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  • A Note on the Provision of a Public Service of Different Quality
    arXiv.cs.GT Pub Date : 2020-03-31
    Monica Anna Giovanniello; Simone Tonin

    We study how the quality dimension affects the social optimum in a model of spatial differentiation where two facilities provide a public service. If quality enters linearly in the individuals' utility function, a symmetric configuration, in which both facilities have the same quality and serve groups of individuals of the same size, does not maximize the social welfare. This is a surprising result

  • A k-hop Collaborate Game Model: Extended to Community Budgets and Adaptive Non-Submodularity
    arXiv.cs.GT Pub Date : 2020-04-02
    Jianxiong Guo; Weili Wu

    Revenue maximization (RM) is one of the most important problems on online social networks (OSNs), which attempts to find a small subset of users in OSNs that makes the expected revenue maximized. It has been researched intensively before. However, most of exsiting literatures were based on non-adaptive seeding strategy and on simple information diffusion model, such as IC/LT-model. It considered the

  • Provable Sample Complexity Guarantees for Learning of Continuous-Action Graphical Games with Nonparametric Utilities
    arXiv.cs.GT Pub Date : 2020-04-01
    Adarsh Barik; Jean Honorio

    In this paper, we study the problem of learning the exact structure of continuous-action games with non-parametric utility functions. We propose an $\ell_1$ regularized method which encourages sparsity of the coefficients of the Fourier transform of the recovered utilities. Our method works by accessing very few Nash equilibria and their noisy utilities. Under certain technical conditions, our method

  • Life is Random, Time is Not: Markov Decision Processes with Window Objectives
    arXiv.cs.GT Pub Date : 2019-01-11
    Thomas Brihaye; Florent Delgrange; Youssouf Oualhadj; Mickael Randour

    The window mechanism was introduced by Chatterjee et al. to strengthen classical game objectives with time bounds. It permits to synthesize system controllers that exhibit acceptable behaviors within a configurable time frame, all along their infinite execution, in contrast to the traditional objectives that only require correctness of behaviors in the limit. The window concept has proved its interest

  • No-regret learning dynamics for extensive-form correlated and coarse correlated equilibria
    arXiv.cs.GT Pub Date : 2020-04-01
    Andrea Celli; Alberto Marchesi; Gabriele Farina; Nicola Gatti

    Recently, there has been growing interest around less-restrictive solution concepts than Nash equilibrium in extensive-form games, with significant effort towards the computation of extensive-form correlated equilibrium (EFCE) and extensive-form coarse correlated equilibrium (EFCCE). In this paper, we show how to leverage the popular counterfactual regret minimization (CFR) paradigm to induce simple

  • How to Cut a Cake Fairly: A Generalization to Groups
    arXiv.cs.GT Pub Date : 2020-01-10
    Erel Segal-Halevi; Warut Suksompong

    A fundamental result in cake cutting states that for any number of players with arbitrary preferences over a cake, there exists a division of the cake such that every player receives a single contiguous piece and no player is left envious. We generalize this result by showing that it is possible to partition the players into groups of any desired sizes and divide the cake among the groups, so that

  • Adversarial Stress Testing of Lifetime Distributions
    arXiv.cs.GT Pub Date : 2020-03-27
    Nozer Singpurwalla

    In this paper we put forward the viewpoint that the notion of stress testing financial institutions and engineered systems can also be made viable appropos the stress testing an individual's strength of conviction in a probability distribution. The difference is interpretation and perspective. To make our case we consider a game theoretic setup entailing two players, an adversarial C, and an amicable

  • The Influence of Reward on the Social Valence of Interactions
    arXiv.cs.GT Pub Date : 2020-03-27
    Tomás Alves; Samuel Gomes; João Dias; Carlos Martinho

    Throughout the years, social norms have been promoted as an informal enforcement mechanism for achieving beneficial collective outcomes. Among the most used methods to foster interactions, framing the context of a situation or setting in-game rules have shown strong results as mediators on how an individual interacts with their peers. Nevertheless, we found that there is a lack of research regarding

