwo-group population. We also simulate a discrete time version of the model.\nLastly, we present data on tablet use in eight countries from 2012-2016 and\nshow that the relationship between use in the \"under 25\" age group and \"55+\"\nage group conforms to the model.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Numerical assessment of the percolation threshold using complement\n  networks: Models of percolation processes on networks currently assume locally\ntree-like structures at low densities, and are derived exactly only in the\nthermodynamic limit. Finite size effects and the presence of short loops in\nreal systems however cause a deviation between the empirical percolation\nthreshold $p_c$ and its model-predicted value $\\pi_c$. Here we show the\nexistence of an empirical linear relation between $p_c$ and $\\pi_c$ across a\nlarge number of real and model networks. Such a putatively universal relation\ncan then be used to correct the estimated value of $\\pi_c$. We further show how\nto obtain a more precise relation using the concept of the complement graph, by\ninvestigating on the connection between the percolation threshold of a network,\n$p_c$, and that of its complement, $\\bar{p}_c$.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Expertise revisited I: Interactional Expertise: The notion of interactional expertise is explained starting with its origins\nand discussing its many applications. Interactional expertise is the ability to\nunderstand a technical area purely be deeply immersed in its\n'practice-language' without actually practising. One of its many applications\nis to explain how large sciences are managed.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Cooperation in public goods games: stay, but not for too long: Cooperation in repeated public goods game is hardly achieved, unless\ncontingent behavior is present. Surely, if mechanisms promoting positive\nassortment between cooperators are present, then cooperators may beat\ndefectors, because cooperators would collect greater payoffs. In the context of\nevolutionary game theory, individuals that always cooperate cannot win the\ncompetition against defectors in well-mixed populations. Here, we study the\nevolution of a population where fitness is obtained in repeated public goods\ngames and players have a fixed probability of playing the next round. As a\nresult, the group size decreases during the game. The population is well-mixed\nand there are only two available strategies: always cooperate (ALLC) or always\ndefect (ALLD). Through numerical calculation and analytical approximations we\nshow that cooperation can emerge if the players stay playing the game, but not\nfor too long. The essential mechanism is the interaction between the transition\nfrom strong to weak altruism, as the group size decreases, and the existence of\nan upper limit to the number of rounds representing limited time availability.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Reciprocal relationships in collective flights of homing pigeons: Collective motion of bird flocks can be explained via the hypothesis of many\nwrongs, and/or, a structured leadership mechanism. In pigeons, previous studies\nhave shown that there is a well-defined hierarchical structure and certain\nspecific individuals occupy more dominant positions --- suggesting that\nleadership by the few individuals drives the behavior of the collective.\nConversely, by analyzing the same data-sets, we uncover a more egalitarian\nmechanism. We show that both reciprocal relationships and a stratified\nhierarchical leadership are important and necessary in the collective movements\nof pigeon flocks. Rather than birds adopting either exclusive averaging or\nleadership strategies, our experimental results show that it is an integrated\ncombination of both compromise and leadership which drives the group's movement\ndecisions.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Planetary-Scale Views on an Instant-Messaging Network: We present a study of anonymized data capturing a month of high-level\ncommunication activities within the whole of the Microsoft Messenger\ninstant-messaging system. We examine characteristics and patterns that emerge\nfrom the collective dynamics of large numbers of people, rather than the\nactions and characteristics of individuals. The dataset contains summary\nproperties of 30 billion conversations among 240 million people. From the data,\nwe construct a communication graph with 180 million nodes and 1.3 billion\nundirected edges, creating the largest social network constructed and analyzed\nto date. We report on multiple aspects of the dataset and synthesized graph. We\nfind that the graph is well-connected and robust to node removal. We\ninvestigate on a planetary-scale the oft-cited report that people are separated\nby ``six degrees of separation'' and find that the average path length among\nMessenger users is 6.6. We also find that people tend to communicate more with\neach other when they have similar age, language, and location, and that\ncross-gender conversations are both more frequent and of longer duration than\nconversations with the same gender.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Bias, Belief and Consensus: Collective opinion formation on fluctuating\n  networks: With the advent of online networks, societies are substantially more\nconnected with individual members able to easily modify and maintain their own\nsocial links. Here, we show that active network maintenance exposes agents to\nconfirmation bias, the tendency to confirm one's beliefs, and we explore how\nthis affects collective opinion formation. We introduce a model of binary\nopinion dynamics on a complex network with fast, stochastic rewiring and show\nthat confirmation bias induces a segregation of individuals with different\nopinions. We use the dynamics of global opinion to generally categorize opinion\nupdate rules and find that confirmation bias always stabilizes the consensus\nstate. Finally, we show that the time to reach consensus has a non-monotonic\ndependence on the magnitude of the bias, suggesting a novel avenue for\nlarge-scale opinion engineering.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Stability and evolution of synonyms and homonyms in signaling game: Synonyms and homonyms appear in all natural languages. We analyse their\nevolution within the framework of the signaling game. Agents in our model use\nreinforcement learning, where probabilities of selection of a communicated word\nor of its interpretation depend on weights equal to the number of accumulated\nsuccessful communications. When the probabilities increase linearly with\nweights, synonyms appear to be very stable and homonyms decline relatively\nfast. Such behaviour seems to be at odds with linguistic observations. A better\nagreement is obtained when probabilities increase faster than linearly with\nweights. Our results may suggest that a certain positive feedback, the\nso-called Metcalfe's Law, possibly drives some linguistic processes. Evolution\nof synonyms and homonyms in our model can be approximately described using a\ncertain nonlinear urn model.",
        "category": "physics_soc-ph"
    },
    {
        "text": "An analytical model of active inference in the Iterated Prisoner's\n  Dilemma: This paper addresses a mathematically tractable model of the Prisoner's\nDilemma using the framework of active inference. In this work, we design pairs\nof Bayesian agents that are tracking the joint game state of their and their\nopponent's choices in an Iterated Prisoner's Dilemma game. The specification of\nthe agents' belief architecture in the form of a partially-observed Markov\ndecision process allows careful and rigourous investigation into the dynamics\nof two-player gameplay, including the derivation of optimal conditions for\nphase transitions that are required to achieve certain game-theoretic steady\nstates. We show that the critical time points governing the phase transition\nare linearly related to each other as a function of learning rate and the\nreward function. We then investigate the patterns that emerge when varying the\nagents' learning rates, as well as the relationship between the stochastic and\ndeterministic solutions to the two-agent system.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Evolutionary advantages of adaptive rewarding: Our wellbeing depends as much on our personal success, as it does on the\nsuccess of our society. The realization of this fact makes cooperation a very\nmuch needed trait. Experiments have shown that rewards can elevate our\nreadiness to cooperate, but since giving a reward inevitably entails paying a\ncost for it, the emergence and stability of such behavior remain elusive. Here\nwe show that allowing for the act of rewarding to self-organize in dependence\non the success of cooperation creates several evolutionary advantages that\ninstill new ways through which collaborative efforts are promoted. Ranging from\nindirect territorial battle to the spontaneous emergence and destruction of\ncoexistence, phase diagrams and the underlying spatial patterns reveal\nfascinatingly reach social dynamics that explains why this costly behavior has\nevolved and persevered. Comparisons with adaptive punishment, however, uncover\nan Achilles heel of adaptive rewarding that is due to over-aggression, which in\nturn hinders optimal utilization of network reciprocity. This may explain why,\ndespite of its success, rewarding is not as firmly weaved into our societal\norganization as punishment.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Multifractal temporally weighted detrended partial cross-correlation\n  analysis to quantify intrinsic power-law cross-correlation of two\n  non-stationary time series affected by common external factors: When common factors strongly influence two cross-correlated time series\nrecorded in complex natural and social systems, the results will be biased if\nwe use multifractal detrended cross-correlation analysis (MF-DXA) without\nconsidering these common factors. Based on multifractal temporally weighted\ndetrended cross-correlation analysis (MF-TWXDFA) proposed by our group and\nmultifractal partial cross-correlation analysis (MF-DPXA) proposed by Qian et\nal., we propose a new method---multifractal temporally weighted detrended\npartial cross-correlation analysis (MF-TWDPCCA) to quantify intrinsic power-law\ncross-correlation of two non-stationary time series affected by common external\nfactors in this paper. We use MF-TWDPCCA to characterize the intrinsic\ncross-correlations between the two simultaneously recorded time series by\nremoving the effects of other potential time series. To test the performance of\nMF-TWDPCCA, we apply it, MF-TWXDFA and MF-DPXA on simulated series. Numerical\ntests on artificially simulated series demonstrate that MF-TWDPCCA can\naccurately detect the intrinsic cross-correlations for two simultaneously\nrecorded series. To further show the utility of MF-TWDPCCA, we apply it on time\nseries from stock markets and find that there exists significantly multifractal\npower-law cross-correlation between stock returns. A new partial\ncross-correlation coefficient is defined to quantify the level of intrinsic\ncross-correlation between two time series.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Searching for superspreaders of information in real-world social media: A number of predictors have been suggested to detect the most influential\nspreaders of information in online social media across various domains such as\nTwitter or Facebook. In particular, degree, PageRank, k-core and other\ncentralities have been adopted to rank the spreading capability of users in\ninformation dissemination media. So far, validation of the proposed predictors\nhas been done by simulating the spreading dynamics rather than following real\ninformation flow in social networks. Consequently, only model-dependent\ncontradictory results have been achieved so far for the best predictor. Here,\nwe address this issue directly. We search for influential spreaders by\nfollowing the real spreading dynamics in a wide range of networks. We find that\nthe widely-used degree and PageRank fail in ranking users' influence. We find\nthat the best spreaders are consistently located in the k-core across\ndissimilar social platforms such as Twitter, Facebook, Livejournal and\nscientific publishing in the American Physical Society. Furthermore, when the\ncomplete global network structure is unavailable, we find that the sum of the\nnearest neighbors' degree is a reliable local proxy for user's influence. Our\nanalysis provides practical instructions for optimal design of strategies for\n\"viral\" information dissemination in relevant applications.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Consensus formation on adaptive networks: The structure of a network can significantly influence the properties of the\ndynamical processes which take place on them. While many studies have been\ndevoted to this influence, much less attention has been devoted to the\ninterplay and feedback mechanisms between dynamical processes and network\ntopology on adaptive networks. Adaptive rewiring of links can happen in real\nlife systems such as acquaintance networks where people are more likely to\nmaintain a social connection if their views and values are similar. In our\nstudy, we consider different variants of a model for consensus formation. Our\ninvestigations reveal that the adaptation of the network topology fosters\ncluster formation by enhancing communication between agents of similar opinion,\nthough it also promotes the division of these clusters. The temporal behavior\nis also strongly affected by adaptivity: while, on static networks, it is\ninfluenced by percolation properties, on adaptive networks, both the early and\nlate time evolution of the system are determined by the rewiring process. The\ninvestigation of a variant of the model reveals that the scenarios of\ntransitions between consensus and polarized states are more robust on adaptive\nnetworks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Modeling Transmission and Radiation Effects when Exploiting Power Line\n  Networks for Communication: Power distribution grids are exploited by Power Line Communication (PLC)\ntechnology to convey high frequency data signals. The natural conformation of\nsuch power line networks causes a relevant part of the high frequency signals\ntraveling through them to be radiated instead of being conducted. This causes\nnot only electromagnetic interference (EMI) with devices positioned next to\npower line cables, but also a consistent deterioration of the signal integrity.\nSince existing PLC channel models do not take into account losses due to\nradiation phenomena, this paper responds to the need of developing accurate\nnetwork simulators. A thorough analysis is herein presented about the conducted\nand radiated effects on the signal integrity, digging into differential mode to\ncommon mode signal conversion due to network imbalances. The outcome of this\nwork allows each network element to be described by a mixed-mode transmission\nmatrix. Furthermore, the classical per-unit-length equivalent circuit of\ntransmission lines is extended to incorporate radiation resistances. The\nresults of this paper lay the foundations for future developments of\ncomprehensive power line network models that incorporate conducted and radiated\nphenomena.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Nonequilibrium phase transition due to social group isolation: We introduce a simple model of a growing system with $m$ competing\ncommunities. The model corresponds to the phenomenon of defeats suffered by\nsocial groups living in isolation. A nonequilibrium phase transition is\nobserved when at critical time $t_c$ the first isolated cluster occurs. In the\none-dimensional system the volume of the new phase, i.e. the number of the\nisolated individuals, increases with time as $Z \\sim t^3$. For a large number\nof possible communities the critical density of filled space equals to $\\rho_c\n= (m/N)^{1/3}$ where $N$ is the system size. A similar transition is observed\nfor Erd\\H{o}s-R\\'{e}nyi random graphs and Barab\\'{a}si-Albert scale-free\nnetworks. Analytic results are in agreement with numerical simulations.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Traffic congestion and travel time prediction based on historical\n  congestion maps and identification of consensual days: In this paper, a new practice-ready method for the real-time estimation of\ntraffic conditions and travel times on highways is introduced. First, after a\nprincipal component analysis, observation days of a historical dataset are\nclustered. Two different methods are compared: a Gaussian Mixture Model and a\nk-means algorithm. The clustering results reveal that congestion maps of days\nof the same group have substantial similarity in their traffic conditions and\ndynamic. Such a map is a binary visualization of the congestion propagation on\nthe freeway, giving more importance to the traffic dynamics. Second, a\nconsensus day is identified in each cluster as the most representative day of\nthe community according to the congestion maps. Third, this information\nobtained from the historical data is used to predict traffic congestion\npropagation and travel times. Thus, the first measurements of a new day are\nused to determine which consensual day is the closest to this new day. The past\nobservations recorded for that consensual day are then used to predict future\ntraffic conditions and travel times. This method is tested using ten months of\ndata collected on a French freeway and shows very encouraging results.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Paradox of integration---Cellular automata approach: We discuss the self-deprecating strategy introduced by Peter Blau as one of\nstages of the process of social integration. Recently we have introduced a\ntwo-dimensional space of status, real and surface one ($A$ and $B$), and we\nhave demonstrated that with this setup, the self-deprecating strategy\nefficiently prevents the rejection [K. Malarz and K. Kulakowski, International\nJournal of Modern Physics C 30, 1950040 (2019)]. There, the process of reducing\nthe conflict was described by master equations, i.e. a set of differential\nequations describing evolution of density $v(A,B)$ of actors of status $(A,B)$.\nHere we reformulate the problem in terms of probabilistic cellular automata.\nThe obtained results for number $n(A,B)$ of actors of status $(A,B)$ are\nqualitatively the same as in the previous approach, both for synchronous and\nasynchronous version of the automaton. Namely, an enhancement of the surface\nstatus compensates a deficiency of the real one.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Physics of Fun: Quantifying Human Engagement into Playful Activities: Engaging in playful activities, such as playing a musical instrument,\nlearning a language, or performing sports, is a fundamental aspect of human\nlife. We present a quantitative empirical analysis of the engagement dynamics\ninto playful activities. We do so by analyzing the behavior of millions of\nplayers of casual video games and discover a scaling law governing the\nengagement dynamics. This power-law behavior is indicative of a multiplicative\n(i.e., ``happy-get-happier'') mechanism of engagement characterized by a set of\ncritical exponents. We also find, depending on the critical exponents, that\nthere is a phase transition between the standard case where all individuals\neventually quit the activity and another phase where a finite fraction of\nindividuals never abandon the activity. The behavior that we have uncovered in\nthis work might not be restricted only to human interaction with videogames.\nInstead, we believe it reflects a more general and profound behavior of how\nhumans become engaged in challenging activities with intrinsic rewards.",
