pwc-archive/papers-with-abstracts huggingface dataset:
An insertable glucose sensor using a compact and cost-effective phosphorescence lifetime imager and machine learning
Authors: Artem Goncharov, Zoltan Gorocs, Ridhi Pradhan, Brian Ko, Ajmal Ajmal, Andres Rodriguez, David Baum, Marcell Veszpremi, Xilin Yang, Maxime Pindrys, Tianle Zheng, Oliver Wang, Jessica C. Ramella-Roman, Michael J. McShane, Aydogan Ozcan
Date: 2024-06-12 00:00:00

Optical continuous glucose monitoring (CGM) systems are emerging for personalized glucose management owing to their lower cost and prolonged durability compared to conventional electrochemical CGMs. Here, we report a computational CGM system, which integrates a biocompatible phosphorescence-based insertable biosensor and a custom-designed phosphorescence lifetime imager (PLI). This compact and cost-effective PLI is designed to capture phosphorescence lifetime images of an insertable sensor through the skin, where the lifetime of the emitted phosphorescence signal is modulated by the local concentration of glucose. Because this phosphorescence signal has a very long lifetime compared to tissue autofluorescence or excitation leakage processes, it completely bypasses these noise sources by measuring the sensor emission over several tens of microseconds after the excitation light is turned off. The lifetime images acquired through the skin are processed by neural network-based models for misalignment-tolerant inference of glucose levels, accurately revealing normal, low (hypoglycemia) and high (hyperglycemia) concentration ranges. Using a 1-mm thick skin phantom mimicking the optical properties of human skin, we performed in vitro testing of the PLI using glucose-spiked samples, yielding 88.8% inference accuracy, also showing resilience to random and unknown misalignments within a lateral distance of ~4.7 mm with respect to the position of the insertable sensor underneath the skin phantom. Furthermore, the PLI accurately identified larger lateral misalignments beyond 5 mm, prompting user intervention for re-alignment. The misalignment-resilient glucose concentration inference capability of this compact and cost-effective phosphorescence lifetime imager makes it an appealing wearable diagnostics tool for real-time tracking of glucose and other biomarkers.
Hyperbolic embedding of brain networks as a tool for epileptic seizures forecasting
Authors: Martin Guillemaud, Louis Cousyn, Vincent Navarro, Mario Chavez
Date: 2024-06-14 00:00:00
Tasks: EEG

The evidence indicates that intracranial EEG connectivity, as estimated from daily resting state recordings from epileptic patients, may be capable of identifying preictal states. In this study, we employed hyperbolic embedding of brain networks to capture non-trivial patterns that discriminate between connectivity networks from days with (preictal) and without (interictal) seizure. A statistical model was constructed by combining hyperbolic geometry and machine learning tools, which allowed for the estimation of the probability of an upcoming seizure. The results demonstrated that representing brain networks in a hyperbolic space enabled an accurate discrimination (85%) between interictal (no-seizure) and preictal (seizure within the next 24 hours) states. The proposed method also demonstrated excellent prediction performances, with an overall accuracy of 87% and an F1-score of 89% (mean Brier score and Brier skill score of 0.12 and 0.37, respectively). In conclusion, our findings indicate that representations of brain connectivity in a latent geometry space can reveal a daily and reliable signature of the upcoming seizure(s), thus providing a promising biomarker for seizure forecasting.
Circular transformation of the European steel industry renders scrap metal a strategic resource
Authors: Peter Klimek, Maximilian Hess, Markus Gerschberger, Stefan Thurner
Date: 2024-06-17 00:00:00
Tasks: ARC