  • Graphical Games and Decomposition
    arXiv.cs.GT Pub Date : 2020-03-29
    Laura Arditti; Giacomo Como; Fabio Fagnani

    We consider graphical games as introduced by Kearns et al. (2001). First we analyse the interaction of graphicality with a notion of strategic equivalence of games, providing a minimal complexity graphical description for games. Then we study the interplay between graphicality and the classical decomposition of games proposed by Candogan et al. (2011), characterizing the graphical properties of each

  • Separable games
    arXiv.cs.GT Pub Date : 2020-03-29
    Laura Arditti; Giacomo Como; Fabio Fagnani

    We introduce the notion of separable games, which refines and generalizes that of graphical games. We prove that there exists a minimal splitting with respect to which a game is separable. Moreover we prove that in every strategic equivalence class, there is a game separable with respect to the minimal splitting in the class. This game is also graphical with respect to the smallest graph in the class

  • Zero-Rating and Net Neutrality: Who Wins, Who Loses?
    arXiv.cs.GT Pub Date : 2020-02-13
    Niloofar Bayat; Richard Ma; Vishal Misra; Dan Rubenstein

    An objective of network neutrality is that the design of regulations for the Internet will ensure that it remains a public, open platform where innovations can thrive. While there is broad agreement that preserving the content quality of service falls under the purview of net neutrality, the role of differential pricing, especially the practice of \emph {zero-rating} remains controversial. Even though

  • Combined Cooling, Heating, and Power System in Blockchain-Enabled Energy Management
    arXiv.cs.GT Pub Date : 2020-03-20
    Jianxiong Guo; Xingjian Ding; Weili Wu

    The combined cooling, heating and power (CCHP) system is a typical distributed, electricity-gas integrated energy scheme in a community. First, it generates electricity by use of gas, and then exploits the waste heat to supply community with heat and cooling. In this paper, we consider a smart city consisting of a number of communities (CCHPs) and an agent of power grid (APG), where CCHPs can sell

  • Dynamic Resilient Network Games with Applications to Multi-Agent Consensus
    arXiv.cs.GT Pub Date : 2020-03-30
    Yurid Nugraha; Ahmet Cetinkaya; TOmohisa Hayakawa; Hideaki Ishii; Quanyan Zhu

    A cyber security problem in a networked system formulated as a resilient graph problem based on a game-theoretic approach is considered. The connectivity of the underlying graph of the network system is reduced by an attacker who removes some of the edges whereas the defender attempts to recover them. Both players are subject to energy constraints so that their actions are restricted and cannot be

  • Discriminatory Price Mechanism for Smart Grid
    arXiv.cs.GT Pub Date : 2020-03-30
    Diptangshu Sen; Kushaagra Goyal; Arnob Ghosh; Varun Ramamohan

    We consider a scenario where the retailers can select different prices to the users in a smart grid. Each user's demand consists of an elastic component and an inelastic component. The retailer's objective is to maximize the revenue, minimize the operating cost, and maximize the user's welfare. The retailer wants to optimize a convex combination of the above objectives using a price signal. The discriminations

  • Monopoly Pricing in a Vertical Market with Demand Uncertainty
    arXiv.cs.GT Pub Date : 2017-09-27
    Stefanos Leonardos; Costis Melolidakis; Constandina Koki

    We study a vertical market with an upsteam supplier and multiple downstream retailers. Demand uncertainty falls to the supplier who acts first and sets a uniform wholesale price before the retailers observe the realized demand and engage in retail competition. Our focus is on the supplier's optimal pricing decision. We express the price elasticity of expected demand in terms of the mean residual demand

  • Inference from Auction Prices
    arXiv.cs.GT Pub Date : 2019-02-19
    Jason Hartline; Aleck Johnsen; Denis Nekipelov; Zihe Wang

    Econometric inference allows an analyst to back out the values of agents in a mechanism from the rules of the mechanism and bids of the agents. This paper gives an algorithm to solve the problem of inferring the values of agents in a dominant-strategy mechanism from the social choice function implemented by the mechanism and the per-unit prices paid by the agents (the agent bids are not observed).