        "category": "physics_soc-ph"
    },
    {
        "text": "On the numerical study of percolation and epidemic critical properties\n  in networks: The static properties of the fundamental model for epidemics of diseases\nallowing immunity (susceptible-infected-removed model) are known to be\nderivable by an exact mapping to bond percolation. Yet when performing\nnumerical simulations of these dynamics in a network a number of subtleties\nmust be taken into account in order to correctly estimate the transition point\nand the associated critical properties. We expose these subtleties and identify\nthe different quantities which play the role of criticality detector in the two\ndynamics.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Distinct spatial characteristics of industrial and public research\n  collaborations: Evidence from the 5th EU Framework Programme: This study compares the spatial characteristics of industrial R&D networks to\nthose of public research R&D networks (i.e. universities and research\norganisations). The objective is to measure the impact of geographical\nseparation effects on the constitution of cross-region R&D collaborations for\nboth types of collaboration. We use data on joint research projects funded by\nthe 5th European Framework Programme (FP) to proxy cross-region collaborative\nactivities. The study area is composed of 255 NUTS-2 regions that cover the\nEU-25 member states (excluding Malta and Cyprus) as well as Norway and\nSwitzerland. We adopt spatial interaction models to analyse how the variation\nof cross-region industry and public research networks is affected by geography.\nThe results of the spatial analysis provide evidence that geographical factors\nsignificantly affect patterns of industrial R&D collaboration, while in the\npublic research sector effects of geography are much smaller. However, the\nresults show that technological distance is the most important factor for both\nindustry and public research cooperative activities.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Science4Peace in difficult times: The war on Ukraine has affected significantly scientific cooperation and\ncommunication in particle physics and also in many other fields of scientific,\ncultural and educational exchange. Immediately after the war in February 2022,\nmany scientific institutions paused or banned scientific cooperation and\nexchange with Russian and Belorusian institutes and their scientists.\nPublications were out on hold or even banned, if Russian scientists were on the\nauthor list. The Science4Peace (S4P) Forum was created in March 2022 as a\nconsequence of these restrictions, as a completely independent Forum for open\ncommunication among scientists, using independent ZOOM rooms and webpages. The\nbasic ideas of the S4P Forum are fully in line with the policy of IUPAP of\nsupporting and encouraging free scientific exchange. In the course of\ndiscussions on the consequences of the war on Ukraine, the S4P Forum organized\na high-level panel discussion on \"Sanctions in Science - One Year of\nSanctions\". The war on Ukraine has also increased enormously the risk of\nnuclear escalation. Together with 14 Nobel laureates and many Scientists, the\nS4P Forum launched an appeal to subscribe to the \"no-first use\" policy and\nurged the governments to subscribe to the Treaty of the Prohibition of Nuclear\nWeapons adopted by the United Nations. The S4P Forum fully supports the ideas\nand activities originating from the International Year of Basic Sciences for\nSustainable Development (IYBSSD) to open and to keep open discussion channels\nat all levels, using Science as a common language.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Optimizing hospital distribution across districts for reducing\n  tuberculosis fatalities: The spatial distributions of diverse facilities are often understood in terms\nof the optimization of the commute distance or the economic profit.\nIncorporating more general objective functions into such optimization framework\nmay be useful, helping the policy decisions to meet various social and economic\ndemands. As an example, we consider how hospitals should be distributed to\nminimize the total fatalities of tuberculosis (TB). The empirical data of Korea\nshows that the fatality rate of TB in a district decreases with the areal\ndensity of hospitals, implying their correlation and the possibility of\nreducing the nationwide fatalities by adjusting the hospital distribution\nacross districts. Approximating the fatality rate by the probability of a\npatient not to visit a hospital in her/his residential district for the\nduration period of TB and evaluating the latter probability in the random-walk\nframework, we obtain the fatality rate as an exponential function of the\nhospital density with a characteristic constant related to each district's\neffective lattice constant estimable empirically. This leads us to the optimal\nhospital distribution which finds the hospital density in a district to be a\nlogarithmic function of the rescaled patient density. The total fatalities is\nreduced by 13\\% with this optimum. The current hospital density deviates from\nthe optimized one in different manners from district to district, which is\nanalyzed in the proposed model framework. The assumptions and limitations of\nour study are also discussed.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Random walks on activity-driven networks with attractiveness: Virtually all real-world networks are dynamical entities. In social networks,\nthe propensity of nodes to engage in social interactions (activity) and their\nchances to be selected by active nodes (attractiveness) are heterogeneously\ndistributed. Here, we present a time-varying network model where each node and\nthe dynamical formation of ties are characterised by these two features. We\nstudy how these properties affect random walk processes unfolding on the\nnetwork when the time scales describing the process and the network evolution\nare comparable. We derive analytical solutions for the stationary state and the\nmean first passage time of the process and we study cases informed by empirical\nobservations of social networks. Our work shows that previously disregarded\nproperties of real social systems such heterogeneous distributions of activity\nand attractiveness as well as the correlations between them, substantially\naffect the dynamical process unfolding on the network.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Stochastic Block Model Reveals the Map of Citation Patterns and Their\n  Evolution in Time: In this study we map out the large-scale structure of citation networks of\nscience journals and follow their evolution in time by using stochastic block\nmodels (SBMs). The SBM fitting procedures are principled methods that can be\nused to find hierarchical grouping of journals into blocks that show similar\nincoming and outgoing citations patterns. These methods work directly on the\ncitation network without the need to construct auxiliary networks based on\nsimilarity of nodes. We fit the SBMs to the networks of journals we have\nconstructed from the data set of around 630 million citations and find a\nvariety of different types of blocks, such as clusters, bridges, sources, and\nsinks. In addition we use a recent generalization of SBMs to determine how much\na manually curated classification of journals into subfields of science is\nrelated to the block structure of the journal network and how this relationship\nchanges in time. The SBM method tries to find a network of blocks that is the\nbest high-level representation of the network of journals, and we illustrate\nhow these block networks (at various levels of resolution) can be used as maps\nof science.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Model for heterogeneous reaction-diffusion systems with application to\n  one epidemic: The dynamics of ecological as well as chemical systems may depend on\nheterogeneous configurations. Heterogeneity in reaction-diffusion systems often\nincrease modelling and simulating difficulties when non-linear effects are\npresent. One synthetic epidemic system with short range heterogeneous\ncomposition is modelled and its space-time evolution studied using maximum\nheterogeneity details. Two other modelling alternatives are applied, one of\nthem using elementary mean-field variables, one other using non-localized\ngeometrical parameters, so avoiding the limitations of the used mean-field\nmodel, while keeping significant features of more detailed models. Both the\ndetailed and the mean-field models are solved by means of the standard finite\nvolume method. The model with less defined geometry is solved by means of one\nmodified version of the finite volume method. Simulation results of the three\nmodels are compared. At the high diffusion range all models behave similarly.\nAt moderate diffusion fluxes, the numerical results of the model with reduced\ngeometric details are in excellent agreement with the results of the detailed\nmodel. The simple mean-field model presents limited accuracy at low and\nmoderate values of the diffusion coefficient.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Market memory and fat tail consequences in option pricing on the expOU\n  stochastic volatility model: The expOU stochastic volatility model is capable of reproducing fairly well\nmost important statistical properties of financial markets daily data. Among\nthem, the presence of multiple time scales in the volatility autocorrelation is\nperhaps the most relevant which makes appear fat tails in the return\ndistributions. This paper wants to go further on with the expOU model we have\nstudied in Ref. 1 by exploring an aspect of practical interest. Having as a\nbenchmark the parameters estimated from the Dow Jones daily data, we want to\ncompute the price for the European option. This is actually done by Monte\nCarlo, running a large number of simulations. Our main interest is to \"see\" the\neffects of a long-range market memory from our expOU model in its subsequent\nEuropean call option. We pay attention to the effects of the existence of a\nbroad range of time scales in the volatility. We find that a richer set of time\nscales brings to a higher price of the option. This appears in clear contrast\nto the presence of memory in the price itself which makes the price of the\noption cheaper.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Epidemic Spreading and Aging in Temporal Networks with Memory: Time-varying network topologies can deeply influence dynamical processes\nmediated by them. Memory effects in the pattern of interactions among\nindividuals are also known to affect how diffusive and spreading phenomena take\nplace. In this paper we analyze the combined effect of these two ingredients on\nepidemic dynamics on networks. We study the susceptible-infected-susceptible\n(SIS) and the susceptible-infected-removed (SIR) models on the recently\nintroduced activity-driven networks with memory. By means of an activity-based\nmean-field approach we derive, in the long time limit, analytical predictions\nfor the epidemic threshold as a function of the parameters describing the\ndistribution of activities and the strength of the memory effects. Our results\nshow that memory reduces the threshold, which is the same for SIS and SIR\ndynamics, therefore favouring epidemic spreading. The theoretical approach\nperfectly agrees with numerical simulations in the long time asymptotic regime.\nStrong aging effects are present in the preasymptotic regime and the epidemic\nthreshold is deeply affected by the starting time of the epidemics. We discuss\nin detail the origin of the model-dependent preasymptotic corrections, whose\nunderstanding could potentially allow for epidemic control on correlated\ntemporal networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Random Walk with Memory on Complex Networks: We study random walk on complex networks with transition probabilities which\ndepend on the current and previously visited nodes. By using an absorbing\nMarkov chain we derive an exact expression for the mean first passage time\nbetween pairs of nodes, for a random walk with a memory of one step. We have\nanalyzed one particular model of random walk, where the transition\nprobabilities depend on the number of paths to the second neighbors. The\nnumerical experiments on paradigmatic complex networks verify the validity of\nthe theoretical expressions, and also indicate that the flattening of the\nstationary occupation probability accompanies a nearly optimal random search.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Anomalous lifetime distributions and topological traps in ordering\n  dynamics: We address the role of community structure of an interaction network in\nordering dynamics, as well as associated forms of metastability. We consider\nthe voter and AB model dynamics in a network model which mimics social\ninteractions. The AB model includes an intermediate state between the two\nexcluding options of the voter model. For the voter model we find dynamical\nmetastable disordered states with a characteristic mean lifetime. However, for\nthe AB dynamics we find a power law distribution of the lifetime of metastable\nstates, so that the mean lifetime is not representative of the dynamics. These\ntrapped metastable states, which can order at all time scales, originate in the\nmesoscopic network structure.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Combination of institutional incentives for cooperative governance of\n  risky commons: Finding appropriate incentives to enforce collaborative efforts for governing\nthe commons in risky situations is a long-lasting challenge. Previous works\nhave demonstrated that both punishing free-riders and rewarding cooperators\ncould be potential tools to reach this goal. Despite weak theoretical\nfoundations, policy-makers frequently impose a punishment-reward combination.\nHere, we consider the emergence of positive and negative incentives and analyze\ntheir simultaneous impact on sustaining risky commons. Importantly, we consider\ninstitutions with fixed and flexible incentives. We find that a local\nsanctioning scheme with pure reward is the optimal incentive strategy. It can\ndrive the entire population towards a highly cooperative state in a broad range\nof parameters, independently of the type of institutions. We show that our\nfinding is also valid for flexible incentives in the global sanctioning scheme,\nalthough the local arrangement works more effectively.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Structure of Phonological Networks Across Multiple Languages: The network characteristics based on the phonological similarities in the\nlexicons of several languages were examined. These languages differed widely in\ntheir history and linguistic structure, but commonalities in the network\ncharacteristics were observed. These networks were also found to be different\nfrom other networks studied in the literature. The properties of these networks\nsuggest explanations for various aspects of linguistic processing and hint at\ndeeper organization within human language.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The effects of degree correlations on network topologies and robustness: Complex networks have been applied to model numerous interactive nonlinear\nsystems in the real world. Knowledge about network topology is crucial for\nunderstanding the function, performance and evolution of complex systems. In\nthe last few years, many network metrics and models have been proposed to\nilluminate the network topology, dynamics and evolution. Since these network\nmetrics and models derive from a wide range of studies, a systematic study is\nrequired to investigate the correlations between them. The present paper\nexplores the effect of degree correlation on the other network metrics through\nstudying an ensemble of graphs where the degree sequence (set of degrees) is\nfixed. We show that to some extent, the characteristic path length, clustering\ncoefficient, modular extent and robustness of networks are directly influenced\nby the degree correlation.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Disentangling co-occurrence patterns in n-partite ecosystems: The need to harmonise apparently irreconcilable arrangements in an ecosystem\n--nestedness and segregation-- has triggered so far different strategies.\nMethodological refinements, or the inclusion of behavioural preferences to the\nnetwork dynamics offer a limited approach to the problem, since one remains\nconstrained within a 2-dimensional view of an ecosystem, i.e. a\npresence-absence matrix. Here we question this partial-view paradigm, and show\nthat nestedness and segregation may coexist across a varied range of scenarios.\nTo do so, we rely on an upscaled representation of an ecological community as\nan $n$-partite hypergraph, inspired by Hutchinson's high-dimensional niche\nconcept and the latest trends on ecological multilayer networks. This yields an\ninclusive description of an ecological system, for which we develop a natural\nextension of the definition of nestedness to larger dimensional spaces,\nrevealing how competitive exclusion may operate regardless of a highly nested\nbipartite system.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Anomalous supply shortages from dynamic pricing in on-demand mobility: Dynamic pricing schemes are increasingly employed across industries to\nmaintain a self-organized balance of demand and supply. However, throughout\ncomplex dynamical systems, unintended collective states exist that may\ncompromise their function. Here we reveal how dynamic pricing may induce\ndemand-supply imbalances instead of preventing them. Combining game theory and\ntime series analysis of dynamic pricing data from on-demand ride-hailing\nservices, we explain this apparent contradiction. We derive a phase diagram\ndemonstrating how and under which conditions dynamic pricing incentivizes\ncollective action of ride-hailing drivers to induce anomalous supply shortages.\nBy disentangling different timescales in price time series of ride-hailing\nservices at 137 locations across the globe, we identify characteristic patterns\nin the price dynamics reflecting these anomalous supply shortages. Our results\nprovide systemic insights for the regulation of dynamic pricing, in particular\nin publicly accessible mobility systems, by unraveling under which conditions\ndynamic pricing schemes promote anomalous supply shortages.