pwc-archive/papers-with-abstracts huggingface dataset:
Deception in Social Learning
Authors: Konstantinos Ntemos, Virginia Bordignon, Stefan Vlaski, Ali H. Sayed
Date: 2021-03-26 00:00:00

A common assumption in the social learning literature is that agents exchange information in an unselfish manner. In this work, we consider the scenario where a subset of agents aims at deceiving the network, meaning they aim at driving the network beliefs to the wrong hypothesis. The adversaries are unaware of the true hypothesis. However, they will "blend in" by behaving similarly to the other agents and will manipulate the likelihood functions used in the belief update process to launch inferential attacks. We will characterize the conditions under which the network is misled. Then, we will explain that it is possible for such attacks to succeed by showing that strategies exist that can be adopted by the malicious agents for this purpose. We examine both situations in which the agents have access to information about the network model as well as the case in which they do not. For the first case, we show that there always exists a way to construct fake likelihood functions such that the network is deceived regardless of the true hypothesis. For the latter case, we formulate an optimization problem and investigate the performance of the derived attack strategy by establishing conditions under which the network is deceived. We illustrate the learning performance of the network in the aforementioned adversarial setting via simulations. In a nutshell, we clarify when and how a network is deceived in the context of non-Bayesian social learning.
Individual Altruism Cannot Overcome Congestion Effects in a Global Pandemic Game
Authors: Philip N. Brown, Brandon Collins, Colton Hill, Gia Barboza, Lisa Hines
Date: 2021-03-24 00:00:00

A key challenge in responding to public health crises such as COVID-19 is the difficulty of predicting the results of feedback interconnections between the disease and society. As a step towards understanding these interconnections, we pose a simple game-theoretic model of a global pandemic in which individuals can choose where to live, and we investigate the global behavior that may emerge as a result of individuals reacting locally to the competing costs of isolation and infection. We study the game-theoretic equilibria that emerge from this setup when the population is composed of either selfish or altruistic individuals. First, we demonstrate that as is typical in these types of games, selfish equilibria are in general not optimal, but that all stable selfish equilibria are within a constant factor of optimal. Second, there exist infinitely-many stable altruistic equilibria; all but finitely-many of these are worse than the worst selfish equilibrium, and the social cost of altruistic equilibria is unbounded. Our work is in sharp contrast to recent work in network congestion games in which all altruistic equilibria are socially optimal. This suggests that a population without central coordination may react very poorly to a pandemic, and that individual altruism could even exacerbate the problem.
Approximate Solutions to a Class of Reachability Games
Authors: David Fridovich-Keil, Claire J. Tomlin
Date: 2020-11-01 00:00:00
Tasks: Collision Avoidance

In this paper, we present a method for finding approximate Nash equilibria in a broad class of reachability games. These games are often used to formulate both collision avoidance and goal satisfaction. Our method is computationally efficient, running in real-time for scenarios involving multiple players and more than ten state dimensions. The proposed approach forms a family of increasingly exact approximations to the original game. Our results characterize the quality of these approximations and show operation in a receding horizon, minimally-invasive control context. Additionally, as a special case, our method reduces to local gradient-based optimization in the single-player (optimal control) setting, for which a wide variety of efficient algorithms exist.
Cooperation in lot-sizing problems with heterogeneous costs: the effect of consolidated periods
Authors: Luis Guardiola, Ana Meca, Justo Puerto
Date: 2021-03-14 00:00:00

We consider a cooperative game defined by an economic lot-sizing problem with heterogeneous costs over a finite time horizon, in which each firm faces demand for a single product in each period and coalitions can pool orders. The model of cooperation works as follows: ordering channels and holding and backlogging technologies are shared among the members of the coalitions. This implies that each firm uses the best ordering channel and holding technology provided by the participants in the consortium. That is, they purchase, hold inventory, pay backlogged demand and make orders at the minimum cost of the coalition members. Thus, firms aim at satisfying their demand over the planing horizon with minimal operation cost. Our contribution is to show that there exist fair allocations of the overall operation cost among the firms so that no group of agents profit from leaving the consortium. Then we propose a parametric family of cost allocations and provide sufficient conditions for this to be a stable family against coalitional defections of firms. Finally, we focus on those periods of the time horizon that are consolidated and we analyze their effect on the stability of cost allocations.
Selling Data to an Agent with Endogenous Information
Authors: Yingkai Li
Date: 2021-03-09 00:00:00