  • Data-Driven Optimization of Personalized Reserve Prices
    arXiv.cs.GT Pub Date : 2019-05-04
    Mahsa Derakhshan; Negin Golrezaei; Renato Paes Leme

    We study the problem of computing data-driven personalized reserve prices in eager second price auctions without having any assumption on valuation distributions. Here, the input is a data-set that contains the submitted bids of $n$ buyers in a set of auctions and the problem is to return personalized reserve prices $\textbf r$ that maximize the revenue earned on these auctions by running eager second

  • Mechanism Design for Wireless Powered Spatial Crowdsourcing Networks
    arXiv.cs.GT Pub Date : 2020-03-27
    Yutao Jiao; Ping Wang; Dusit Niyato; Bin Lin; Dong In Kim

    Wireless power transfer (WPT) is a promising technology to prolong the lifetime of the sensors and communication devices, i.e., workers, in completing crowdsourcing tasks by providing continuous and cost-effective energy supplies. In this paper, we propose a wireless powered spatial crowdsourcing framework which consists of two mutually dependent phases: task allocation phase and data crowdsourcing

  • Auction Mechanisms in Cloud/Fog Computing Resource Allocation for Public Blockchain Networks
    arXiv.cs.GT Pub Date : 2018-04-26
    Yutao Jiao; Ping Wang; Dusit Niyato; Kongrath Suankaewmanee

    As an emerging decentralized secure data management platform, blockchain has gained much popularity recently. To maintain a canonical state of blockchain data record, proof-of-work based consensus protocols provide the nodes, referred to as miners, in the network with incentives for confirming new block of transactions through a process of "block mining" by solving a cryptographic puzzle. Under the

  • Pedestrian Models for Autonomous Driving Part II: high level models of human behaviour
    arXiv.cs.GT Pub Date : 2020-03-26
    Fanta Camara; Nicola Bellotto; Serhan Cosar; Florian Weber; Dimitris Nathanael; Matthias Althoff; Jingyuan Wu; Johannes Ruenz; André Dietrich; Gustav Markkula; Anna Schieben; Fabio Tango; Natasha Merat; Charles W. Fox

    Autonomous vehicles (AVs) must share space with human pedestrians, both in on-road cases such as cars at pedestrian crossings and off-road cases such as delivery vehicles navigating through crowds on high-streets. Unlike static and kinematic obstacles, pedestrians are active agents with complex, interactive motions. Planning AV actions in the presence of pedestrians thus requires modelling of their

  • Toward an Automated Auction Framework for Wireless Federated Learning Services Market
    arXiv.cs.GT Pub Date : 2019-12-13
    Yutao Jiao; Ping Wang; Dusit Niyato; Bin Lin; Dong In Kim

    In traditional machine learning, the central server first collects the data owners' private data together and then trains the model. However, people's concerns about data privacy protection are dramatically increasing. The emerging paradigm of federated learning efficiently builds machine learning models while allowing the private data to be kept at local devices. The success of federated learning

  • Last Round Convergence and No-Instant Regret in Repeated Games with Asymmetric Information
    arXiv.cs.GT Pub Date : 2020-03-26
    Le Cong Dinh; Long Tran-Thanh; Tri-Dung Nguyen; Alain B. Zemkoho

    This paper considers repeated games in which one player has more information about the game than the other players. In particular, we investigate repeated two-player zero-sum games where only the column player knows the payoff matrix A of the game. Suppose that while repeatedly playing this game, the row player chooses her strategy at each round by using a no-regret algorithm to minimize her (pseudo)

  • A Misreport- and Collusion-Proof Crowdsourcing Mechanism without Quality Verification
    arXiv.cs.GT Pub Date : 2020-03-26
    Kun Li; Shengling Wang; Xiuzhen Cheng; Qin Hu

    Quality control plays a critical role in crowdsourcing. The state-of-the-art work is not suitable for large-scale crowdsourcing applications, since it is a long haul for the requestor to verify task quality or select professional workers in a one-by-one mode. In this paper, we propose a misreport- and collusion-proof crowdsourcing mechanism, guiding workers to truthfully report the quality of submitted