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Local fractality: the case of forest fires in Portugal: The research deals with a study of local fractality in spatial distribution\nof forest fires in Portugal using the sandbox method. The general procedure is\nthe following: (a) define a circle centred in each and all events with\nincreasing radius R; (b) count the number of other events located within the\ncircle of radius R, N(R); (c) plot the growth curve which is the functional\ndependence of N(R) versus R; and (d) estimate the local fractal dimension as\nthe slope on log[N(R)] versus log[R]. The computation is carried out by using\nthe location of every fire event as a centre but without the final averaging\nover all the fires for a given R, which is usually performed to get a global\nfractal dimension and to estimate global clustering. Sandbox method is widely\nused in many applications in physics and other subjects. The local procedure\nhas the ability to provide the most complete information regarding the spatial\ndistribution of clustering and avoiding non-homogeneity and non-stationarity\nproblems. Most of the analysis was performed using the National Mapping Burnt\nArea (NMBA) database which accounts for 32,156 fires during the 1975 - 2013\nperiod. The results of local analysis are compared with a randomly generated\npattern in forest zones (validity domain). The results demonstrate interesting\nlocal spatial patterns of clustering. Some results on global measures are\nreported as well.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Revealing spatio-temporal interaction patterns behind complex cities: Cities are typical dynamic complex systems that connect people and facilitate\ninteractions. Revealing universal collective patterns behind spatio-temporal\ninteractions between residents is crucial for various urban studies, of which\nwe are still lacking a comprehensive understanding. Massive cellphone data\nenable us to construct interaction networks based on spatio-temporal\nco-occurrence of individuals. The rank-size distributions of hourly dynamic\npopulation of locations are stable, although people are almost constantly\nmoving in cities and hotspots that attract people are changing over time in a\nday. A larger city is of a stronger heterogeneity as indicated by a larger\nscaling exponent. After aggregating spatio-temporal interaction networks over\nconsecutive time windows, we reveal a switching behavior of cities between two\nstates. During the \"active\" state, the whole city is concentrated in fewer\nlarger communities; while in the \"sleeping\" state, people are scattered in more\nsmaller communities. Above discoveries are universal over diversified cities\nacross continents. In addition, a city sleeps less, when its population grows\nlarger. And spatio-temporal interaction segregation can be well approximated by\nresidential segregation in smaller cities, but not in larger ones. We propose a\ntemporal-population-weighted-opportunity model by integrating time-dependent\ndeparture probability to make dynamic predictions on human mobility, which can\nreasonably well explain observed patterns of spatio-temporal interactions in\ncities.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Universality of citation distributions for academic institutions and\n  journals: Citations measure the importance of a publication, and may serve as a proxy\nfor its popularity and quality of its contents. Here we study the distributions\nof citations to publications from individual academic institutions for a single\nyear. The average number of citations have large variations between different\ninstitutions across the world, but the probability distributions of citations\nfor individual institutions can be rescaled to a common form by scaling the\ncitations by the average number of citations for that institution. We find this\nfeature seem to be universal for a broad selection of institutions irrespective\nof the average number of citations per article. A similar analysis for\ncitations to publications in a particular journal in a single year reveals\nsimilar results. We find high absolute inequality for both these sets, Gini\ncoefficients being around $0.66$ and $0.58$ for institutions and journals\nrespectively. We also find that the top $25$% of the articles hold about $75$%\nof the total citations for institutions and the top $29$% of the articles hold\nabout $71$% of the total citations for journals.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Mobility strategies based on percolation theory to avoid the spread of\n  diseases: COVID-19: Human mobility is an important factor in the propagation of infectious\ndiseases. In particular, the spatial spread of a disease is a consequence of\nhuman mobility. On the other hand, the control strategies based on mobility\nrestrictions are generally unpopular and costly. These high social and economic\ncosts make it very important to design global protocols where the cost is\nminimized and effects maximized. In this work, we calculate the percolation\nthreshold of the spread in a network of a disease. In particular, we found the\nnumber of roads to close and regions to isolate in the Puebla State, Mexico, to\navoid the global spread of COVID-19. Computational simulations taking into\naccount the proposed strategy show a potential reduction of 94% of infections.\nThis methodology can be used in broader and different areas to help in the\ndesign of health policies.\n  --\n  La movilidad de las personas es uno de los principales factores que propician\nla propagaci\\'on de epidemias. En particular, es el factor que genera el\nesparcimiento de la enfermedad en diferentes regiones. Las medidas de control\nepidemiol\\'ogico basadas en la restricci\\'on de movilidad son generalmente poco\npopulares y las consecuencias econ\\'omicas pueden llegar a ser muy grandes.\nDebido a los altos costos de estas medidas, es de gran relevancia tener\nestrategias globales que optimicen las medidas minimizando los costos. En este\ntrabajo, se calcula el umbral de percolaci\\'on de la propagaci\\'on de\nenfermedades en redes. De manera particular, se encuentra el n\\'umero de\ncaminos a restringir y localidades que tienen que ser aisladas para limitar la\npropagaci\\'on global de COVID-19 en el Estado de Puebla, M\\'exico. Simulaciones\ncomputacionales donde se implementan las medidas de restricci\\'on de movilidad\nentre los diferentes municipios, junto con las medidas de confinamiento,\nmuestran que es posible reducir un 94% de la poblaci\\'on afectada comparado con\nel caso en el que no se implementa ninguna medida. Esta metodolog\\'ia puede ser\naplicada a distintas zonas para ayudar a las autoridades de salud en la toma de\ndecisiones.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Got the Flu (or Mumps)? Check the Eigenvalue!: For a given, arbitrary graph, what is the epidemic threshold? That is, under\nwhat conditions will a virus result in an epidemic? We provide the super-model\ntheorem, which generalizes older results in two important, orthogonal\ndimensions. The theorem shows that (a) for a wide range of virus propagation\nmodels (VPM) that include all virus propagation models in standard literature\n(say, [8][5]), and (b) for any contact graph, the answer always depends on the\nfirst eigenvalue of the connectivity matrix. We give the proof of the theorem,\narithmetic examples for popular VPMs, like flu (SIS), mumps (SIR), SIRS and\nmore. We also show the implications of our discovery: easy (although sometimes\ncounter-intuitive) answers to `what-if' questions; easier design and evaluation\nof immunization policies, and significantly faster agent-based simulations.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Study of a Market Model with Conservative Exchanges on Complex Networks: Many models of market dynamics make use of the idea of conservative wealth\nexchanges among economic agents. A few years ago an exchange model using\nextremal dynamics was developed and a very interesting result was obtained: a\nself-generated minimum wealth or poverty line. On the other hand, the wealth\ndistribution exhibited an exponential shape as a function of the square of the\nwealth. These results have been obtained both considering exchanges between\nnearest neighbors or in a mean field scheme. In the present paper we study the\neffect of distributing the agents on a complex network. We have considered\narchetypical complex networks: Erd\\\"{o}s-R\\'enyi random networks and scale-free\nnetworks. The presence of a poverty line with finite wealth is preserved but\nspatial correlations are important, particularly between the degree of the node\nand the wealth. We present a detailed study of the correlations, as well as the\nchanges in the Gini coefficient, that measures the inequality, as a function of\nthe type and average degree of the considered networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Network community detection and clustering with random walks: We present a novel approach to partitioning network nodes into\nnon-overlapping communities - a key step in revealing network modularity and\nhierarchical organization. Our methodology, applicable to networks with both\nweighted and unweighted symmetric edges, uses random walks to explore\nneighboring nodes in the same community. The walk-likelihood algorithm (WLA)\nproduces an optimal partition of network nodes into a given number of\ncommunities. The walk-likelihood community finder (WLCF) employs WLA to predict\nboth the optimal number of communities and the corresponding network partition.\nWe have extensively benchmarked both algorithms, finding that they outperform\nor match other methods in terms of the modularity of predicted partitions and\nthe number of links between communities. Making use of the computational\nefficiency of our approach, we investigated a large-scale map of roads and\nintersections in the state of Colorado. Our clustering yielded geographically\nsensible boundaries between neighboring communities.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Ranking Swing Voters in Congressional Elections: We present a model for quantitatively identifying swing voters in\ncongressional elections. This is achieved by predicting an individual voter's\nlikelihood to vote and an individual voter's likelihood to vote for a given\nparty, if he votes. We make a rough prediction of these values. We then update\nthese predictions by incorporating information on a municipality wide basis via\naggregate data to enhance our estimate under the assumption that nearby voters\nhave similar behavior, which could be due to social interaction or common\nexternal factors. Finally, we use a ranking scheme on these predictions to\nidentify two key types of voter: 1) Voters who are likely to vote that we can\nconvince to vote for a given party; and, 2) Voters who are likely to vote for a\ngiven party, if they vote, that we can convince to actually turn out to vote.\nOnce these voters have been identified, a political campaign can use this\ninformation to micro-target voters and win more votes.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Proper Interpretation of Heaps' and Zipf's Laws: We checked that the distribution of words in text should uniform, which gives\nHeaps' law as natural result, that is, the number of types of words can be\nexpressed as a power law of the number of tokens within text. We developed a\n``superposition'' model, which leads to an asymptotic power-law distribution of\nthe number of occurrences (or frequency) of words, that is, Zipf's law. The\nmodel is well consistent with observations.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A Case Study of Gender Bias at the Postdoctoral Level in Physics, and\n  its Resulting Impact on the Academic Career Advancement of Females: This case study of a typical U.S. particle physics experiment explores the\nissues of gender bias and how it affects the academic career advancement\nprospects of women in the field of physics beyond the postdoctoral level; we\nuse public databases to study the career paths of the full cohort of 57 former\npostdoctoral researchers on the Run II Dzero experiment to examine if males and\nfemales were treated in a gender-blind fashion on the experiment.\n  The study finds that the female researchers were on average significantly\nmore productive compared to their male peers, yet were allocated only 1/3 the\namount of conference presentations based on their productivity. The study also\nfinds that the dramatic gender bias in allocation of conference presentations\nappeared to have significant negative impact on the academic career advancement\nof the females.\n  The author has a PhD in particle physics and worked for six years as a\npostdoctoral research scientist, five of which were spent collaborating at\nFermilab. She is currently completing a graduate degree in statistics.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A map of approaches to temporal networks: The study of temporal networks is motivated by the simple and important\nobservation that just as network structure can affect dynamics, so can\nstructure in time. Just as network topology can teach us about the system in\nquestion, so can its temporal characteristics. In many cases, leaving out\neither one of these components would lead to an incomplete understanding of the\nsystem or poor predictions. We argue that including time into network modeling\ninevitably leads researchers away from the trodden paths of network science.\nTemporal network theory requires something different -- new methods, new\nconcepts, new questions -- compared to static networks. In this introductory\nchapter, we overview the ideas that the field of temporal networks has brought\nforward in the last decade. We also place the contributions to the current\nvolume on this map of temporal-network approaches.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A Random Walk Perspective on Hide-and-Seek Games: We investigate hide-and-seek games on complex networks using a random walk\nframework. Specifically, we investigate the efficiency of various degree-biased\nrandom walk search strategies to locate items that are randomly hidden on a\nsubset of vertices of a random graph. Vertices at which items are hidden in the\nnetwork are chosen at random as well, though with probabilities that may depend\non degree. We pitch various hide and seek strategies against each other, and\ndetermine the efficiency of search strategies by computing the average number\nof hidden items that a searcher will uncover in a random walk of $n$ steps. Our\nanalysis is based on the cavity method for finite single instances of the\nproblem, and generalises previous work of De Bacco et al. [1] so as to cover\ndegree-biased random walks. We also extend the analysis to deal with the\nthermodynamic limit of infinite system size. We study a broad spectrum of\nfunctional forms for the degree bias of both the hiding and the search strategy\nand investigate the efficiency of families of search strategies for cases where\ntheir functional form is either matched or unmatched to that of the hiding\nstrategy. Our results are in excellent agreement with those of numerical\nsimulations. We propose two simple approximations for predicting efficient\nsearch strategies. One is based on an equilibrium analysis of the random walk\nsearch strategy. While not exact, it produces correct orders of magnitude for\nparameters characterising optimal search strategies. The second exploits the\nexistence of an effective drift in random walks on networks, and is expected to\nbe efficient in systems with low concentration of small degree nodes.",
        "category": "physics_soc-ph"
    },
    {
        "text": "An Agent-Based Approach to Self-Organized Production: The chapter describes the modeling of a material handling system with the\nproduction of individual units in a scheduled order. The units represent the\nagents in the model and are transported in the system which is abstracted as a\ndirected graph. Since the hindrances of units on their path to the destination\ncan lead to inefficiencies in the production, the blockages of units are to be\nreduced. Therefore, the units operate in the system by means of local\ninteractions in the conveying elements and indirect interactions based on a\nmeasure of possible hindrances. If most of the units behave cooperatively\n(\"socially\"), the blockings in the system are reduced.\n  A simulation based on the model shows the collective behavior of the units in\nthe system. The transport processes in the simulation can be compared with the\nprocesses in a real plant, which gives conclusions about the consequencies for\nthe production based on the superordinate planning.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Spontaneous Emergence of Conventions: An Experimental Study of\n  Cultural Evolution: How do shared conventions emerge in complex decentralized social systems?\nThis question engages fields as diverse as linguistics, sociology and cognitive\nscience. Previous empirical attempts to solve this puzzle all presuppose that\nformal or informal institutions, such as incentives for global agreement,\ncoordinated leadership, or aggregated information about the population, are\nneeded to facilitate a solution. Evolutionary theories of social conventions,\nby contrast, hypothesize that such institutions are not necessary in order for\nsocial conventions to form. However, empirical tests of this hypothesis have\nbeen hindered by the difficulties of evaluating the real-time creation of new\ncollective behaviors in large decentralized populations. Here, we present\nexperimental results - replicated at several scales - that demonstrate the\nspontaneous creation of universally adopted social conventions, and show how\nsimple changes in a population's network structure can direct the dynamics of\nnorm formation, driving human populations with no ambition for large scale\ncoordination to rapidly evolve shared social conventions.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Stochastic sampling effects favor manual over digital contact tracing: Isolation of symptomatic individuals, tracing and testing of their\nnonsymptomatic contacts are fundamental strategies for mitigating the current\nCOVID-19 pandemic. The breaking of contagion chains relies on two complementary\nstrategies: manual reconstruction of contacts based on interviews and a digital\n(app-based) privacy-preserving contact tracing. We compare their effectiveness\nusing model parameters tailored to describe SARS-CoV-2 diffusion within the\nactivity-driven model, a general empirically validated framework for network\ndynamics. We show that, even for equal probability of tracing a contact, manual\ntracing robustly performs better than the digital protocol, also taking into\naccount the intrinsic delay and limited scalability of the manual procedure.\nThis result is explained in terms of the stochastic sampling occurring during\nthe case-by-case manual reconstruction of contacts, contrasted with the\nintrinsically prearranged nature of digital tracing, determined by the decision\nto adopt the app or not by each individual. The better performance of manual\ntracing is enhanced by heterogeneity in agent behavior: superspreaders not\nadopting the app are completely invisible to digital contact tracing, while\nthey can be easily traced manually, due to their multiple contacts. We show\nthat this intrinsic difference makes the manual procedure dominant in realistic\nhybrid protocols.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The End of Cheap Uranium: Historic data from many countries demonstrate that on average no more than\n50-70% of the uranium in a deposit could be mined. An analysis of more recent\ndata from Canada and Australia leads to a mining model with an average deposit\nextraction lifetime of 10+- 2 years. This simple model provides an accurate\ndescription of the extractable amount of uranium for the recent mining\noperations.\n  Using this model for all larger existing and planned uranium mines up to\n2030, a global uranium mining peak of at most 58 +- 4 ktons around the year\n2015 is obtained. Thereafter we predict that uranium mine production will\ndecline to at most 54 +- 5 ktons by 2025 and, with the decline steepening, to\nat most 41 +- 5 ktons around 2030. This amount will not be sufficient to fuel\nthe existing and planned nuclear power plants during the next 10-20 years. In\nfact, we find that it will be difficult to avoid supply shortages even under a\nslow 1%/year worldwide nuclear energy phase-out scenario up to 2025. We thus\nsuggest that a worldwide nuclear energy phase-out is in order.\n  If such a slow global phase-out is not voluntarily effected, the end of the\npresent cheap uranium supply situation will be unavoidable. The result will be\nthat some countries will simply be unable to afford sufficient uranium fuel at\nthat point, which implies involuntary and perhaps chaotic nuclear phase-outs in\nthose countries involving brownouts, blackouts, and worse.",