We consider a model of a data broker selling information to a single agent to maximize his revenue. The agent has a private valuation of the additional information, and upon receiving the signal from the data broker, the agent can conduct her own experiment to refine her posterior belief on the states with additional costs. To maximize expected revenue, only offering full information in general is suboptimal, and the optimal mechanism may contain a continuum of menu options with partial information to prevent the agent from having incentives to acquire additional information from other sources. However, our main result shows that the additional benefit from price discrimination is limited, i.e., posting a deterministic price for revealing full information obtains at least half of the optimal revenue for arbitrary prior and cost functions.
Decentralized Microgrid Energy Management: A Multi-agent Correlated Q-learning Approach
Authors: Hao Zhou, Melike Erol-Kantarci
Date: 2021-03-06 00:00:00
Tasks: energy management, energy trading, Management, Q-Learning

Microgrids (MG) are anticipated to be important players in the future smart grid. For proper operation of MGs an Energy Management System (EMS) is essential. The EMS of an MG could be rather complicated when renewable energy resources (RER), energy storage system (ESS) and demand side management (DSM) need to be orchestrated. Furthermore, these systems may belong to different entities and competition may exist between them. Nash equilibrium is most commonly used for coordination of such entities however the convergence and existence of Nash equilibrium can not always be guaranteed. To this end, we use the correlated equilibrium to coordinate agents, whose convergence can be guaranteed. In this paper, we build an energy trading model based on mid-market rate, and propose a correlated Q-learning (CEQ) algorithm to maximize the revenue of each agent. Our results show that CEQ is able to balance the revenue of agents without harming total benefit. In addition, compared with Q-learning without correlation, CEQ could save 19.3% cost for the DSM agent and 44.2% more benefits for the ESS agent.
Prosumer Behavior: Decision Making with Bounded Horizon
Authors: Mohsen Rajabpour, Arnold Glass, Robert Mulligan, Narayan B. Mandayam
Date: 2021-03-05 00:00:00
Tasks: Decision Making

Most studies of prosumer decision making in the smart grid have focused on single, temporally discrete decisions within the framework of expected utility theory (EUT) and behavioral theories such as prospect theory. In this work, we study prosumer decision making in a more natural, ongoing market situation in which a prosumer has to decide every day whether to sell any surplus energy units generated by the solar panels on her roof or hold (store) the energy units in anticipation of a future sale at a better price. Within this context, we propose a new behavioral model that extends EUT to take into account the notion of a bounded temporal horizon over which various decision parameters are considered. Specifically, we introduce the notion of a bounded time window (the number of upcoming days over which a prosumer evaluates the probability that each possible price will be the highest) that prosumers implicitly impose on their decision making in arriving at hold or sell decisions. The new behavioral model assumes that humans make decisions that will affect their lives within a bounded time window regardless of how far into the future their units may be sold. Modeling the utility of the prosumer using parameters such as the offered price on a day, the number of energy units the prosumer has available for sale on a day, and the probabilities of the forecast prices, we fit both traditional EUT and the proposed behavioral model with bounded time windows to data collected from 57 homeowners over 68 days in a simulated energy market. Each prosumer generated surplus units of solar power and had the opportunity to sell those units to the local utility at the price set that day by the utility or hold the units for sale in the future. For most participants, a bounded horizon in the range of 4-5 days provided a much better fit to their responses than was found for the traditional (unbounded) EUT model
The Effect of Behavioral Probability Weighting in a Simultaneous Multi-Target Attacker-Defender Game
Authors: Mustafa Abdallah, Timothy Cason, Saurabh Bagchi, Shreyas Sundaram
Date: 2021-03-05 00:00:00
Tasks: Decision Making

We consider a security game in a setting consisting of two players (an attacker and a defender), each with a given budget to allocate towards attack and defense, respectively, of a set of nodes. Each node has a certain value to the attacker and the defender, along with a probability of being successfully compromised, which is a function of the investments in that node by both players. For such games, we characterize the optimal investment strategies by the players at the (unique) Nash Equilibrium. We then investigate the impacts of behavioral probability weighting on the investment strategies; such probability weighting, where humans overweight low probabilities and underweight high probabilities, has been identified by behavioral economists to be a common feature of human decision-making. We show via numerical experiments that behavioral decision-making by the defender causes the Nash Equilibrium investments in each node to change (where the defender overinvests in the high-value nodes and underinvests in the low-value nodes).
Social Profit Optimization with Demand Response Management in Electricity Market: A Multi-timescale Leader-following Approach
Authors: Jianzheng Wang, Yipeng Pang, Guoqiang Hu
Date: 2021-03-02 00:00:00
Tasks: Management