  • Simultaneous 2nd Price Item Auctions with No-Underbidding
    arXiv.cs.GT Pub Date : 2020-03-26
    Michal Feldman; Galia Shabtai

    We study the price of anarchy (PoA) of simultaneous 2nd price auctions (S2PA) under a natural condition of {\em no underbidding}. No underbidding means that an agent's bid on every item is at least its marginal value given the outcome. In a 2nd price auction, underbidding on an item is weakly dominated by bidding the item's marginal value. Indeed, the no underbidding assumption is justified both theoretically

  • Efficient Estimation of Equilibria in Large Aggregative Games with Coupling Constraints
    arXiv.cs.GT Pub Date : 2019-11-21
    Paulin JacquotTROPICAL; Cheng WanEDF R&D OSIRIS; Olivier BeaudeEDF R&D OSIRIS; Nadia OudjaneEDF R&D OSIRIS

    Aggregative games have many industrial applications, and computing an equilibrium in those games is challenging when the number of players is large. In the framework of atomic aggregative games with coupling constraints, we show that variational Nash equilibria of a large aggregative game can be approximated by a Wardrop equilibrium of an auxiliary population game of smaller dimension. Each population

  • Assignment and Pricing of Shared Rides in Ride-Sourcing using Combinatorial Double Auctions
    arXiv.cs.GT Pub Date : 2019-09-18
    Renos Karamanis; Eleftherios Anastasiadis; Panagiotis Angeloudis; Marc Stettler

    Transportation Network Companies employ dynamic pricing methods at periods of peak travel to incentivise driver participation and balance supply and demand for rides. Surge pricing multipliers are commonly used and are applied following demand and estimates of customer and driver trip valuations. Combinatorial double auctions have been identified as a suitable alternative, as they can achieve maximum

  • Punishing defectors and rewarding cooperators: Do people discriminate between genders?
    arXiv.cs.GT Pub Date : 2020-03-24
    Hélène Barcelo; Valerio Capraro

    Do people discriminate between men and women when they are in charge of punishing defectors or rewarding cooperators? Answering this question has potentially far-reaching implications on gender equity, since cooperative behaviour forms the basis of our societies and is typically enforced through punishment or rewarding. In this paper we report on two pre-registered experiments, that we hope shed some

  • Improved lower bound on the dimension of the EU council's voting rules
    arXiv.cs.GT Pub Date : 2020-03-25
    Stefan Kober; Stefan Weltge

    Kurz and Napel (2015) proved that the voting system of the EU council (based on the 2014 population data) cannot be represented as the intersection of six weighted games, i.e., its dimension is at least 7. This set a new record for real-world voting rules and the authors posed the exact determination as a challenge. Recently, Chen, Cheung, and Ng (2019) showed that the dimension is at most 24. We provide

  • Distributed Reinforcement Learning for Cooperative Multi-Robot Object Manipulation
    arXiv.cs.GT Pub Date : 2020-03-21
    Guohui Ding; Joewie J. Koh; Kelly Merckaert; Bram Vanderborght; Marco M. Nicotra; Christoffer Heckman; Alessandro Roncone; Lijun Chen

    We consider solving a cooperative multi-robot object manipulation task using reinforcement learning (RL). We propose two distributed multi-agent RL approaches: distributed approximate RL (DA-RL), where each agent applies Q-learning with individual reward functions; and game-theoretic RL (GT-RL), where the agents update their Q-values based on the Nash equilibrium of a bimatrix Q-value game. We validate

  • A Game-Theoretic Model of Human Driving and Application to Discretionary Lane-Changes
    arXiv.cs.GT Pub Date : 2020-03-22
    Jehong Yoo; Reza Langari

    In this paper we consider the application of Stackelberg game theory to model discretionary lane-changing in lightly congested highway setting. The fundamental intent of this model, which is parameterized to capture driver disposition (aggressiveness or inattentiveness), is to help with the development of decision-making strategies for autonomous vehicles in ways that are mindful of how human drivers