        "category": "physics_soc-ph"
    },
    {
        "text": "COVID-19 second wave mortality in Europe and the United States: This paper introduces new methods to analyze the changing progression of\nCOVID-19 cases to deaths in different waves of the pandemic. First, an\nalgorithmic approach partitions each country or state's COVID-19 time series\ninto a first wave and subsequent period. Next, offsets between case and death\ntime series are learned for each country via a normalized inner product.\nCombining these with additional calculations, we can determine which countries\nhave most substantially reduced the mortality rate of COVID-19. Finally, our\npaper identifies similarities in the trajectories of cases and deaths for\nEuropean countries and U.S. states. Our analysis refines the popular conception\nthat the mortality rate has greatly decreased throughout Europe during its\nsecond wave of COVID-19; instead, we demonstrate substantial heterogeneity\nthroughout Europe and the U.S. The Netherlands exhibited the largest reduction\nof mortality, a factor of 16, followed by Denmark, France, Belgium, and other\nWestern European countries, greater than both Eastern European countries and\nU.S. states. Some structural similarity is observed between Europe and the\nUnited States, in which Northeastern states have been the most successful in\nthe country. Such analysis may help European countries learn from each other's\nexperiences and differing successes to develop the best policies to combat\nCOVID-19 as a collective unit.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Power and Leadership: A Complex Systems Science Approach Part I -\n  Representation and Dynamics: Historical narratives are dominated by actions of powerful individuals as\nwell as competitions for power. Characterizing the origins, types and\ncompetitive strength of different kinds of power may yield a scaffolding for\nunderstanding historical processes and mechanisms for winning or avoiding\nconflicts. Michael Mann distinguished four types of power: political, military,\neconomic, and ideological. We show this framework is justified by four\nmotivations of individuals to transfer decisions to leaders: Desire to be a\nmember of a collective, avoiding harm due to threat, gaining benefit due to\npayment, acquiring a value system. Constructing models of societies based upon\nthese types of power enables distinguishing social systems and describing their\ndynamics. Dynamics include (a) competition between power systems, (b)\ncompetition between powerful individuals, and (c) the dynamics of values within\na powerful individual. A historical trend is the progressive separation of\ntypes of power to distinct groups of individuals. In ancient empires all forms\nof power were concentrated in a single individual, e.g. Caesar during the Pax\nRomana period. In an idealized modern democratic state, the four types of power\nare concentrated in distinct sets of individuals. The progressive separation\nsuggests that in some contexts this confers a \"fitness\" advantage in an\nevolutionary process similar to the selection of biological organisms. However,\nindividual countries may not separate power completely. The influence of wealth\nin politics and regulatory capture is a signature of economic leader dominance,\ne.g. the US. Important roles of political leaders in economics and corruption\nare a signature of the political leader dominance, e.g. China. Ideological\nleaders dominate in theocracies, e.g. Iran. Military leaders dominate in\ndictatorships or where military leaders select leadership, e.g. Egypt.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Pandemic Vulnerability Index of US Cities: A Hybrid Knowledge-based and\n  Data-driven Approach: Cities become mission-critical zones during pandemics and it is vital to\ndevelop a better understanding of the factors that are associated with\ninfection levels. The COVID-19 pandemic has impacted many cities severely;\nhowever, there is significant variance in its impact across cities. Pandemic\ninfection levels are associated with inherent features of cities (e.g.,\npopulation size, density, mobility patterns, socioeconomic condition, and\nhealth environment), which need to be better understood. Intuitively, the\ninfection levels are expected to be higher in big urban agglomerations, but the\nmeasurable influence of a specific urban feature is unclear. The present study\nexamines 41 variables and their potential influence on COVID-19 cases and\nfatalities. The study uses a multi-method approach to study the influence of\nvariables, classified as demographic, socioeconomic, mobility and connectivity,\nurban form and density, and health and environment dimensions. This study\ndevelops an index dubbed the PVI-CI for classifying the pandemic vulnerability\nlevels of cities, grouping them into five vulnerability classes, from very high\nto very low. Furthermore, clustering and outlier analysis provides insights on\nthe spatial clustering of cities with high and low vulnerability scores. This\nstudy provides strategic insights into levels of influence of key variables\nupon the spread of infections as well as fatalities, along with an objective\nranking for the vulnerability of cities. Thus it provides critical wisdom\nneeded for urban healthcare policy and resource management. The pandemic\nvulnerability index calculation method and the process present a blueprint for\nthe development of similar indices for cities in other countries, leading to a\nbetter understanding and improved pandemic management for urban areas and\npost-pandemic urban planning across the world.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Bi-modal public transit system for Berlin and Brandenburg: Bi-modla public transport system consisting of a rail-bound line service and\na fleet of on-demand shuttles providing connections to the line service stops\nis studied for the states of Berlin and Brandenburg for user adoption of x = 1%\nand x = 10% of the total population.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Goal quest for an intelligent surfer moving in a chaotic flow: We consider a model of an intelligent surfer moving on the Ulam network\ngenerated by a chaotic dynamics in the Chirikov standard map. This directed\nnetwork is obtained by the Ulam method with a division of the phase space in\ncells of fixed size forming the nodes of a Markov chain. The goal quest for\nthis surfer is to determine the network path from an initial node A to a final\nnode B with minimal resistance given by the sum of inverse transition\nprobabilities. We develop an algorithm for the intelligent surfer that allows\nto perform the quest in a small number of transitions which grows only\nlogarithmically with the network size. The optimal path search is done on a\nfractal intersection set formed by nodes with small Erd\\\"os numbers of the\nforward and inverted networks. The intelligent surfer exponentially outperforms\na naive surfer who tries to minimize its phase space distance to the target B.\nWe argue that such an algorithm provides new hints for motion control in\nchaotic flows.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Epidemic Dynamics On Information-Driven Adaptive Networks: can evolve simultaneously. For the information-driven adaptive process,\nsusceptible (infected) individuals who have abilities to recognize the disease\nwould break the links of their infected (susceptible) neighbors to prevent the\nepidemic from further spreading. Simulation results and numerical analyses\nbased on the pairwise approach indicate that the information-driven adaptive\nprocess can not only slow down the speed of epidemic spreading, but can also\ndiminish the epidemic prevalence at the final state significantly. In addition,\nthe disease spreading and information diffusion pattern on the lattice give a\nvisual representation about how the disease is trapped into an isolated field\nwith the information-driven adaptive process. Furthermore, we perform the local\nbifurcation analysis on four types of dynamical regions, including healthy,\noscillatory, bistable and endemic, to understand the evolution of the observed\ndynamical behaviors. This work may shed some lights on understanding how\ninformation affects human activities on responding to epidemic spreading.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Oscillating behavior within the social force model: The social force model belongs to a class of microscopic force-based\npedestrian model for which the interaction with the neighbors solely depends on\nthe distance spacings. Yet, distance based models can lead to unrealistic\noscillating behaviors with collision and negative peed (especially in 1D\nscenarios. With appropriate approximations we analyze the behavior of a\nsimplified force-based model and show that with \"realistic\" parameter values\nthe system oscillates.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Indirect reciprocity with assessments of group reputation: Indirect reciprocity unveils how social cooperation is founded upon moral\nsystems. Under the frame of dyadic games based on individual reputations, the\n\"leading-eight\" social norms distinguish themselves in promoting and sustaining\ncooperation. However, in real societies, people constantly interact within\nlarger groups. They are assessed not only by their own behaviors, but also the\nreputations of the groups they come from and interact with. Here, we study\nindirect reciprocity in public goods games with group reputations. We show that\nvarying criteria of group reputation assessment destabilize the consensus\nwithin leading-eight populations. We find the presence of an optimal,\nmoderately set social assessment criterion that is most conductive to promoting\ncooperation. As the payoff of group cooperation increases, the assessment\ncriterion evolves to relaxation following two dynamical patterns, indicating\nthat social prosperity encourages people to be tolerate. Our work identifies\nthe role of group reputation assessment to boost cooperation in social\npopulations.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Toward a Mathematical Theory of Behavioral-Social Dynamics for\n  Pedestrian Crowds: This paper presents a new approach to behavioral-social dynamics of\npedestrian crowds by suitable development of methods of the kinetic theory. It\nis shown how heterogeneous individual behaviors can modify the collective\ndynamics, as well as how local unusual behaviors can propagate in the crowd.\nThe main feature of this approach is a detailed analysis of the interactions\nbetween dynamics and social behaviors.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Clustered marginalization of minorities during social transitions\n  induced by co-evolution of behaviour and network structure: Large-scale transitions in societies are associated with both individual\nbehavioural change and restructuring of the social network. These two factors\nhave often been considered independently, yet recent advances in social network\nresearch challenge this view. Here we show that common features of societal\nmarginalization and clustering emerge naturally during transitions in a\nco-evolutionary adaptive network model. This is achieved by explicitly\nconsidering the interplay between individual interaction and a dynamic network\nstructure in behavioural selection. We exemplify this mechanism by simulating\nhow smoking behaviour and the network structure get reconfigured by changing\nsocial norms. Our results are consistent with empirical findings: The\nprevalence of smoking was reduced, remaining smokers were preferentially\nconnected among each other and formed increasingly marginalised clusters. We\npropose that self-amplifying feedbacks between individual behaviour and dynamic\nrestructuring of the network are main drivers of the transition. This\ngenerative mechanism for co-evolution of individual behaviour and social\nnetwork structure may apply to a wide range of examples beyond smoking.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The shape of memory in temporal networks: Temporal networks are widely used models for describing the architecture of\ncomplex systems. Network memory -- that is the dependence of a temporal\nnetwork's structure on its past -- has been shown to play a prominent role in\ndiffusion, epidemics and other processes occurring over the network, and even\nto alter its community structure. Recent works have proposed to estimate the\nlength of memory in a temporal network by using high-order Markov models. Here\nwe show that network memory is inherently multidimensional and cannot be\nmeaningfully reduced to a single scalar quantity. Accordingly, we introduce a\nmathematical framework for defining and efficiently estimating the microscopic\nshape of memory, which fully characterises how the activity of each link\nintertwines with the activities of all other links. We validate our methodology\non a wide range of synthetic models of temporal networks with tuneable memory,\nand subsequently study the heterogeneous shapes of memory emerging in various\nreal-world networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Urban Dynamics Through the Lens of Human Mobility: The urban spatial structure represents the distribution of public and private\nspaces in cities and how people move within them. While it usually evolves\nslowly, it can change fast during large-scale emergency events, as well as due\nto urban renewal in rapidly developing countries. This work presents an\napproach to delineate such urban dynamics in quasi-real-time through a human\nmobility metric, the mobility centrality index $\\Delta KS$. As a case study, we\ntracked the urban dynamics of eleven Spanish cities during the COVID-19\npandemic. Results revealed that their structures became more monocentric during\nthe lockdown in the first wave, but kept their regular spatial structures\nduring the second wave. To provide a more comprehensive understanding of\nmobility from home, we also introduce a dimensionless metric, $KS_{HBT}$, which\nmeasures the extent of home-based travel and provides statistical insights into\nthe transmission of COVID-19. By utilizing individual mobility data, our\nmetrics enable the detection of changes in the urban spatial structure.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Influencing factors investigation of passenger head injury in subway\n  collisions: With the development of the subway and the pressing demand of environmentally\nfriendly transportation, more and more people travel by subway. In recent\ndecades, the issues about passenger passive safety on the train have received\nextensive attention. In this research, the head injury of a standing passenger\nin the subway is investigated. Three MADYMO models of the different standing\npassenger postures, defined as baseline scenarios, are numerically set up.\nHIC15 values of passengers with different postures are gained by systematic\nparametric studies. The injury numerical simulation results of various\nscenarios with different friction coefficients, collision acceleration,\nstanding angle, horizontal handrail height, and ring handrail height are\nanalyzed. Results show that the horizontal handrail provides better protection\nin the three different standing passenger postures. Different friction\ncoefficients and the standing angle have great impact on the head injuries of\npassengers in three different scenarios. The handrail height also has some\neffects on head injury of passengers with different standing postures, so it is\nnecessary to be considered when designing the interior layout of the subway.\nThis study may provide guidance for the safety design of the subway and some\nadvices for standing subway passengers.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A new method for comparing rankings through complex networks: Model and\n  analysis of competitiveness of major European soccer leagues: In this paper we show a new technique to analyze families of rankings. In\nparticular we focus on sports rankings and, more precisely, on soccer leagues.\nWe consider that two teams compete when they change their relative positions in\nconsecutive rankings. This allows to define a graph by linking teams that\ncompete. We show how to use some structural properties of this competitivity\ngraph to measure to what extend the teams in a league compete. These structural\nproperties are the mean degree, the mean strength and the clustering\ncoefficient. We give a generalization of the Kendall's correlation coefficient\nto more than two rankings. We also show how to make a dynamic analysis of a\nleague and how to compare different leagues. We apply this technique to analyze\nthe four major European soccer leagues: Bundesliga, Italian Lega, Spanish Liga,\nand Premier League. We compare our results with the classical analysis of sport\nranking based on measures of competitive balance.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Impact of personal income on mortality by age: biological versus\n  socio-economic effects: The influence of per capita income on life expectancy is well documented,\nmostly through studies of multinational samples. However, one expects fairly\nweak correlations at both ends of the life span, that is to say in early\ninfancy and in age groups of elderly from 85 to 100 years. The reason is that\nat both ends mortality is largely controled by biological factors rather than\nby socio-economic conditions. In order to test this conjecture, we explore the\ninfluence of income on age groups, separately in France, the United States and\nSouth Korea. More precisely in each country we compare income and mortality\ndata in as many regional subunits as possible. One noteworthy constatation is\nthat, contrary to a common view, personal income is only weakly correlated with\ninfant mortality (i.e. mortality under the age of one year). More broadly, we\npropose as a conjecture that the common pattern revealed by the analysis of the\nthree countries is also valid in other developed countries.