The steel industry is a major contributor to CO2 emissions, accounting for 7% of global emissions. The European steel industry is seeking to reduce its emissions by increasing the use of electric arc furnaces (EAFs), which can produce steel from scrap, marking a major shift towards a circular steel economy. Here, we show by combining trade with business intelligence data that this shift requires a deep restructuring of the global and European scrap trade, as well as a substantial scaling of the underlying business ecosystem. We find that the scrap imports of European countries with major EAF installations have steadily decreased since 2007 while globally scrap trade started to increase recently. Our statistical modelling shows that every 1,000 tonnes of EAF capacity installed is associated with an increase in annual imports of 550 tonnes and a decrease in annual exports of 1,000 tonnes of scrap, suggesting increased competition for scrap metal as countries ramp up their EAF capacity. Furthermore, each scrap company enables an increase of around 79,000 tonnes of EAF-based steel production per year in the EU. Taking these relations as causal and extrapolating to the currently planned EAF capacity, we find that an additional 730 (SD 140) companies might be required, employing about 35,000 people (IQR 29,000-50,000) and generating an additional estimated turnover of USD 35 billion (IQR 27-48). Our results thus suggest that scrap metal is likely to become a strategic resource. They highlight the need for a massive restructuring of the industry's supply networks and identify the resulting growth opportunities for companies.
Local wealth condensation for yard-sale models with wealth-dependent biases
Authors: Christoph Börgers, Claude Greengard
Date: 2024-06-16 00:00:00

In Chakraborti's yard-sale model of an economy, identical agents engage in pairwise trades, resulting in wealth exchanges that conserve each agent's expected wealth. Doob's martingale convergence theorem immediately implies almost sure wealth condensation, i.e., convergence to a state in which a single agent owns the entire economy. If some pairs of agents are not allowed to trade with each other, the martingale convergence theorem still implies local wealth condensation, i.e., convergence to a state in which some agents are wealthy, while all their trading partners are impoverished. In this note, we propose a new, more elementary proof of this result. Unlike the proof based on the martingale convergence theorem, our argument applies to models with a wealth-acquired advantage, and even to certain models with a poverty-acquired advantage.
Resilience of international oil trade networks under extreme event shock-recovery simulations
Authors: Na Wei, Wen-Jie Xie, Wei-Xing Zhou
Date: 2024-06-17 00:00:00

With the frequent occurrence of black swan events, global energy security situation has become increasingly complex and severe. Assessing the resilience of the international oil trade network (iOTN) is crucial for evaluating its ability to withstand extreme shocks and recover thereafter, ensuring energy security. We overcomes the limitations of discrete historical data by developing a simulation model for extreme event shock-recovery in the iOTNs. We introduce network efficiency indicator to measure oil resource allocation efficiency and evaluate network performance. Then, construct a resilience index to explore the resilience of the iOTNs from dimensions of resistance and recoverability. Our findings indicate that extreme events can lead to sharp declines in performance of the iOTNs, especially when economies with significant trading positions and relations suffer shocks. The upward trend in recoverability and resilience reflects the self-organizing nature of the iOTNs, demonstrating its capacity for optimizing its own structure and functionality. Unlike traditional energy security research based solely on discrete historical data or resistance indicators, our model evaluates resilience from multiple dimensions, offering insights for global energy governance systems while providing diverse perspectives for various economies to mitigate risks and uphold energy security.
EM Estimation of Conditional Matrix Variate $t$ Distributions
Authors: Battulga Gankhuu
Date: 2024-06-16 00:00:00

Conditional matrix variate student $t$ distribution was introduced by Battulga (2024a). In this paper, we propose a new version of the conditional matrix variate student $t$ distribution. The paper provides EM algorithms, which estimate parameters of the conditional matrix variate student $t$ distributions, including general cases and special cases with Minnesota prior.
Randomization Inference: Theory and Applications
Authors: David M. Ritzwoller, Joseph P. Romano, Azeem M. Shaikh
Date: 2024-06-13 00:00:00
Tasks: valid