  • A Stackelberg Game Theoretic Model of Lane-Merging
    arXiv.cs.GT Pub Date : 2020-03-22
    Jehong Yoo; Reza Langari

    Merging in the form of a mandatory lane-change is an important issue in transportation research. Even when safely completed, merging may disturb the mainline traffic and reduce the efficiency or capacity of the roadway. In this paper, we consider a Stackelberg game-theoretic driver behavior model where the so-called utilities or payoffs reflect the merging vehicle's aggressiveness as it pertains the

  • Optimal No-regret Learning in Repeated First-price Auctions
    arXiv.cs.GT Pub Date : 2020-03-22
    Yanjun Han; Zhengyuan Zhou; Tsachy Weissman

    We study online learning in repeated first-price auctions with censored feedback, where a bidder, only observing the winning bid at the end of each auction, learns to adaptively bid in order to maximize her cumulative payoff. To achieve this goal, the bidder faces a challenging dilemma: if she wins the bid--the only way to achieve positive payoffs--then she is not able to observe the highest bid of

  • Hypothesis Testing Approach to Detecting Collusion in Competitive Environments
    arXiv.cs.GT Pub Date : 2020-03-22
    Pedro Hespanhol; Anil Aswani

    There is growing concern about the possibility for tacit collusion using algorithmic pricing, and regulators need tools to help detect the possibility of such collusion. This paper studies how to design a hypothesis testing framework in order to decide whether agents are behaving competitively or not. In our setting, agents are utility-maximizing and compete over prices of items. A regulator, with

  • Egalitarian solution for games with discrete side payment
    arXiv.cs.GT Pub Date : 2020-03-23
    Takafumi Otsuka

    In this paper, we study the egalitarian solution for games with discrete side payment, where the characteristic function is integer-valued and payoffs of players are integral vectors. The egalitarian solution, introduced by Dutta and Ray in 1989, is a solution concept for transferable utility cooperative games in characteristic form, which combines commitment for egalitarianism and promotion of indivisual

  • Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling
    arXiv.cs.GT Pub Date : 2020-03-23
    Yu-Guan Hsieh; Franck Iutzeler; Jérôme Malick; Panayotis Mertikopoulos

    Owing to their stability and convergence speed, extragradient methods have become a staple for solving large-scale saddle-point problems in machine learning. The basic premise of these algorithms is the use of an extrapolation step before performing an update; thanks to this exploration step, extra-gradient methods overcome many of the non-convergence issues that plague gradient descent/ascent schemes

  • A distributed (preconditioned) projected-reflected-gradient algorithm for stochastic generalized Nash equilibrium problems
    arXiv.cs.GT Pub Date : 2020-03-20
    Barbara Franci; Sergio Grammatico

    We consider the stochastic generalized Nash equilibrium problem (SGNEP) with joint feasibility constraints and expected-value cost functions. We propose a distributed stochastic preconditioned projected reflected gradient algorithm and show its almost sure convergence when the pseudogradient mapping is cocoercive. The algorithm is based on monotone operator splitting methods for SGNEPs when the expected-value

  • Optimising Game Tactics for Football
    arXiv.cs.GT Pub Date : 2020-03-23
    Ryan Beal; Georgios Chalkiadakis; Timothy J. Norman; Sarvapali D. Ramchurn

    In this paper we present a novel approach to optimise tactical and strategic decision making in football (soccer). We model the game of football as a multi-stage game which is made up from a Bayesian game to model the pre-match decisions and a stochastic game to model the in-match state transitions and decisions. Using this formulation, we propose a method to predict the probability of game outcomes

  • Mislearning from Censored Data: The Gambler's Fallacy in Optimal-Stopping Problems
    arXiv.cs.GT Pub Date : 2018-03-21
    Kevin He

    I study endogenous learning dynamics for people expecting systematic reversals from random sequences - the "gambler's fallacy." Biased agents face an optimal-stopping problem. They are uncertain about the underlying distribution and learn its parameters from predecessors. Agents stop when early draws are "good enough," so predecessors' experience contain negative streaks but not positive streaks. Since