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Growth strategy determines network performance: The interplay between structure and function is crucial in determining some\nemerging properties of many natural systems. Here we use an adaptive neural\nnetwork model inspired in observations of synaptic pruning that couples\nactivity and topological dynamics and reproduces experimental temporal profiles\nof synaptic density, including an initial transient period of relatively high\nsynaptic connectivity. Using a simplified framework, we prove that the\nexistence of this transient is critical in providing ordered stationary states\nthat have the property of being able to store stable memories. In fact, there\nis a discontinuous phase transition between the ordered memory phase and a\ndisordered one as a function of the initial transient synaptic density. We also\nshow that intermediate synaptic density values are optimal in order to obtain\nthese stable memory states with a minimum energy consumption, and that\nultimately it is the transient heterogeneity in the network what determines the\nstationary state. Our results here could explain why the pruning curves\nobserved in actual brain areas present their characteristic temporal profiles\nand, eventually, anomalies such as autism and schizophrenia associated,\nrespectively, with a deficit or an excess of pruning.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Integrating Travel Demand and Network Modelling: a Myth or Future of\n  Transport Modelling: In this paper, a novel transport planning model system (TPMS) is formulated\nwhich is built on the concepts of supernetworks, multi-modality, integrity and\ncalibration. In the proposed formulation, activity travel pattern (ATP) choice\nfacets including the choices of activity, activity sequence, mode, departure\ntime, and parking location, are all unified into a time-dependent supernetwork.\nThe proposed model accounts for the dynamicity of the network, including\ntime-of-day and congestion effects. These help capturing the interdependencies\namong all different attributes of a full transport planning system. Moreover,\nthe proposed TPMS explicitly formulates an operating capacitated public\ntransport system. To allow visiting locations multiple times and to alleviate\nthe complexity of the proposed supernetwork, a novel multi-visit vehicle\nrouting problem is proposed which does not enumerate the node and link visits.\nIn order to calibrate the model based on the major travel attributes of the\ntravel survey data, a set of splitting ratios are introduced to distribute\ntrips on the supernetwork. The model uses the splitting ratios to integrate the\nsupernetwork and the traffic assignment model in a unified TPMS structure. At\nlast, numerical examples are provided to demonstrate the advantages of the\nproposed approach.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Fat-tailed fluctuations in the size of organizations: the role of social\n  influence: Organizational growth processes have consistently been shown to exhibit a\nfatter-than-Gaussian growth-rate distribution in a variety of settings. Long\nperiods of relatively small changes are interrupted by sudden changes in all\nsize scales. This kind of extreme events can have important consequences for\nthe development of biological and socio-economic systems. Existing models do\nnot derive this aggregated pattern from agent actions at the micro level. We\ndevelop an agent-based simulation model on a social network. We take our\ndeparture in a model by a Schwarzkopf et al. on a scale-free network. We\nreproduce the fat-tailed pattern out of internal dynamics alone, and also find\nthat it is robust with respect to network topology. Thus, the social network\nand the local interactions are a prerequisite for generating the pattern, but\nnot the network topology itself. We further extend the model with a parameter\n$\\delta$ that weights the relative fraction of an individual's neighbours\nbelonging to a given organization, representing a contextual aspect of social\ninfluence. In the lower limit of this parameter, the fraction is irrelevant and\nchoice of organization is random. In the upper limit of the parameter, the\nlargest fraction quickly dominates, leading to a winner-takes-all situation. We\nrecover the real pattern as an intermediate case between these two extremes.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Malleability of complex networks: Most complex networks are not static, but evolve along time. Given a specific\nconfiguration of one such changing network, it becomes a particularly\ninteresting issue to quantify the diversity of possible unfoldings of its\ntopology. In this work, we suggest the concept of malleability of a network,\nwhich is defined as the exponential of the entropy of the probabilities of each\npossible unfolding with respect to a given configuration. We calculate the\nmalleability with respect to specific measurements of the involved topologies.\nMore specifically, we identify the possible topologies derivable from a given\nconfiguration and calculate some topological measurement of them (e.g.\nclustering coefficient, shortest path length, assortativity, etc.), leading to\nrespective probabilities being associated to each possible measurement value.\nThough this approach implies some level of degeneracy in the mapping from\ntopology to measurement space, it still paves the way to inferring the\nmalleability of specific network types with respect to given topological\nmeasurements. We report that the malleability, in general, depends on each\nspecific measurement, with the average shortest path length and degree\nassortativity typically leading to large malleability values. The maximum\nmalleability was observed for the Wikipedia network and the minimum for the\nWatts-Strogatz model.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Facilitating Cooperation in Human-Agent Hybrid Populations through\n  Autonomous Agents: Cooperation is a vital social behavior that plays a crucial role in human\nprosperity, enabling conflict resolution and averting disastrous outcomes. With\nthe increasing presence of autonomous agents (AAs), human-agent interaction\nbecomes more frequent in modern society. We investigate the impact of\ncooperative and defective AAs on human cooperation within the framework of\nevolutionary game theory, particularly in one-shot social dilemma games. Our\nfindings reveal that cooperative AAs have a limited impact on prisoner's\ndilemma, but facilitate cooperation in stag hunt games. Surprisingly, defective\nAAs, rather than cooperative AAs, promote complete dominance of cooperation in\nsnowdrift games. Meanwhile, in scenarios with weak imitation strength,\ncooperative AAs are able to maintain or even promote cooperation in all these\ngames. Additionally, the results obtained from structured populations also\nimply that the effectiveness of AAs in promoting cooperation can be maximized\nby carefully considering their design and application in a given context.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Rumor propagation with heterogeneous transmission in social networks: Rumor models consider that information transmission occurs with the same\nprobability between each pair of nodes. However, this assumption is not\nobserved in social networks, which contain influential spreaders. To overcome\nthis limitation, we assume that central individuals have a higher capacity of\nconvincing their neighbors than peripheral subjects. By extensive numerical\nsimulations we find that the spreading is improved in scale-free networks when\nthe transmission probability is proportional to PageRank, degree, and\nbetweenness centrality. In addition, the results suggest that the spreading can\nbe controlled by adjusting the transmission probabilities of the most central\nnodes. Our results provide a conceptual framework for understanding the\ninterplay between rumor propagation and heterogeneous transmission in social\nnetworks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Quantitative patterns in drone wars: Attacks by drones (i.e., unmanned combat air vehicles) continue to generate\nheated political and ethical debates. Here we examine the quantitative nature\nof drone attacks, focusing on how their intensity and frequency compare with\nthat of other forms of human conflict. Instead of the power-law distribution\nfound recently for insurgent and terrorist attacks, the severity of attacks is\nmore akin to lognormal and exponential distributions, suggesting that the\ndynamics underlying drone attacks lie beyond these other forms of human\nconflict. We find that the pattern in the timing of attacks is consistent with\none side having almost complete control, an important if expected result. We\nshow that these novel features can be reproduced and understood using a\ngenerative mathematical model in which resource allocation to the dominant side\nis regulated through a feedback loop.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The role of inflexible minorities in the breaking of democratic opinion\n  dynamics: We study the effect of inflexible agents on two state opinion dynamics. The\nmodel operates via repeated local updates of random grouping of agents. While\nfloater agents do eventually flip their opinion to follow the local majority,\ninflexible agents keep their opinion always unchanged. It is a quenched\nindividual opinion. In the bare model (no inflexibles), a separator at 50%\ndrives the dynamics towards either one of two pure attractors, each associated\nwith a full polarization along one of the opinions. The initial majority wins.\nThe existence of inflexibles for only one of the two opinions is found to shift\nthe separator at a lower value than 50% in favor of that side. Moreover it\ncreates an incompressible minority around the inflexibles, one of the pure\nattractors becoming a mixed phase attractor. In addition above a threshold of\n17% inflexibles make their side sure of winning whatever the initial conditions\nare. The inflexible minority wins. An equal presence of inflexibles on both\nsides restores the balanced dynamics with again a separator at 50% and now two\nmixed phase attractors on each side. Nevertheless, beyond 25% the dynamics is\nreversed with a unique attractor at a fifty-fifty stable equilibrium. But a\nvery small advantage in inflexibles results in a decisive lowering of the\nseparator at the advantage of the corresponding opinion. A few percent\nadvantage does guarantee to become majority with one single attractor. The\nmodel is solved exhaustedly for groups of size 3.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Investigating the Empirical Existence of Static User Equilibrium: The Static User Equilibrium is a powerful framework for the theoretical study\nof traffic. Despite the restricting assumption of stationary flows that\nintuitively limit its application to real traffic systems, many operational\nmodels implementing it are still used without an empirical validation of the\nexistence of the equilibrium. We investigate its existence on a traffic dataset\nof three months for the region of Paris, FR. The implementation of an\napplication for interactive spatio-temporal data exploration allows to\nhypothesize a high spatial and temporal heterogeneity, and to guide further\nquantitative work. The assumption of locally stationary flows is invalidated in\na first approximation by empirical results, as shown by a strong spatial and\ntemporal variability in shortest paths and in network topological measures such\nas betweenness centrality. Furthermore, the behavior of spatial autocorrelation\nindex of congestion patterns at different spatial ranges suggest a chaotic\nevolution at the local scale, especially during peak hours. We finally discuss\nthe implications of these empirical findings and describe further possible\ndevelopments based on the estimation of Lyapunov dynamical stability of traffic\nflows.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Models of the World human population growth- critical analysis: We analyze mathematical models of the global human population growth and\ncompare them to actual dynamics of the world population and of the world\nsurplus product. We consider a possibility that the so-called world's\ndemographic transition is not a dynamic crossover but a phase transition that\naffects all aspects of our life.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Network-Based Prediction of the 2019-nCoV Epidemic Outbreak in the\n  Chinese Province Hubei: At the moment of writing (12 February, 2020), the future evolution of the\n2019-nCoV virus is unclear. Predictions of the further course of the epidemic\nare decisive to deploy targeted disease control measures. We consider a\nnetwork-based model to describe the 2019-nCoV epidemic in the Hubei province.\nThe network is composed of the cities in Hubei and their interactions (e.g.,\ntraffic flow). However, the precise interactions between cities is unknown and\nmust be inferred from observing the epidemic. We propose a network-based method\nto predict the future prevalence of the 2019-nCoV virus in every city. Our\nresults indicate that network-based modelling is beneficial for an accurate\nforecast of the epidemic outbreak.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Simple Quantum Model of Learning Explains the Yerkes-Dodson Law in\n  Psychology: We propose the simple model of learning based on which we derive and explain\nthe Yerkes-Dodson law - one of the oldest laws of experimental psychology. The\napproach uses some ideas of quantum theory of open systems (QTOS) and develops\nthe method of statistical description of psychological systems that was\nproposed by author earlier.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Measuring social dynamics in a massive multiplayer online game: Quantification of human group-behavior has so far defied an empirical,\nfalsifiable approach. This is due to tremendous difficulties in data\nacquisition of social systems. Massive multiplayer online games (MMOG) provide\na fascinating new way of observing hundreds of thousands of simultaneously\nsocially interacting individuals engaged in virtual economic activities. We\nhave compiled a data set consisting of practically all actions of all players\nover a period of three years from a MMOG played by 300,000 people. This\nlarge-scale data set of a socio-economic unit contains all social and economic\ndata from a single and coherent source. Players have to generate a virtual\nincome through economic activities to `survive' and are typically engaged in a\nmultitude of social activities offered within the game. Our analysis of\nhigh-frequency log files focuses on three types of social networks, and tests a\nseries of social-dynamics hypotheses. In particular we study the structure and\ndynamics of friend-, enemy- and communication networks. We find striking\ndifferences in topological structure between positive (friend) and negative\n(enemy) tie networks. All networks confirm the recently observed phenomenon of\nnetwork densification. We propose two approximate social laws in communication\nnetworks, the first expressing betweenness centrality as the inverse square of\nthe overlap, the second relating communication strength to the cube of the\noverlap. These empirical laws provide strong quantitative evidence for the Weak\nties hypothesis of Granovetter. Further, the analysis of triad significance\nprofiles validates well-established assertions from social balance theory. We\nfind overrepresentation (underrepresentation) of complete (incomplete) triads\nin networks of positive ties, and vice versa for networks of negative ties...",
        "category": "physics_soc-ph"
    },
    {
        "text": "Behavioral and Network Origins of Wealth Inequality: Insights from a\n  Virtual World: Almost universally, wealth is not distributed uniformly within societies or\neconomies. Even though wealth data have been collected in various forms for\ncenturies, the origins for the observed wealth-disparity and social inequality\nare not yet fully understood. Especially the impact and connections of human\nbehavior on wealth could so far not be inferred from data. Here we study wealth\ndata from the virtual economy of the massive multiplayer online game (MMOG)\nPardus. This data not only contains every player's wealth at every point in\ntime, but also all actions of every player over a timespan of almost a decade.\nWe find that wealth distributions in the virtual world are very similar to\nthose in western countries. In particular we find an approximate exponential\nfor low wealth and a power-law tail. The Gini index is found to be $g=0.65$,\nwhich is close to the indices of many Western countries. We find that\nwealth-increase rates depend on the time when players entered the game. Players\nthat entered the game early on tend to have remarkably higher wealth-increase\nrates than those who joined later. Studying the players' positions within their\nsocial networks, we find that the local position in the trade network is most\nrelevant for wealth. Wealthy people have high in- and out-degree in the trade\nnetwork, relatively low nearest-neighbor degree and a low clustering\ncoefficient. Wealthy players have many mutual friendships and are socially well\nrespected by others, but spend more time on business than on socializing. We\nfind that players that are not organized within social groups with at least\nthree members are significantly poorer on average. We observe that high\n`political' status and high wealth go hand in hand. Wealthy players have few\npersonal enemies, but show animosity towards players that behave as public\nenemies.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Heterogenous scaling in interevent time of on-line bookmarking: In this paper, we study the statistical properties of bookmarking behaviors\nin Delicious.com. We find that the interevent time distributions of bookmarking\ndecays powerlike as interevent time increases at both individual and population\nlevel. Remarkably, we observe a significant change in the exponent when\ninterevent time increases from intra-day to inter-day range. In addition,\ndependence of exponent on individual Activity is found to be different in the\ntwo ranges. These results suggests that mechanisms driving human actions are\ndifferent in intra- and inter-day range. Instead of monotonically increasing\nwith Activity, we find that inter-day exponent peaks at value around 3. We\nfurther show that less active users are more likely to resemble poisson process\nin bookmarking. Based on the temporal-preference model, preliminary\nexplanations for this dependence have been given . Finally, a universal\nbehavior in inter-day scale is observed by considering the rescaled variable.