In the electricity market, it is quite common that the market participants make "selfish" strategies to harvest the maximum profits for themselves, which may cause the social benefit loss and impair the sustainability of the society in the long term. Regarding this issue, in this work, we will discuss how the social profit can be improved through strategic demand response (DR) management. Specifically, we explore two interaction mechanisms in the market: Nash equilibrium (NE) and Stackelberg equilibrium (SE) among utility companies (UCs) and user-UC interactions, respectively. At the user side, each user determines the optimal energy-purchasing strategy to maximize its own profit. At the UC side, a governmental UC (g-UC) is considered, who aims to optimize the social profit of the market. Meanwhile, normal UCs play games to maximize their own profits. As a result, a basic leader-following problem among the UCs is formulated under the coordination of the independent system operator (ISO). Moreover, by using our proposed demand function amelioration (DFA) strategy, a multi-timescale leader-following problem is formulated. In this case, the maximal market efficiency can be achieved without changing the "selfish instinct" of normal UCs. In addition, by considering the local constraints for the UCs, two projection-based pricing algorithms are proposed for UCs, which can provide approximate optimal solutions for the resulting non-convex social profit optimization problems. The feasibility of the proposed algorithms is verified by using the concept of price of anarchy (PoA) in a multi-UC multi-user market model in the simulation.
Selling Two Identical Objects
Authors: Sushil Bikhchandani, Debasis Mishra
Date: 2020-09-24 00:00:00
Tasks: Vocal Bursts Valence Prediction

It is well-known that optimal (i.e., revenue-maximizing) selling mechanisms in multidimensional type spaces may involve randomization. We obtain conditions under which deterministic mechanisms are optimal for selling two identical, indivisible objects to a single buyer. We analyze two settings: (i) decreasing marginal values (DMV) and (ii) increasing marginal values (IMV). Thus, the values of the buyer for the two units are not independent. We show that under a well-known condition on distributions~(due to McAfee and McMillan (1988)), (a) it is optimal to sell the first unit deterministically in the DMV model and (b) it is optimal to bundle (which is a deterministic mechanism) in the IMV model. Under a stronger sufficient condition on distributions, a deterministic mechanism is optimal in the DMV model. Our results apply to heterogeneous objects when there is a specified sequence in which the two objects must be sold.
Classification of Priorities Such That Deferred Acceptance is Obviously Strategyproof
Authors: Clayton Thomas
Date: 2020-11-24 00:00:00
Tasks: Classification, General Classification

We study the strategic simplicity of stable matching mechanisms where one side has fixed preferences, termed priorities. Specifically, we ask which priorities are such that the strategyproofness of deferred acceptance (DA) can be recognized by agents unable to perform contingency reasoning, that is, \emph{when is DA obviously strategyproof} (Li, 2017)? We answer this question by completely characterizing those priorities which make DA obviously strategyproof (OSP). This solves an open problem of Ashlagi and Gonczarowski, 2018. We find that when DA is OSP, priorities are either acyclic (Ergin, 2002), a restrictive condition which allows priorities to only differ on only two agents at a time, or contain an extremely limited cyclic pattern where all priority lists are identical except for exactly two. We conclude that, for stable matching mechanisms, the tension between understandability (in the sense of OSP) and expressiveness of priorities is very high.
A Pursuit-Evasion Differential Game with Strategic Information Acquisition
Authors: Yunhan Huang, Quanyan Zhu
Date: 2021-02-10 00:00:00

This paper studies a two-person linear-quadratic-Gaussian pursuit-evasion differential game with costly but controlled information. One player can decide when to observe the other player's state. However, one observation of another player's state comes with two costs: the direct cost of observing and the implicit cost of exposing his state. We call games of this type a Pursuit-Evasion-Exposure-Concealment (PEEC) game. The PEEC game constitutes two types of strategies: The control strategies and the observation strategies. We fully characterize the Nash control strategies of the PEEC game using techniques such as completing squares and the calculus of variations. We show that the derivation of the Nash observation strategies and the Nash control strategies can be decoupled. We develop a set of necessary conditions that facilitate the numerical computation of the Nash observation strategies. We show, in theory, that players with less maneuverability prefer concealment to exposure. We also show that when the game's horizon goes to infinity, the Nash observation strategy is to observe periodically, and the expected distance between the pursuer and the evader goes to zero with a bounded second moment. We conducted a series of numerical experiments to study the proposed PEEC game. We illustrate the numerical results using both figures and animation. Numerical results show that the pursuer can maintain high-grade performance even when the number of observations is limited. We also show that an evader with low maneuverability can still escape if the evader increases his stealthiness.
How to Motivate and Engage Generation Clash of Clans at Work? Emergent Properties of Business Gamification Elements in the Digital Economy
Authors: Nicholas Dacre, Panos Constantinides, Joe Nandhakumar
Date: 2021-03-23 00:00:00
Tasks: Game Design