We review approaches to statistical inference based on randomization. Permutation tests are treated as an important special case. Under a certain group invariance property, referred to as the ``randomization hypothesis,'' randomization tests achieve exact control of the Type I error rate in finite samples. Although this unequivocal precision is very appealing, the range of problems that satisfy the randomization hypothesis is somewhat limited. We show that randomization tests are often asymptotically, or approximately, valid and efficient in settings that deviate from the conditions required for finite-sample error control. When randomization tests fail to offer even asymptotic Type 1 error control, their asymptotic validity may be restored by constructing an asymptotically pivotal test statistic. Randomization tests can then provide exact error control for tests of highly structured hypotheses with good performance in a wider class of problems. We give a detailed overview of several prominent applications of randomization tests, including two-sample permutation tests, regression, and conformal inference.
Multidimensional clustering in judge designs
Authors: Johannes W. Ligtenberg, Tiemen Woutersen
Date: 2024-06-13 00:00:00
Tasks: Clustering

Estimates in judge designs run the risk of being biased due to the many judge identities that are implicitly or explicitly used as instrumental variables. The usual method to analyse judge designs, via a leave-out mean instrument, eliminates this many instrument bias only in case the data are clustered in at most one dimension. What is left out in the mean defines this clustering dimension. How most judge designs cluster their standard errors, however, implies that there are additional clustering dimensions, which makes that a many instrument bias remains. We propose two estimators that are many instrument bias free, also in multidimensional clustered judge designs. The first generalises the one dimensional cluster jackknife instrumental variable estimator, by removing from this estimator the additional bias terms due to the extra dependence in the data. The second models all but one clustering dimensions by fixed effects and we show how these numerous fixed effects can be removed without introducing extra bias. A Monte-Carlo experiment and the revisitation of two judge designs show the empirical relevance of properly accounting for multidimensional clustering in estimation.
Big data in economics
Authors: Bogdan Oancea
Date: 2024-06-16 00:00:00
Tasks: Decision Making, Sociology

The term of big data was used since 1990s, but it became very popular around 2012. A recent definition of this term says that big data are information assets characterized by high volume, velocity, variety and veracity that need special analytical methods and software technologies to extract value form them. While big data was used at the beginning mostly in information technology field, now it can be found in every area of activity: in governmental decision-making processes, manufacturing, education, healthcare, economics, engineering, natural sciences, sociology. The rise of Internet, mobile phones, social media networks, different types of sensors or satellites provide enormous quantities of data that can have profound effects on economic research. The data revolution that we are facing transformed the way we measure the human behavior and economic activities. Unemployment, consumer price index, population mobility, financial transactions are only few examples of economic phenomena that can be analyzed using big data sources. In this paper we will start with a taxonomy of big data sources and show how these new data sources can be used in empirical analyses and to build economic indicators very fast and with reduced costs.
Research on Trends in Illegal Wildlife Trade based on Comprehensive Growth Dynamic Model
Authors: Run-Xuan Tang
Date: 2024-06-16 00:00:00
Tasks: regression

This paper presents an innovative Comprehensive Growth Dynamic Model (CGDM). CGDM is designed to simulate the temporal evolution of an event, incorporating economic and social factors. CGDM is a regression of logistic regression, power law regression, and Gaussian perturbation term. CGDM is comprised of logistic regression, power law regression, and Gaussian perturbation term. CGDM can effectively forecast the temporal evolution of an event, incorporating economic and social factors. The illicit trade in wildlife has a deleterious impact on the ecological environment. In this paper, we employ CGDM to forecast the trajectory of illegal wildlife trade from 2024 to 2034 in China. The mean square error is utilized as the loss function. The model illuminates the future trajectory of illegal wildlife trade, with a minimum point occurring in 2027 and a maximum point occurring in 2029. The stability of contemporary society can be inferred. CGDM's robust and generalizable nature is also evident.
Rationalizability, Cost-Rationalizability, and Afriat's Efficiency Index
Authors: Matthew Polisson, John K. -H. Quah
Date: 2024-06-14 00:00:00