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Spatial changes in park visitation at the onset of the pandemic: The COVID-19 pandemic disrupted the mobility patterns of a majority of\nAmericans beginning in March 2020. Despite the beneficial, socially distanced\nactivity offered by outdoor recreation, confusing and contradictory public\nhealth messaging complicated access to natural spaces. Working with a dataset\ncomprising the locations of roughly 50 million distinct mobile devices in 2019\nand 2020, we analyze weekly visitation patterns for 8,135 parks across the\nUnited States. Using Bayesian inference, we identify regions that experienced a\nsubstantial change in visitation in the first few weeks of the pandemic. We\nfind that regions that did not exhibit a change were likely to have smaller\npopulations, and to have voted more republican than democrat in the 2020\nelections. Our study contributes to a growing body of literature using passive\nobservations to explore who benefits from access to nature.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Optimal control in opinion dynamics models: towards a unified framework: Understanding how individuals change their opinions is essential to mitigate\nthe harmful effect of fake news and stop opinion polarization. Building upon an\nextremely flexible opinion dynamics model that can capture the key microscopic\nmechanisms of social influence, the current paper elaborates a framework to\nfind optimal control, which, thus, can be applied to a broad set of opinion\ndynamics settings. Using a combination of theoretical and computational\napproaches, I characterize the properties of optimal control and demonstrates\nhow the elaborated framework can be applied to specific examples, which cover\nthe classical situations that garner remarkable attention in the literature on\nopinion dynamics models: assimilative influence, bounded confidence, and\ndissimilative influence.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Short-term predictions of country-specific Covid-19 infection rates\n  based on power law scaling exponents: The number of corona virus (COVID-19) infections grows worldwide. In order to\ncreate short term predictions to prepare for the extent of the global pandemic\nwe analyze infection data from the top 25 affected countries. It is shown that\nall country-specific infection rates follow a power law growth behavior and the\nscaling exponents per country are calculated. We find two different growth\npatterns: either steady power law growth from the very beginning with moderate\nscaling exponents of 3-5 or explosive power law growth with dramatic scaling\nexponents of 8-11. In the case of the USA we even find an exponent of 16.59. By\nmeans of data analysis we confirm that instituting strict measures of\nlock-downs combined with a strong adherence by the population are effective\nmeans to bring the growth rates down. While many countries have instituted\nmeasures there are only three countries (Denmark, Norway, and South Korea) so\nfar where such lock-downs led to a significant reduction of the growth rate. In\nthe case of Denmark we calculate the reduction of the scaling exponents to move\nfrom 6.82 to 1.47.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A game theoretic model of wealth distribution: In this work we consider an agent based model in order to study the wealth\ndistribution problem where the interchange is determined with a symmetric zero\nsum game. Simultaneously, the agents update their way of play trying to learn\nthe optimal one. Here, the agents use mixed strategies. We study this model\nusing both simulations and theoretical tools. We derive the equations for the\nlearning mechanism, and we show that the mean strategy of the population\nsatisfies an equation close to the classical replicator equation.\n  Concerning the wealth distribution, there are two interesting situations\ndepending on the equilibrium of the game. If the equilibrium is a pure\nstrategy, the wealth distribution is fixed after some transient time, and those\nplayers which are close to optimal strategy are richer. When the game has an\nequilibrium in mixed strategies, the stationary wealth distribution is close to\na Gamma distribution with variance depending on the coefficients of the game\nmatrix. We compute theoretically their second moment in this case.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Critical Network Cascades with Re-excitable nodes: Why tree-like\n  approximations usually work, when they breakdown, and how to correct them: Network science is a rapidly expanding field, with a large and growing body\nof work on network-based dynamical processes. Most theoretical results in this\narea rely on the so-called \\emph{locally tree-like approximation}. This is,\nhowever, usually an `uncontrolled' approximation, in the sense that the\nmagnitudes of the error are typically unknown, although numerical results show\nthat this error is often surprisingly small. In this paper, we place this\napproximation on more rigorous footing by calculating the magnitude of\ndeviations away from tree-based theories in the context of discrete-time\ncritical network cascades with re-excitable nodes. We discuss the conditions\nunder which tree-like approximations give good results for calculating network\ncriticality, and also explain the reasons for deviation from this\napproximation, in terms of the density of certain kinds of network motifs.\nUsing this understanding, we derive results for network criticality that apply\nto general networks that explicitly do not satisfy the locally tree-like\napproximation. In particular, we focus on the bi-parallel motif, the smallest\nmotif relevant to the failure of a tree-based theory in this context, and we\nderive the corrections due to such motifs on the conditions for criticality. We\nverify our claims on computer-generated networks, and we confirm that our\ntheory accurately predicts the observed deviations from criticality. Using our\ntheory, we explain why numerical simulations often show that deviations from a\ntree-based theory are surprisingly small. More specifically, we show that these\ndeviations are negligible for networks whose average degree is even modestly\nlarge compared to one, justifying why tree-based theories appear to work well\nfor most real-world networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Exploration of the effects of epidemics on the regional socio-economics:\n  a modelling approach: Pandemics, in addition to affecting the health of populations, can have huge\nimpacts on their social and economic behavior. These factors, on the other\nhand, have the potential to feed back to and influence the disease spreading.\nIt is important to systematically study these interrelations, to determine\nwhich ones have significant effects, and whether the effects are adverse or\nbeneficial. Our recently developed epidemic model with agent-based and\ngeographical elements is used in this study for such a purpose. We perform an\nextensive parameter space exploration of the socio-economic part of the model,\nincluding factors like the attitudes (called values) of the agents towards the\ndisease spreading, health, economic situation, and regulations by government\nagents. We search for prominent patterns from the resulting simulated data\nusing basic classification tools, namely self-organizing maps and principal\ncomponent analysis. We seek to isolate the most important value parameters of\nthe population and government agents influencing the disease spreading speed\nand patterns, and monitor different quantities of the model output, such as\ninfection rates, the propagation speed of the epidemic, economic activity,\ngovernment regulations, and the compliance of population. Out of these, the\nones describing the epidemic spreading were resulting in the most distinctive\nclustering of the data, and they were selected as the basis of the remaining\nanalysis. We relate the found clusters to three distinct types of disease\nspreading: wave-like, chaotic, and transitional spreading patterns. The most\nimportant value parameter contributing to phase changes between these phases\nwas found to be the compliance of the population agents towards the government\nregulations.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Robustness of community structure to node removal: The identification of modular structures is essential for characterizing real\nnetworks formed by a mesoscopic level of organization where clusters contain\nnodes with a high internal degree of connectivity. Many methods have been\ndeveloped to unveil community structures, but only a few studies have probed\ntheir suitability in incomplete networks. Here we assess the accuracy of\ncommunity detection techniques in incomplete networks generated in sampling\nprocesses. We show that the walktrap and fast greedy algorithms are highly\naccurate for detecting the modular structure of incomplete complex networks\neven if many of their nodes are removed. Furthermore, we implemented an\napproach that improved the time performance of the walktrap and fast greedy\nalgorithms, while retaining the accuracy rate in identifying the community\nmembership of nodes. Taken together our results show that this new approach can\nbe applied to speed up virtually any community detection method in dense\ncomplex networks, as it is the case of similarity networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Control Distance and Energy Scaling of Complex Networks: It has recently been shown that the average energy required to control a\nsubset of nodes in a complex network scales exponentially with the cardinality\nof the subset. While the mean scales exponentially, the variance of the control\nenergy over different subsets of nodes is large and has as of yet not been\nexplained. Here, we provide an explanation of the large variance as a result of\nboth the length of the path that connects control inputs to the target nodes\nand the redundancy of paths of shortest length. Our first result provides an\nexact upper bound of the control energy as a function of path length between\ndriver node and target node along an infinite path graph. We also show that the\nenergy estimation is still very accurate even when finite size effects are\ntaken into account. Our second result refines the upper bound that takes into\naccount not only the length of the path, but also the redundancy of paths. We\nshow that it improves the upper bound approximation by an order of magnitude or\nmore. Finally, we lay out the foundations for a more accurate estimation of the\ncontrol energy for the multi-target and multi-driver problem.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Impacts of nature deprivations during the COVID-19 pandemic: A pre-post\n  comparison: Nature provides a myriad of intangible and non-material services to people.\nHowever, urbanites are increasingly disconnected from the natural world. The\nconsequences of this progressive disconnection from nature remain difficult to\nmeasure as this process is slow and long-term monitoring or large-scale\nmanipulation on nature experiences are scarce. Measures to contain the spread\nof the recent covid-19 pandemic (i.e., lockdowns) have potentially reduced or\neven suppressed nature experiences in cities. This situation provided an\nopportunity for conducting a longitudinal study that can serve as a sort of\nnatural experiment to quantify the effects of nature deprivation on\nindividuals' health, well-being and relationship to nature. We collected data\non these variables from the same individuals inhabiting a large metropolis (Tel\nAviv, Israel) twice, in 2018 (before) and during the lockdown in 2020. Our\nresults confirmed that frequency, duration and quality of nature interactions\ndropped during the lockdown, while environmental attitudes and affinity towards\nnature remained similar. This was particularly true for people living in the\nleast green neighborhoods, where a significant decrease in personal and social\nwell-being was also found. Finally, affinity towards nature influenced\nwell-being through nature experiences in 2018. The mediation effect was not\nsignificant in 2020, probably due to the decrease in nature experiences during\nthe lockdown, but the direct relationship between affinity towards nature and\nwell-being remained strong. These results provide insights into the means\nrequired to align the public health and conservation agendas to safeguard\nurbanites' health and well-being during a pandemic and mitigate the\nbiodiversity crisis.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Multistable binary decision making on networks: We propose a simple model for a binary decision making process on a graph,\nmotivated by modeling social decision making with cooperative individuals. The\nmodel is similar to a random field Ising model or fiber bundle model, but with\nkey differences on heterogeneous networks. For many types of disorder and\ninteractions between the nodes, we predict discontinuous phase transitions with\nmean field theory which are largely independent of network structure. We show\nhow these phase transitions can also be understood by studying microscopic\navalanches, and describe how network structure enhances fluctuations in the\ndistribution of avalanches. We suggest theoretically the existence of a\n\"glassy\" spectrum of equilibria associated with a typical phase, even on\ninfinite graphs, so long as the first moment of the degree distribution is\nfinite. This behavior implies that the model is robust against noise below a\ncertain scale, and also that phase transitions can switch from discontinuous to\ncontinuous on networks with too few edges. Numerical simulations suggest that\nour theory is accurate.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Modeling the emergence of universality in color naming patterns: The empirical evidence that human color categorization exhibits some\nuniversal patterns beyond superficial discrepancies across different cultures\nis a major breakthrough in cognitive science. As observed in the World Color\nSurvey (WCS), indeed, any two groups of individuals develop quite different\ncategorization patterns, but some universal properties can be identified by a\nstatistical analysis over a large number of populations. Here, we reproduce the\nWCS in a numerical model in which different populations develop independently\ntheir own categorization systems by playing elementary language games. We find\nthat a simple perceptual constraint shared by all humans, namely the human Just\nNoticeable Difference (JND), is sufficient to trigger the emergence of\nuniversal patterns that unconstrained cultural interaction fails to produce. We\ntest the results of our experiment against real data by performing the same\nstatistical analysis proposed to quantify the universal tendencies shown in the\nWCS [Kay P and Regier T. (2003) Proc. Natl. Acad. Sci. USA 100: 9085-9089], and\nobtain an excellent quantitative agreement. This work confirms that synthetic\nmodeling has nowadays reached the maturity to contribute significantly to the\nongoing debate in cognitive science.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The strength and weakness of disease-induced herd immunity: When a fraction of a population becomes immune to an infectious disease, the\nrisk of widespread outbreaks decreases non-linearly as a result of the\ncollective protection known as herd immunity. Typically, immunity to a disease\ncan be acquired through natural infection or vaccination. It has been argued\nthat natural infection in a heterogeneous population may induce a stronger herd\nimmunity effect than homogeneous vaccination, because the early stages of\ntransmission would primarily affect highly interactive individuals, leading to\nmore efficient suppression of subsequent infections. In this study, we provide\na comprehensive assessment of the herd immunity effect by analyzing the\nbehavior of the susceptible-infected-recovered (SIR) model on static contact\nnetworks. We find that the effectiveness of disease-induced herd immunity is\nshaped by two competing mechanisms: While it exploits degree heterogeneity in\nthe contact network to efficiently target highly connected individuals for\nimmunity, it also introduces mixing heterogeneity between immune and\nsusceptible individuals, weakening herd immunity. By controlling the degree of\nspatial embeddedness of the contact network structure, we find that spatial\nstructure generally makes herd immunity stronger and also enhances the\nadvantage of the herd immunity effect induced by random immunization over the\ndisease-induced one in networks with low degree heterogeneity. These findings\nprovide valuable insights into the complexity of herd immunity and have\nimplications for designing effective disease control strategies.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Learning to flock through reinforcement: Flocks of birds, schools of fish, insects swarms are examples of coordinated\nmotion of a group that arises spontaneously from the action of many\nindividuals. Here, we study flocking behavior from the viewpoint of multi-agent\nreinforcement learning. In this setting, a learning agent tries to keep contact\nwith the group using as sensory input the velocity of its neighbors. This goal\nis pursued by each learning individual by exerting a limited control on its own\ndirection of motion. By means of standard reinforcement learning algorithms we\nshow that: i) a learning agent exposed to a group of teachers, i.e. hard-wired\nflocking agents, learns to follow them, and ii) that in the absence of\nteachers, a group of independently learning agents evolves towards a state\nwhere each agent knows how to flock. In both scenarios, i) and ii), the\nemergent policy (or navigation strategy) corresponds to the polar velocity\nalignment mechanism of the well-known Vicsek model. These results show that a)\nsuch a velocity alignment may have naturally evolved as an adaptive behavior\nthat aims at minimizing the rate of neighbor loss, and b) prove that this\nalignment does not only favor (local) polar order, but it corresponds to best\npolicy/strategy to keep group cohesion when the sensory input is limited to the\nvelocity of neighboring agents. In short, to stay together, steer together.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Human Activity in the Web: The recent information technology revolution has enabled the analysis and\nprocessing of large-scale datasets describing human activities. The main source\nof data is represented by the Web, where humans generally use to spend a\nrelevant part of their day. Here we study three large datasets containing the\ninformation about Web human activities in different contexts. We study in\ndetails inter-event and waiting time statistics. In both cases, the number of\nsubsequent operations which differ by tau units of time decays power-like as\ntau increases. We use non-parametric statistical tests in order to estimate the\nsignificance level of reliability of global distributions to describe activity\npatterns of single users. Global inter-event time probability distributions are\nnot representative for the behavior of single users: the shape of single\nusers'inter-event distributions is strongly influenced by the total number of\noperations performed by the users and distributions of the total number of\noperations performed by users are heterogeneous. A universal behavior can be\nanyway found by suppressing the intrinsic dependence of the global probability\ndistribution on the activity of the users. This suppression can be performed by\nsimply dividing the inter-event times with their average values. Differently,\nwaiting time probability distributions seem to be independent of the activity\nof users and global probability distributions are able to significantly\nrepresent the replying activity patterns of single users.