Organisations are currently lacking in developing and implementing business systems in meaningful ways to motivate and engage their staff. This is particularly salient as the average employee spends eleven cumulative years of their life at work, however less than one third of the workforce are actually engaged in their duties throughout their career. Such low levels of engagement are particularly prominent with younger employees, referred to as Generation Y (GenY), who are the least engaged of all groups at work. However, they will dedicate around five cumulative years of their life immersed playing video games such as Clash of Clans, whether for social, competitive, extrinsic, or intrinsic motivational factors. Using behavioural concepts derived from video games, and applying game design elements in business systems to motivate employees in the digital economy, is a concept which has come to be recognised as Business Gamification. Thus, the purpose of this research paper is to further our understanding of game design elements for business, and investigate their properties from design to implementation in gamified systems. Following a two-year ethnographic style study with both a system development, and a communication agency largely staffed with GenY employees, findings suggest properties in game design elements are emergent and temporal in their instantiations.
QUCoughScope: An Artificially Intelligent Mobile Application to Detect Asymptomatic COVID-19 Patients using Cough and Breathing Sounds
Authors: Muhammad E. H. Chowdhury, Nabil Ibtehaz, Tawsifur Rahman, Yosra Magdi Salih Mekki, Yazan Qibalwey, Sakib Mahmud, Maymouna Ezeddin, Susu Zughaier, Sumaya Ali S A Al-Maadeed
Date: 2021-03-20 00:00:00
Tasks: Diagnostic

In the break of COVID-19 pandemic, mass testing has become essential to reduce the spread of the virus. Several recent studies suggest that a significant number of COVID-19 patients display no physical symptoms whatsoever. Therefore, it is unlikely that these patients will undergo COVID-19 test, which increases their chances of unintentionally spreading the virus. Currently, the primary diagnostic tool to detect COVID-19 is RT-PCR test on collected respiratory specimens from the suspected case. This requires patients to travel to a laboratory facility to be tested, thereby potentially infecting others along the way.It is evident from recent researches that asymptomatic COVID-19 patients cough and breath in a different way than the healthy people. Several research groups have created mobile and web-platform for crowdsourcing the symptoms, cough and breathing sounds from healthy, COVID-19 and Non-COVID patients. Some of these data repositories were made public. We have received such a repository from Cambridge University team under data-sharing agreement, where we have cough and breathing sound samples for 582 and 141 healthy and COVID-19 patients, respectively. 87 COVID-19 patients were asymptomatic, while rest of them have cough. We have developed an Android application to automatically screen COVID-19 from the comfort of people homes. Test subjects can simply download a mobile application, enter their symptoms, record an audio clip of their cough and breath, and upload the data anonymously to our servers. Our backend server converts the audio clip to spectrogram and then apply our state-of-the-art machine learning model to classify between cough sounds produced by COVID-19 patients, as opposed to healthy subjects or those with other respiratory conditions. The system can detect asymptomatic COVID-19 patients with a sensitivity more than 91%.
Dynamic Control Allocation between Onboard and Delayed Remote Control for Unmanned Aircraft System Detect-and-Avoid
Authors: Asma Tabassum, He Bai
Date: 2021-03-14 00:00:00

This paper develops and evaluates the performance of an allocation agent to be potentially integrated into the onboard Detect and Avoid (DAA) computer of an Unmanned Aircraft System (UAS). We consider a UAS that can be fully controlled by the onboard DAA system and by a remote human pilot. With a communication channel prone to latency, we consider a mixed initiative interaction environment, where the control authority of the UAS is dynamically allocated by the allocation agent. In an encounter with a dynamic intruder, the probability of collision may increase in the absence of pilot commands in the presence of latency. Moreover, a delayed pilot command may not result in safe resolution of the current scenario and need to be improvised. We design an optimization algorithm to reduce collision risk and refine delayed pilot commands. Towards this end, a Markov Decision Process (MDP)and its solution are employed to create a wait time map. The map consists of estimated times that the UAS can wait for the remote pilot commands at each state. A command blending algorithm is designed to select an avoidance maneuver that prioritizes the pilot intention extracted from the pilot commands. The wait time map and the command blending algorithm are implemented and integrated into a closed-loop simulator. We conduct ten thousands fast-time Monte Carlo simulations and compare the performance of the integrated setup with a standalone DAA setup. The simulation results show that the allocation agent enables the UAS to wait without inducing any near mid air collision (NMAC) and severe loss of well clear (LoWC) while positively improve pilot involvement in the encounter resolution.