  • Policy Regret in Repeated Games
    arXiv.cs.GT Pub Date : 2018-11-09
    Raman Arora; Michael Dinitz; Teodor V. Marinov; Mehryar Mohri

    The notion of \emph{policy regret} in online learning is a well defined? performance measure for the common scenario of adaptive adversaries, which more traditional quantities such as external regret do not take into account. We revisit the notion of policy regret and first show that there are online learning settings in which policy regret and external regret are incompatible: any sequence of play

  • Price of Anarchy in Bernoulli Congestion Games with Affine Costs
    arXiv.cs.GT Pub Date : 2019-03-08
    Roberto Cominetti; Marco Scarsini; Marc Schröder; Nicolás Stier-Moses

    Since demand in transportation networks is uncertain, commuters need to anticipate different traffic conditions. We capture this uncertainty by assuming that each commuter may make the trip or not with a fixed probability, creating an atomic congestion game with Bernoulli demands. Each commuter participates with an exogenous probability $p_i\in[0,1]$, which is common knowledge, independently of everybody

  • Incentives in Ethereum's Hybrid Casper Protocol
    arXiv.cs.GT Pub Date : 2019-03-11
    Vitalik Buterin; Daniel Reijsbergen; Stefanos Leonardos; Georgios Piliouras

    We present an overview of hybrid Casper the Friendly Finality Gadget (FFG): a Proof-of-Stake checkpointing protocol overlaid onto Ethereum's Proof-of-Work blockchain. We describe its core functionalities and reward scheme, and explore its properties. Our findings indicate that Casper's implemented incentives mechanism ensures liveness, while providing safety guarantees that improve over standard Proof-of-Work

  • Weighted Voting on the Blockchain: Improving Consensus in Proof of Stake Protocols
    arXiv.cs.GT Pub Date : 2019-03-11
    Stefanos Leonardos; Daniel Reijsbergen; Georgios Piliouras

    Proof of Stake (PoS) protocols rely on voting mechanisms to reach consensus on the current state. If an enhanced majority of staking nodes, also called validators, agree on a proposed block, then this block is appended to the blockchain. Yet, these protocols remain vulnerable to faults caused by validators who abstain either accidentally or maliciously. To protect against such faults while retaining

  • Privacy, Altruism, and Experience: Estimating the Perceived Value of Internet Data for Medical Uses
    arXiv.cs.GT Pub Date : 2019-06-20
    Gilie Gefen; Omer Ben-Porat; Moshe Tennenholtz; Elad Yom-Tov

    People increasingly turn to the Internet when they have a medical condition. The data they create during this process is a valuable source for medical research and for future health services. However, utilizing these data could come at a cost to user privacy. Thus, it is important to balance the perceived value that users assign to these data with the value of the services derived from them. Here we

  • PREStO: A Systematic Framework for Blockchain Consensus Protocols
    arXiv.cs.GT Pub Date : 2019-06-15
    Stefanos Leonardos; Daniel Reijsbergen; Georgios Piliouras

    The rapid evolution of blockchain technology has brought together stakeholders from fundamentally different backgrounds. The result is a diverse ecosystem, as exemplified by the development of a wide range of different blockchain protocols. This raises questions for decision and policy makers: How do different protocols compare? What are their trade-offs? Existing efforts to survey the area reveal

  • Energy and Social Cost Minimization for Data Dissemination in Wireless Networks: Centralized and Decentralized Approaches
    arXiv.cs.GT Pub Date : 2019-11-06
    Mahdi Mousavi; Anja Klein

    We study multi-hop data-dissemination in a wireless network from one source to multiple nodes where some of the nodes of the network act as re-transmitting nodes and help the source in data dissemination. In this network, we study two scenarios; i) the transmitting nodes do not need an incentive for transmission and ii) they do need an incentive and are paid by their corresponding receiving nodes by

  • Dynamic Games of Asymmetric Information for Deceptive Autonomous Vehicles
    arXiv.cs.GT Pub Date : 2019-06-30
    Linan Huang; Quanyan Zhu