This note explains the equivalence between approximate rationalizability and approximate cost-rationalizability within the context of consumer demand. In connection with these results, we interpret Afriat's (1973) critical cost efficiency index (CCEI) as a measure of approximate rationalizability through cost inefficiency, in the sense that an agent is spending more money than is required to achieve her utility targets.
California Community Colleges: Designing mentoring networks for access to social capital
Authors: A. Balaraman, S. Maokosy, L. Slaton, R. Cardona, P. Maokosy, N. Gaitan
Date: 2024-06-13 00:00:00

Successful careers are built on Skills (what you know), Occupational Identity (what you believe you can be) and Social Capital (who you know). Higher-ed spends significant resources in addressing the first, sometimes to the exclusion of the other two - which are difficult and expensive to teach and administer. This research specifically explores how near-peer mentoring programs, rather than a stand-alone opt-in guidance, can be integrated into the instruction/pedagogy by faculty at California community colleges. The research was conducted at 5 California community colleges (Reedley, Porterville, Coalinga, Solano and Glendale). A mixed-methods approach was used to gather social cognitive measures of student self-efficacy, occupational identity and social capital access. Measures were collected using survey instruments at the beginning of the mentoring program, and at its culmination. One of the most consistent measures observed across all the pilots was the increase in student self-efficacy of skills and competencies (3% - 7%) across colleges, geographies, and course formats after the mentoring program. Additionally, the research offers insights in implementing peer and near-peer mentoring programs that improve course completion, with significant instructional (and non-instructional) cost advantages.
Data Trade and Consumer Privacy
Authors: Jiadong Gu
Date: 2024-06-18 00:00:00

This paper studies optimal mechanisms for collecting and trading data. Consumers benefit from revealing information about their tastes to a service provider because this improves the service. However, the information is also valuable to a third party as it may extract more revenue from the consumer in another market called the product market. The paper characterizes the constrained optimal mechanism for the service provider subject to incentive feasibility. It is shown that the service provider sometimes sells no information or only partial information in order to preserve profits in the service market. In a general setup, the service provision distortion and no-price discrimination in the product market are exclusive. Moreover, a ban on data trade may reduce social welfare because it makes it harder to price discriminate in the product market.
Persuasion and Optimal Stopping
Authors: Andrew Koh, Sivakorn Sanguanmoo, Weijie Zhong
Date: 2024-06-18 00:00:00

We provide a unified analysis of how dynamic information should be designed in optimal stopping problems: a principal controls the flow of information about a payoff relevant state to persuade an agent to stop at the right time, in the right state, and choose the right action. We further show that for arbitrary preferences, intertemporal commitment is unnecessary: optimal dynamic information designs can always be made dynamically consistent.
Dynamic Evidence Disclosure: Delay the Good to Accelerate the Bad
Authors: Jan Knoepfle, Julia Salmi
Date: 2024-06-17 00:00:00

We analyze the dynamic tradeoff between generating and disclosing evidence. Agents are tempted to delay investing in a new technology in order to learn from information generated by the experiences of others. This informational free-riding is collectively harmful as it slows down learning and innovation adoption. A welfare-maximizing designer can delay the disclosure of previously generated information in order to speed up adoption. The optimal policy transparently discloses bad news and delays good news. This finding resonates with regulation demanding that fatal breakdowns be reported promptly. The designer's intervention makes all agents better off.
Incentive Contracts and Peer Effects in the Workplace
Authors: Marc Claveria-Mayol, Pau Milán, Nicolás Oviedo-Dávila
Date: 2024-06-17 00:00:00