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Do interactions among unequal agents undermine those of low status?: We consider a recent model in which agents hold opinions about each other and\ninfluence each other's opinions during random pair interactions. When the\nopinions are initially close, on the short term, all the opinions tend to\nincrease over time. On the contrary, when the opinions are initially very\nunequal, the opinions about agents of high status increase, but the opinions\nabout agents of low status tend to stagnate without gossip and to decrease with\ngossip. We derive a moment approximation of the average opinion changes that\nexplains these observations.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A Data-Driven and Integrated Evaluation of Area-wide Impacts of Double\n  Parking Using Macroscopic and Microscopic Models: Double parking that often negatively affects traffic operations and safety is\nnot a new phenomenon on urban streets. This study proposes a novel data-driven\nintegrated framework for estimating the actual frequency of double parking so\nthat both microscopic and macroscopic models can be utilized to quantify\narea-wide impacts in the presence of double parking. The findings of the study\ncan provide transportation agencies with useful insights on identifying\nlocations that will experience the greatest benefits by removing problematic\ndouble parking. As a result, various parking enforcement and management\nstrategies can be planned more effectively.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Multifractal analysis of weighted networks by a modified sandbox\n  algorithm: Complex networks have attracted growing attention in many fields. As a\ngeneralization of fractal analysis, multifractal analysis (MFA) is a useful way\nto systematically describe the spatial heterogeneity of both theoretical and\nexperimental fractal patterns. Some algorithms for MFA of unweighted complex\nnetworks have been proposed in the past a few years, including the sandbox (SB)\nalgorithm recently employed by our group. In this paper, a modified SB\nalgorithm (we call it SBw algorithm) is proposed for MFA of weighted\nnetworks.First, we use the SBw algorithm to study the multifractal property of\ntwo families of weighted fractal networks (WFNs): \"Sierpinski\" WFNs and \"Cantor\ndust\" WFNs. We also discuss how the fractal dimension and generalized fractal\ndimensions change with the edge-weights of the WFN. From the comparison between\nthe theoretical and numerical fractal dimensions of these networks, we can find\nthat the proposed SBw algorithm is efficient and feasible for MFA of weighted\nnetworks. Then, we apply the SBw algorithm to study multifractal properties of\nsome real weighted networks ---collaboration networks. It is found that the\nmultifractality exists in these weighted networks, and is affected by their\nedge-weights.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Hubs and Clusters in the Evolving U. S. Internal Migration Network: Most nations of the world periodically publish N x N origin-destination\ntables, recording the number of people who lived in geographic subdivision i at\ntime t and j at t+1. We have developed and widely applied to such national\ntables and other analogous (weighted, directed) socioeconomic networks, a\ntwo-stage--double-standardization and (strong component) hierarchical\nclustering--procedure. Previous applications of this methodology and related\nanalytical issues are discussed. Its use is illustrated in a large-scale study,\nemploying recorded United States internal migration flows between the 3,000+\ncounty-level units of the nation for the periods 1965-1970 and 1995-2000.\nProminent, important features--such as ''cosmopolitan hubs'' and ``functional\nregions''--are extracted from master dendrograms. The extent to which such\ncharacteristics have varied over the intervening thirty years is evaluated.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Vulnerability and Resilience of Social Engagement: Equilibrium Theory: Social networks of engagement sometimes dramatically collapse. A widely\nadopted paradigm to understand this catastrophe dynamics is the threshold model\nbut previous work only considered the irreversible K-core pruning process and\nthe resulting kinetic activity patterns. Here we study the network alliance\nproblem as a simplified model of social engagement by equilibrium statistical\nmechanics. Our theory reveals that the surviving kinetic alliances are\nout-of-equilibrium and atypical configurations which may become highly\nvulnerable to single-node-triggered cascading failures as they relax towards\nequilibrium. Our theory predicts that if the fraction of active nodes is beyond\na certain critical value, the equilibrium (typical) alliance configurations\ncould be protected from cascading failures by a simple least-effort local\nintervention strategy. We confirm these results by extensive Monte Carlo\nsimulations.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Global path preference and local response: A reward decomposition\n  approach for network path choice analysis in the presence of locally\n  perceived attributes: This study performs an attribute-level analysis of the global and local path\npreferences of network travelers. To this end, a reward decomposition approach\nis proposed and integrated into a link-based recursive (Markovian) path choice\nmodel. The approach decomposes the instantaneous reward function associated\nwith each state-action pair into the global utility, a function of attributes\nglobally perceived from anywhere in the network, and the local utility, a\nfunction of attributes that are only locally perceived from the current state.\nOnly the global utility then enters the value function of each state,\nrepresenting the future expected utility toward the destination. This\nglobal-local path choice model with decomposed reward functions allows us to\nanalyze to what extent and which attributes affect the global and local path\nchoices of agents. Moreover, unlike most adaptive path choice models, the\nproposed model can be estimated based on revealed path observations (without\nthe information of plans) and as efficiently as deterministic recursive path\nchoice models. The model was applied to the real pedestrian path choice\nobservations in an urban street network where the green view index was\nextracted as a visual street quality from Google Street View images. The result\nrevealed that pedestrians locally perceive and react to the visual street\nquality, rather than they have the pre-trip global perception on it.\nFurthermore, the simulation results using the estimated models suggested the\nimportance of location selection of interventions when policy-related\nattributes are only locally perceived by travelers.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Vaccination rates modulate the correlation between country latitude and\n  covid-19 surge date arising this autumn 2021: A clear linear correlation of covid-19 surge date with country latitude was\nobserved last autumn 2020, but not with temperature, nor with humidity,\npointing the seasonal vitamin D status decline as a contributor factor. Well\nvaccinated European countries are this autumn 2021 repeating explosive surges.\nWe aim to evaluate the potential link between surge date, country latitude and\npopulation vaccination rate. Only a weak correlation was observed this autumn.\nHowever, after shifting the country latitude towards the south proportionally\nto its vaccination rate, a clear linear correlation was retrieved. This\nobservation, joined to other ones, supports that vitamin D deficiency could\nreduce the vaccination efficiency. There is no objective benefit to hold a\nvitamin D deficiency. Thus, while European populations are undergoing the\nseasonal autumn-winter vitamin D status decline, it already makes sense to\nscreen the elder people vitamin D status and fix individual deficiencies.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Transportation Needs Assessment for Rural Communities: A Case of Pickens\n  County, Alabama: The lack of transportation options and limited public transit service in\nrural areas of the United States may negatively affect residents quality of\nlife by limiting employment, community engagement, education, and quality\nhealthcare opportunities. This study used a mixed method approach combining\nquantitative and qualitative research methods to conduct the transportation\nneeds assessment in Pickens County, Alabama. The study examined access to\nhealthcare, jobs, and other relevant resources across all population categories\nin the area, including vulnerable populations, such as senior citizens, people\nwith disabilities, and low-income households. Additionally, this study\ninvestigated public perceptions of and opinions about H.E.L.P. Inc., an\norigin-to-destination demand-response bus service for Pickens County residents.\nThe study findings may be helpful to stakeholders, policymakers, and\ntransportation services providers in the rural communities of the U.S., working\nto develop new approaches that would help meet the transportation needs of\nlocal communities.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Polemical Case Study of Opinion Dynamics:Patterns of Filter Bubbles in\n  Non-Consensus, Rewire Phenomena: In this paper, we will review some of the issues that have been raised by\nopinion dynamics theory to date. In particular, we conducted a hypothesis-based\nsimulation using a socio-physical approach regarding the filter bubble\nphenomenon that tends to occur under special conditions such as (1) Distance,\n(2) Time, and (3) Existence of strong opinion clusters in the barriers to\nconsensus building (4) Place where opinions are not influenced In particular,\nthis paper discusses the hypothesis and simulations of filter bubbles, in which\nopinions diverge or converge without reaching consensus under conditions in\nwhich non-consensus is likely to be emphasized. This article serves as an\nAppendix in \"Case Study On Opinion Dynamics In Dyadic Risk On Possibilities Of\nTrust-Distrust Model.\"",
        "category": "physics_soc-ph"
    },
    {
        "text": "Randomizing bipartite networks: the case of the World Trade Web: Within the last fifteen years, network theory has been successfully applied\nboth to natural sciences and to socioeconomic disciplines. In particular,\nbipartite networks have been recognized to provide a particularly insightful\nrepresentation of many systems, ranging from mutualistic networks in ecology to\ntrade networks in economy, whence the need of a pattern detection-oriented\nanalysis in order to identify statistically-significant structural properties.\nSuch an analysis rests upon the definition of suitable null models, i.e. upon\nthe choice of the portion of network structure to be preserved while\nrandomizing everything else. However, quite surprisingly, little work has been\ndone so far to define null models for real bipartite networks. The aim of the\npresent work is to fill this gap, extending a recently-proposed method to\nrandomize monopartite networks to bipartite networks. While the proposed\nformalism is perfectly general, we apply our method to the binary, undirected,\nbipartite representation of the World Trade Web, comparing the observed values\nof a number of structural quantities of interest with the expected ones,\ncalculated via our randomization procedure. Interestingly, the behavior of the\nWorld Trade Web in this new representation is strongly different from the\nmonopartite analogue, showing highly non-trivial patterns of self-organization.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Kuramoto dilemma alleviated by optimizing connectivity and rationality: Recently, Antonioni and Cardillo proposed a coevolutionary model based on the\nintertwining of oscillator synchronization and evolutionary game theory [Phys.\nRev. Lett. \\textbf{118}, 238301 (2017)], in which each Kuramoto oscillator can\ndecide whether to interact-or not-with its neighbors, and all oscillators can\nreceive some benefits from the local synchronization but those who choose to\ninteract must pay a cost. Oscillators are allowed to update their strategies\naccording to payoff difference, wherein the strategy of an oscillator who has\nobtained higher payoff is more likely to be followed. Utilizing this\ncoevolutionary model, we find that the global synchronization level reaches the\nhighest level when the average degree of the underlying interaction network is\nmoderate. We also study how synchronization is affected by the individual\nrationality in choosing strategy.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Power and the Pandemic: Exploring Global Changes in Electricity Demand\n  During COVID-19: Understanding how efforts to limit exposure to COVID-19 have altered\nelectricity demand provides insights not only into how dramatic restrictions\nshape electricity demand but also about future electricity use in a\npost-COVID-19 world. We develop a unified modeling framework to quantify and\ncompare electricity usage changes in 58 countries and regions around the world\nfrom January-May 2020. We find that daily electricity demand declined as much\nas 10% in April 2020 compared to modelled demand, controlling for weather,\nseasonal and temporal effects, but with significant variation. Clustering\ntechniques show that four impact groups capture systematic differences in\ntiming and depth of electricity usage changes, ranging from a mild decline of\n2% to an extreme decline of 26%. These groupings do not align with geography,\nwith almost every continent having at least one country or region that\nexperienced a dramatic reduction in demand and one that did not. Instead, we\nfind that such changes relate to government restrictions and mobility.\nGovernment restrictions have a non-linear effect on demand that generally\nsaturates at its most restrictive levels and sustains even as restrictions\nease. Mobility offers a sharper focus on electricity demand change with\nworkplace and residential mobility strongly linked to demand changes at the\ndaily level. Steep declines in electricity usage are associated with workday\nhourly load patterns that resemble pre-COVID weekend usage. Quantifying these\nimpacts is a crucial first step in understanding the impacts of crises like the\npandemic and the associated societal response on electricity demand.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A thermodynamic parallel of the Braess road-network paradox: We provide here a thermodynamic analog of the Braess road-network paradox\nwith irreversible engines working between reservoirs that are placed at\nvertices of the network. Paradoxes of different kinds reappear, emphasizing the\nspecialty of the network.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The Distance-Decay Function of Geographical Gravity Model: Power Law or\n  Exponential Law?: The distance-decay function of the geographical gravity model is originally\nan inverse power law, which suggests a scaling process in spatial interaction.\nHowever, the distance exponent of the model cannot be explained with the ideas\nfrom Euclidean geometry. This results in what is called dimension dilemma. In\nparticular, the gravity model based on power law could not be derived from\ngeneral principles by traditional ways. Consequently, a negative exponential\nfunction substituted for the inverse power function to serve for a\ndistance-decay function for the gravity model. However, the exponential-based\ngravity model goes against the first law of geography. This paper is devoted to\nsolve these kinds of problems by mathematical reasoning and empirical analysis.\nFirst, it can be proved that the distance exponent of the gravity model is\nessentially a fractal dimension. Thus the dimensional dilemma of the\npower-based gravity model can be resolved using the concepts from fractal\ngeometry. Second, the exponential function indicates locality and localization,\nwhich violates the basic principle of spatial interaction. The power function\nimplies action at a distance, which is the necessary condition of geographical\ngravitation. Third, the gravity model based on power law decay can be derived\nfrom the entropy- maximizing principle by introducing a proper postulate. The\nobservational data of China's cities and regions are employed to verify the\ntheoretical inferences, and the results support power-law distance decay. A\nconclusion can be reached that the preferred form of geographical gravity model\nis its original form, which is based on an inverse power law rather than a\nnegative exponential law.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Strategic tradeoffs in competitor dynamics on adaptive networks: Recent empirical work highlights the heterogeneity of social competitions\nsuch as political campaigns: proponents of some ideologies seek debate and\nconversation, others create echo chambers. While symmetric and static network\nstructure is typically used as a substrate to study such competitor dynamics,\nnetwork structure can instead be interpreted as a signature of the competitor\nstrategies, yielding competition dynamics on adaptive networks. Here we\ndemonstrate that tradeoffs between aggressiveness and defensiveness (i.e.,\ntargeting adversaries vs. targeting like-minded individuals) creates\nparadoxical behaviour such as non-transitive dynamics. And while there is an\noptimal strategy in a two competitor system, three competitor systems have no\nsuch solution; the introduction of extreme strategies can easily affect the\noutcome of a competition, even if the extreme strategies have no chance of\nwinning. Not only are these results reminiscent of classic paradoxical results\nfrom evolutionary game theory, but the structure of social networks created by\nour model can be mapped to particular forms of payoff matrices. Consequently,\nsocial structure can act as a measurable metric for social games which in turn\nallows us to provide a game theoretical perspective on online political\ndebates.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A bi-directional approach to comparing the modular structure of networks: Here we propose a new method to compare the modular structure of a pair of\nnode-aligned networks. The majority of current methods, such as normalized\nmutual information, compare two node partitions derived from a community\ndetection algorithm yet ignore the respective underlying network topologies.