    This paper studies rational and persistent deception among intelligent robots to enhance the security and operation efficiency of autonomous vehicles. We present an N-person K-stage nonzero-sum game with an asymmetric information structure where each robot's private information is modeled as a random variable or its type. The deception is persistent as each robot's private type remains unknown to other

  • Learning Zero-Sum Simultaneous-Move Markov Games Using Function Approximation and Correlated Equilibrium
    arXiv.cs.GT Pub Date : 2020-02-17
    Qiaomin Xie; Yudong Chen; Zhaoran Wang; Zhuoran Yang

    We develop provably efficient reinforcement learning algorithms for two-player zero-sum Markov games in which the two players simultaneously take actions. To incorporate function approximation, we consider a family of Markov games where the reward function and transition kernel possess a linear structure. Both the offline and online settings of the problems are considered. In the offline setting, we

  • Mutants and Residents with Different Connection Graphs in the Moran Process
    arXiv.cs.GT Pub Date : 2017-10-19
    Themistoklis Melissourgos; Sotiris Nikoletseas; Christoforos Raptopoulos; Paul Spirakis

    The Moran process, as studied by Lieberman et al. [L05], is a stochastic process modeling the spread of genetic mutations in populations. In this process, agents of a two-type population (i.e. mutants and residents) are associated with the vertices of a graph. Initially, only one vertex chosen u.a.r. is a mutant, with fitness $r > 0$, while all other individuals are residents, with fitness $1$. In

  • Strategic Contention Resolution in Multiple Channels
    arXiv.cs.GT Pub Date : 2018-10-10
    George Christodoulou; Themistoklis Melissourgos; Paul G. Spirakis

    We consider the problem of resolving contention in communication networks with selfish users. In a \textit{contention game} each of $n \geq 2$ identical players has a single information packet that she wants to transmit using one of $k \geq 1$ multiple-access channels. To do that, a player chooses a slotted-time protocol that prescribes the probabilities with which at a given time-step she will attempt

  • Approximating the Existential Theory of the Reals
    arXiv.cs.GT Pub Date : 2018-10-02
    Argyrios Deligkas; John Fearnley; Themistoklis Melissourgos; Paul G. Spirakis

    The Existential Theory of the Reals (ETR) consists of existentially quantified Boolean formulas over equalities and inequalities of polynomial functions of variables in $\mathbb{R}$. In this paper we propose and study the approximate existential theory of the reals ($\epsilon$-ETR), in which the constraints only need to be satisfied approximately. We first show that when the domain of the variables

  • The Strahler number of a parity game
    arXiv.cs.GT Pub Date : 2020-03-19
    Laure Daviaud; Marcin Jurdziński; K. S. Thejaswini

    The Strahler number of a rooted tree is the largest height of a perfect binary tree that is its minor. The Strahler number of a parity game is proposed to be defined as the smallest Strahler number of the tree of any of its attractor decompositions. It is proved that parity games can be solved in quasi-linear space and in time that is polynomial in the number of vertices~$n$ and linear in $({d}/{2k})^k$

  • Existence of an equilibrium for pure exchange economy with fuzzy preferences
    arXiv.cs.GT Pub Date : 2020-03-19
    Xia Zhang; Hao Sun; Xuanzhu Jin; Moses Olabhele Esangbedo

    This paper focuses on a new model to reach the existence of equilibrium in a pure exchange economy with fuzzy preferences (PXE-FP). The proposed model integrates exchange, consumption and the agent's fuzzy preference in the consumption set. We set up a new fuzzy binary relation on the consumption set to evaluate the fuzzy preferences. Also, we prove that there exists a continuous fuzzy order-preserving

  • Last-Iterate Convergence: Zero-Sum Games and Constrained Min-Max Optimization
    arXiv.cs.GT Pub Date : 2018-07-11
    Constantinos Daskalakis; Ioannis Panageas