We analyze how firms should design wage contracts when workers collaborate in teams and effort costs depend on colleagues through a peer network. Performance-based compensation generates incentives that cascade through the organization, which firms target to boost profits. We analyze optimal incentive design if firms can -- and can't -- fully discriminate across workers, and when the production technology is separable or complementary across divisions. When workers' effort is substitutable, the most central workers -- those who influence most colleagues directly and indirectly -- receive the steepest incentives only when output risk is sufficiently large; otherwise firms prioritize workers that are closer to those they influence. We derive a sufficient network statistic that measures the surplus loss when firms must compensate workers of varying centrality equally. Finally, when production technology exhibits complementarity across teams, stronger incentives are assigned to workers who influence colleagues in small teams that receive little influence from others. We apply our findings to organizational design questions, such as optimal firm structure and workforce investments.
Moral Hazard with Network Effects
Authors: Marc Claveria-Mayol
Date: 2024-06-17 00:00:00

I study a moral hazard problem between a principal and multiple agents who experience positive peer effects represented by a (weighted) network. Under the optimal linear contract, the principal provides high-powered incentives to central agents in the network in order to exploit the larger incentive spillovers such agents create. The analysis reveals a novel measure of network centrality that captures rich channels of direct and indirect incentive spillovers and characterizes the optimal contract and its induced equilibrium efforts. The notion of centrality relevant for incentive spillovers in the model emphasizes the role of pairs of agents who link to common neighbors in the network. This characterization leads to a measure of marginal network effects and identifies the agents whom the principal targets with stronger incentives in response to the addition (or strengthening) of a link. When the principal can position agents with heterogeneous costs of effort in the network, the principal prefers to place low-cost agents in central positions. The results shed light on how firms can increase productivity through corporate culture, office layout, and social interactions.
Hoping for the best while preparing for the worst in the face of uncertainty: a new type of incomplete preferences
Authors: Pierre Bardier, Bach Dong-Xuan, Van-Quy Nguyen
Date: 2024-06-17 00:00:00
Tasks: Decision Making

We propose and axiomatize a new model of incomplete preferences under uncertainty, which we call hope-and-prepare preferences. Act f is considered more desirable than act g when, and only when, both an optimistic evaluation, computed as the welfare level attained in a best-case scenario, and a pessimistic one, computed as the welfare level attained in a worst-case scenario, rank f above g. Our comparison criterion involves multiple priors, as best and worst cases are determined among sets of probability distributions, and is, generically, less conservative than Bewley preferences and twofold multi-prior preferences, the two ambiguity models that are closest to ours.
Endogenous Attention and the Spread of False News
Authors: Tuval Danenberg, Drew Fudenberg
Date: 2024-06-16 00:00:00

We study the impact of endogenous attention in a dynamic model of social media sharing. Each period, a distinct user randomly draws a story from the pool of stories on the platform and decides whether or not to share it. Users want to share stories that are true and interesting, but differentiating true stories from false ones requires attention. Before deciding whether to share a story, users choose their level of attention based on how interesting the story is and the platform's current proportions of true and false stories. We characterize the limit behavior of the share of true stories using stochastic approximation techniques. For some parameter specifications, the system has a unique limit. For others, the limit is random -- starting from the same initial conditions, the platform may end up with very different proportions of true and false stories and different user sharing behavior. We present various comparative statics for the limit. Endogenous attention leads to a counterbalancing force to changes in the credibility of false stories but can intensify the effects of changes in false stories' production rate.
Endogenous Identity in a Social Network
Authors: Christian Ghiglino, Nicole Tabasso
Date: 2024-06-16 00:00:00

Interaction with individuals from other socioeconomic classes has been shown to be a main driver for social mobility. We employ tools of social identity theory and network analysis to show how exposure to individuals of different social identities can lead to interactions with them, and an adoption of their identity, creating social mobility. We find that even if all individuals have the same ability, they may endogenously choose different identities, leading to different classes and actions. In particular, we derive a sufficient condition for such an equilibrium to exist, which equates to a novel measure of cohesion. Furthermore, we show that the most socially mobile individuals (changing their identity) are those who either have few connections or a more heterogeneous mix of identities in their connections. Finally, we show that upward social mobility increases action levels in society, but not necessarily welfare.