\nAddressing this gap, our method deploys a community detection quality function\nto assess the fit of each node partition with respect to the other network's\nconnectivity structure. Specifically, for two networks A and B, we project the\nnode partition of B onto the connectivity structure of A. By evaluating the fit\nof B's partition relative to A's own partition on network A (using a standard\nquality function), we quantify how well network A describes the modular\nstructure of B. Repeating this in the other direction, we obtain a\ntwo-dimensional distance measure, the bi-directional (BiDir) distance. The\nadvantages of our methodology are three-fold. First, it is adaptable to a wide\nclass of community detection algorithms that seek to optimize an objective\nfunction. Second, it takes into account the network structure, specifically the\nstrength of the connections within and between communities, and can thus\ncapture differences between networks with similar partitions but where one of\nthem might have a more defined or robust community structure. Third, it can\nalso identify cases in which dissimilar optimal partitions hide the fact that\nthe underlying community structure of both networks is relatively similar. We\nillustrate our method for a variety of community detection algorithms,\nincluding multi-resolution approaches, and a range of both simulated and real\nworld networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Collective dynamics of capacity-constrained ride-pooling fleets: Ride-pooling (or ride-sharing) services combine trips of multiple customers\nalong similar routes into a single vehicle. The collective dynamics of the\nfleet of ride-pooling vehicles fundamentally underlies the efficiency of these\nservices. In simplified models, the common features of these dynamics give rise\nto scaling laws of the efficiency that are valid across a wide range of street\nnetworks and demand settings. However, it is unclear how constraints of the\nvehicle fleet impact such scaling laws. Here, we map the collective dynamics of\ncapacity-constrained ride-pooling fleets to services with unlimited passenger\ncapacity and identify an effective fleet size of available vehicles as the\nrelevant scaling parameter characterizing the dynamics. Exploiting this\nmapping, we generalize the scaling laws of ride-pooling efficiency to\ncapacity-constrained fleets. We approximate the scaling function with a\nqueueing theoretical analysis of the dynamics in a minimal model system,\nthereby enabling mean-field predictions of required fleet sizes in more complex\nsettings. These results may help to transfer insights from existing\nride-pooling services to new settings or service locations.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The case for caution in predicting scientists' future impact: We stress-test the career predictability model proposed by Acuna et al.\n[Nature 489, 201-202 2012] by applying their model to a longitudinal career\ndata set of 100 Assistant professors in physics, two from each of the top 50\nphysics departments in the US. The Acuna model claims to predict h(t+\\Delta t),\na scientist's h-index \\Delta t years into the future, using a linear\ncombination of 5 cumulative career measures taken at career age t. Here we\ninvestigate how the \"predictability\" depends on the aggregation of career data\nacross multiple age cohorts. We confirm that the Acuna model does a respectable\njob of predicting h(t+\\Delta t) up to roughly 6 years into the future when\naggregating all age cohorts together. However, when calculated using subsets of\nspecific age cohorts (e.g. using data for only t=3), we find that the model's\npredictive power significantly decreases, especially when applied to early\ncareer years. For young careers, the model does a much worse job of predicting\nfuture impact, and hence, exposes a serious limitation. The limitation is\nparticularly concerning as early career decisions make up a significant\nportion, if not the majority, of cases where quantitative approaches are likely\nto be applied.",
        "category": "physics_soc-ph"
    },
    {
        "text": "The application of mixed-use measures at the pedestrian-scale: Mixed-use urbanism affords access to diverse assortments of land-uses within\na pedestrian-accessible context. It confers advantages such as reductions to\ndriving, air pollution, and Body Mass Index with associated increases in active\ntransportation and improvements to health. However, whereas mixed-use urbanism\nis clearly beneficial, methods for measuring and assessing the presence of\nmixed-uses at a granular level of analysis remain murkier. This work\ndemonstrates techniques for gauging mixed-uses in more spatially precise terms\nconcurring more readily with an urbanist's conception of pedestrian-accessible\nmixed-uses. It does so through the use of the cityseer-api Python package,\nwhich facilitates the use of spatially granular land-use classification data\nassigned to adjacent street edges and then aggregated dynamically, with\ndistances measured from each point of analysis to each accessible land-use\nwhile taking the direction of approach into account. It is argued that Hill\nNumbers is a suitable measure of diversity because it can mirror the intent of\ntraditional indices while behaving more intuitively. Further, distance-weighted\nformulations of Hill diversity can be applied with spatial impedances, thus\nconferring a particularly spatially nuanced gauge of local access to\nmixed-uses. These methods and indices are demonstrated for Greater London with\nobservations correlated to Principal Component Analysis derived from a range of\nland-use accessibilities measured from the same locations and for the same\npoint-of-interest dataset. The Hill diversity measures, particularly the\ndistance-weighted formulations, offer the most robust correlations for both\nexpansive mixed-use districts and more local 'high-street' mixes of uses while\nyielding the most intuitive and spatially precise behaviour in the accompanying\nplots.",
        "category": "physics_soc-ph"
    },
    {
        "text": "High precision analysis of unidirectional pedestrian flow within the\n  Hermes Project: The aim of the Hermes project is the development of a computer simulation\nbased on evacuation assistant to support security services in case of emergency\nin complex buildings and thus to improve safety at mass events. One goal of the\nproject is to build and to calibrate models for pedestrian dynamics\nspecifically designed for forecasting the emergency egress of large crowds\nfaster than real-time. In this contribution, we give an overview of the project\nand its experimental results of unidirectional flow in a corridor. Trajectories\nof all pedestrians are analyzed with different measurement methods. The data\nwill be used for model calibration.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Collision-free speed model for pedestrian dynamics: We propose in this paper a minimal speed-based pedestrian model for which\nparticle dynamics are intrinsically collision-free. The speed model is an\noptimal velocity function depending on the agent length (i.e.\\ particle\ndiameter), maximum speed and time gap parameters. The direction model is a\nweighted sum of exponential repulsion from the neighbors, calibrated by the\nrepulsion rate and distance. The model's main features like the reproduction of\nempirical phenomena are analysed by simulation. We point out that phenomena of\nself-organisation observable in force-based models and field studies can be\nreproduced by the collision-free model with low computational effort.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Analytical and numerical treatment of continuous ageing in the voter\n  model: The conventional voter model is modified so that an agent's switching rate\ndepends on the `age' of the agent, that is, the time since the agent last\nswitched opinion. In contrast to previous work, age is continuous in the\npresent model. We show how the resulting individual-based system with\nnon-Markovian dynamics and concentration-dependent rates can be handled both\ncomputationally and analytically. Lewis' thinning algorithm can be modified in\norder to provide an efficient simulation method. Analytically, we demonstrate\nhow the asymptotic approach to an absorbing state (consensus) can be deduced.\nWe discuss three special cases of the age dependent switching rate: one in\nwhich the concentration of voters can be approximated by a fractional\ndifferential equation, another for which the approach to consensus is\nexponential in time, and a third case in which the system reaches a frozen\nstate instead of consensus. Finally, we include the effects of spontaneous\nchange of opinion, i.e., we study a noisy voter model with continuous ageing.\nWe demonstrate that this can give rise to a continuous transition between\ncoexistence and consensus phases. We also show how the stationary probability\ndistribution can be approximated, despite the fact that the system cannot be\ndescribed by a conventional master equation.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Cascading Behavior in Large Blog Graphs: How do blogs cite and influence each other? How do such links evolve? Does\nthe popularity of old blog posts drop exponentially with time? These are some\nof the questions that we address in this work. Our goal is to build a model\nthat generates realistic cascades, so that it can help us with link prediction\nand outlier detection.\n  Blogs (weblogs) have become an important medium of information because of\ntheir timely publication, ease of use, and wide availability. In fact, they\noften make headlines, by discussing and discovering evidence about political\nevents and facts. Often blogs link to one another, creating a publicly\navailable record of how information and influence spreads through an underlying\nsocial network. Aggregating links from several blog posts creates a directed\ngraph which we analyze to discover the patterns of information propagation in\nblogspace, and thereby understand the underlying social network. Not only are\nblogs interesting on their own merit, but our analysis also sheds light on how\nrumors, viruses, and ideas propagate over social and computer networks.\n  Here we report some surprising findings of the blog linking and information\npropagation structure, after we analyzed one of the largest available datasets,\nwith 45,000 blogs and ~ 2.2 million blog-postings. Our analysis also sheds\nlight on how rumors, viruses, and ideas propagate over social and computer\nnetworks. We also present a simple model that mimics the spread of information\non the blogosphere, and produces information cascades very similar to those\nfound in real life.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Socio-economic utility and chemical potential: In statistical physics, the conservation of particle number results in the\nequalization of the chemical potential throughout a system at equilibrium. In\ncontrast, the homogeneity of utility in socio-economic models is usually\nthought to rely on the competition between individuals, leading to Nash\nequilibrium. We show that both views can be reconciled by introducing a notion\nof chemical potential in a wide class of socio-economic models, and by relating\nit in a direct way to the equilibrium value of the utility. This approach also\nallows the dependence of utility across the system to be determined when agents\ntake decisions in a probabilistic way. Numerical simulations of a urban\neconomic model also suggest that our result is valid beyond the initially\nconsidered class of solvable models.",
        "category": "physics_soc-ph"
    },
    {
        "text": "A complex network approach to cloud computing: Cloud computing has become an important means to speed up computing. One\nproblem influencing heavily the performance of such systems is the choice of\nnodes as servers responsible for executing the users' tasks. In this article we\nreport how complex networks can be used to model such a problem. More\nspecifically, we investigate the performance of the processing respectively to\ncloud systems underlain by Erdos-Renyi and Barabasi-Albert topology containing\ntwo servers. Cloud networks involving two communities not necessarily of the\nsame size are also considered in our analysis. The performance of each\nconfiguration is quantified in terms of two indices: the cost of communication\nbetween the user and the nearest server, and the balance of the distribution of\ntasks between the two servers. Regarding the latter index, the ER topology\nprovides better performance than the BA case for smaller average degrees and\nopposite behavior for larger average degrees. With respect to the cost, smaller\nvalues are found in the BA topology irrespective of the average degree. In\naddition, we also verified that it is easier to find good servers in the ER\nthan in BA. Surprisingly, balance and cost are not too much affected by the\npresence of communities. However, for a well-defined community network, we\nfound that it is important to assign each server to a different community so as\nto achieve better performance.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Evolving nature of human contact networks with its impact on epidemic\n  processes: Human contact networks are constituted by a multitude of individuals and\npairwise contacts among them. However, the dynamic nature, which generates the\nevolution of human contact networks, of contact patterns is not known yet.\nHere, we analyse three empirical datasets and identify two crucial mechanisms\nof the evolution of temporal human contact networks, i.e. the activity state\ntransition laws for an individual to be socially active, and the contact\nestablishment mechanism that active individuals adopt. We consider both of the\ntwo mechanisms to propose a temporal network model, named the memory driven\n(MD) model, of human contact networks. Then we study the susceptible-infected\n(SI) spreading processes on empirical human contact networks and four\ncorresponding temporal network models, and compare the full prevalence time of\nSI processes with various infection rates on the networks. The full prevalence\ntime of SI processes in the MD model is the same as that in real-world human\ncontact networks. Moreover, we find that the individual activity state\ntransition promotes the spreading process, while, the contact establishment of\nactive individuals suppress the prevalence. Apart from this, we observe that\neven a small percentage of individuals to explore new social ties is able to\ninduce an explosive spreading on networks. The proposed temporal network\nframework could help the further study of dynamic processes in temporal human\ncontact networks, and offer new insights to predict and control the diffusion\nprocesses on networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Network Dimensions in the Getty Provenance Index: In this article we make a case for a systematic application of complex\nnetwork science to study art market history and more general collection\ndynamics. We reveal social, temporal, spatial, and conceptual network\ndimensions, i.e. network node and link types, previously implicit in the Getty\nProvenance Index (GPI). As a pioneering art history database active since the\n1980s, the GPI provides online access to source material relevant for research\nin the history of collecting and art markets. Based on a subset of the GPI, we\ncharacterize an aggregate of more than 267,000 sales transactions connected to\nroughly 22,000 actors in four countries over 20 years at daily resolution from\n1801 to 1820. Striving towards a deeper understanding on multiple levels we\ndisambiguate social dynamics of buying, brokering, and selling, while observing\na general broadening of the market, where large collections are split into\nsmaller lots. Temporally, we find annual market cycles that are shifted by\ncountry and obviously favor international exchange. Spatially, we differentiate\nnear-monopolies from regions driven by competing sub-centers, while uncovering\nasymmetries of international market flux. Conceptually, we track dynamics of\nartist attribution that clearly behave like product categories in a very slow\nsupermarket. Taken together, we introduce a number of meaningful network\nperspectives dealing with historical art auction data, beyond the analysis of\nsocial networks within a single market region. The results presented here have\ninspired a Linked Open Data conversion of the GPI, which is currently in\nprocess and will allow further analysis by a broad set of researchers.",
        "category": "physics_soc-ph"
    },
    {
        "text": "How individuals learn to take turns: Emergence of alternating\n  cooperation in a congestion game and the prisoner's dilemma: In many social dilemmas, individuals tend to generate a situation with low\npayoffs instead of a system optimum (\"tragedy of the commons\"). Is the routing\nof traffic a similar problem? In order to address this question, we present\nexperimental results on humans playing a route choice game in a computer\nlaboratory, which allow one to study decision behavior in repeated games beyond\nthe Prisoner's Dilemma. We will focus on whether individuals manage to find a\ncooperative and fair solution compatible with the system-optimal road usage. We\nfind that individuals tend towards a user equilibrium with equal travel times\nin the beginning. However, after many iterations, they often establish a\ncoherent oscillatory behavior, as taking turns performs better than applying\npure or mixed strategies. The resulting behavior is fair and compatible with\nsystem-optimal road usage. In spite of the complex dynamics leading to\ncoordinated oscillations, we have identified mathematical relationships\nquantifying the observed transition process. Our main experimental discoveries\nfor 2- and 4-person games can be explained with a novel reinforcement learning\nmodel for an arbitrary number of persons, which is based on past experience and\ntrial-and-error behavior. Gains in the average payoff seem to be an important\ndriving force for the innovation of time-dependent response patterns, i.e. the\nevolution of more complex strategies. Our findings are relevant for decision\nsupport systems and routing in traffic or data networks.",
        "category": "physics_soc-ph"
    },
    {
        "text": "Tracing the Evolution of Physics on the Backbone of Citation Networks: Many innovations are inspired by past ideas in a non-trivial way. Tracing\nthese origins and identifying scientific branches is crucial for research\ninspirations. In this paper, we use citation relations to identify the\ndescendant chart, i.e. the family tree of research papers. Unlike other\nspanning trees which focus on cost or distance minimization, we make use of the\nnature of citations and identify the most important parent for each\npublication, leading to a tree-like backbone of the citation network. Measures\nare introduced to validate the backbone as the descendant chart. We show that\ncitation backbones can well characterize the hierarchical and fractal structure\nof scientific development, and lead to accurate classification of fields and\nsub-fields.",
        "category": "physics_soc-ph"
    }
]