    Motivated by applications in Game Theory, Optimization, and Generative Adversarial Networks, recent work of Daskalakis et al \cite{DISZ17} and follow-up work of Liang and Stokes \cite{LiangS18} have established that a variant of the widely used Gradient Descent/Ascent procedure, called "Optimistic Gradient Descent/Ascent (OGDA)", exhibits last-iterate convergence to saddle points in {\em unconstrained}

  • Limited Lookahead in Imperfect-Information Games
    arXiv.cs.GT Pub Date : 2019-02-17
    Christian Kroer; Tuomas Sandholm

    Limited lookahead has been studied for decades in perfect-information games. We initiate a new direction via two simultaneous deviation points: generalization to imperfect-information games and a game-theoretic approach. We study how one should act when facing an opponent whose lookahead is limited. We study this for opponents that differ based on their lookahead depth, based on whether they, too,

  • To Infinity and Beyond: Scaling Economic Theories via Logical Compactness
    arXiv.cs.GT Pub Date : 2019-06-25
    Yannai A. Gonczarowski; Scott Duke Kominers; Ran I. Shorrer

    Many economic-theoretic models incorporate finiteness assumptions that, while introduced for simplicity, play a real role in the analysis. Such assumptions introduce a conceptual problem, as results that rely on finiteness are often implicitly nonrobust; for example, they may rely on edge effects or artificial boundary conditions. Here, we present a unified method that enables us to remove finiteness

  • An Iterative Quadratic Method for General-Sum Differential Games with Feedback Linearizable Dynamics
    arXiv.cs.GT Pub Date : 2019-10-01
    David Fridovich-Keil; Vicenc Rubies-Royo; Claire J. Tomlin

    Iterative linear-quadratic (ILQ) methods are widely used in the nonlinear optimal control community. Recent work has applied similar methodology in the setting of multiplayer general-sum differential games. Here, ILQ methods are capable of finding local equilibria in interactive motion planning problems in real-time. As in most iterative procedures, however, this approach can be sensitive to initial

  • Adversarial Risk Analysis for First-Price Sealed-Bid Auctions
    arXiv.cs.GT Pub Date : 2019-11-22
    Muhammad Ejaz; Chaitanya Joshi; Stephen Joe

    Adversarial Risk Analysis (ARA) is an upcoming methodology that is considered to have advantages over the traditional decision theoretic and game theoretic approaches. ARA solutions for first-price sealed-bid (FPSB) auctions have been found but only under strong assumptions which make the model somewhat unrealistic. In this paper, we use ARA methodology to model FPSB auctions using more realistic assumptions

  • The value of randomized strategies in distributionally robust risk averse network interdiction games
    arXiv.cs.GT Pub Date : 2020-03-17
    Utsav Sadana; Erick Delage

    Conditional Value at Risk (CVaR) is widely used to account for the preferences of a risk-averse agent in the extreme loss scenarios. To study the effectiveness of randomization in interdiction games with an interdictor that is both risk and ambiguity averse, we introduce a distributionally robust network interdiction game where the interdictor randomizes over the feasible interdiction plans in order

  • Dynamic Information Design: A Simple Problem on Optimal Sequential Information Disclosure
    arXiv.cs.GT Pub Date : 2020-03-17
    Farzaneh Farhadi; Demosthenis Teneketzis

    We study a dynamic information design problem in a finite-horizon setting consisting of two strategic and long-term optimizing agents, namely a principal (he) and a detector (she). The principal observes the evolution of a Markov chain that has two states, one "good" and one "bad" absorbing state, and has to decide how to sequentially disclose information to the detector. The detector's only information

  • Maximizing Influence-based Group Shapley Centrality
    arXiv.cs.GT Pub Date : 2020-03-17
    Ruben Becker; Gianlorenzo D'Angelo; Hugo Gilbert

    One key problem in network analysis is the so-called influence maximization problem, which consists in finding a set $S$ of at most $k$ seed users, in a social network, maximizing the spread of information from $S$. This paper studies a related but slightly different problem: We want to find a set $S$ of at most $k$ seed users that maximizes the spread of information, when $S$ is added to an already

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全球疫情及响应:BMC Medicine专题征稿