Potential Games

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2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Priority Coordination Games: Hodge Decomposition and a Sharp Design Limit
Oct 5, 2026Zhihao Lin, Jianglin Lan, Anh-Tu Nguyen +1Multi-Agent Coordination Game Theory
Graduate School of Information Science and Technology, The University of Osaka, Suita, Osaka 565-0871, Japan · James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, United Kingdom · LAMIH UMR CNRS 8201 Laboratory, Université Polytechnique Hauts-de-France, 59313 Valenciennes, France +2
Distributed Algorithms for $α$ -Potential Functions in General-Sum Games
Oct 4, 2026Yifei Chen, Chinmay MaheshwariDistributed Optimization Potential Games
Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, Maryland, USA · Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, USA
Decentralized Decision-Making among Heterogeneous Autonomous Vehicles: An $α$ -Potential Game Framework
Sep 30, 2026Anran Hu, Zhexin Wang, Yufei Zhang +1Game Theory Autonomous Driving
Department of Industrial Engineering and Operations Research, Columbia University · Department of Mathematics, Imperial College London, London, UK · Department of Civil Engineering and Engineering Mechanics, Data Science Institute, Columbia University
Stable and Budget-Feasible Coalition Formation for Clustered Federated Learning: A Hedonic Potential-Game Approach
Jul 29, 2026Cengis HasanCooperative Game Theory Clustering
Cognifinity, Luxembourg
Distributed Constraint Optimization via Online Learning and Iterative Pricing with Application to Large-Scale Satellite Scheduling
Jul 28, 2026Itai Zilberstein, Pranav Rajbhandari, Steve Chien +1Distributed Optimization Potential Games
Department of Computer Science, Carnegie Mellon University · Jet Propulsion Laboratory, California Institute of Technology · Additional affiliations: Strategy Robot, Inc., Strategic Machine, Inc., Optimized Markets, Inc.
Pure Nash Equilibria under the Affine Mechanism: A Potential Game of Exaggeration
Jun 27, 2026Jason Jisen Li, Young Wu, Yancheng Zhu +2Nash Equilibrium Mechanism Design
University of Wisconsin - Madison
Exit-and-Join Dynamics for Decentralized Coalition Formation
Jun 18, 2026Quanyan ZhuCooperative Game Theory Potential Games
Department of Electrical and Computer Engineering New York University Tandon School of Engineering Brooklyn, NY, USA
A four-player potential game for barren-plateau-aware quantum ansatz design
Apr 23, 2026Rubén Darío GuerreroQuantum Chemistry Barren Plateaus
Parametrized-QC-Graphs Project
Toward Cooperative Driving in Mixed Traffic: An Adaptive Potential Game-Based Approach with Field Test Verification
Apr 22, 2026Shiyu Fang, Xiaocong Zhao, Xuekai Liu +4Intelligent Transportation Systems Autonomous Driving
Key Laboratory of Road and Traffic Engineering of the State, Ministry of Education, Tongji University, Shanghai, 201804, China · State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing, 100084, China · State Key Lab of Intelligent Transportation System, School of Transportation Science and Engineering, Beihang University, Beijing, 100191, China
Cooperate to Compete: Strategic Data Generation and Incentivization Framework for Coopetitive Cross-Silo Federated Learning
Apr 16, 2026Thanh Linh Nguyen, Nguyen Van Huynh, Quoc-Viet PhamSynthetic Data Generation Potential Games
School of Computer Science and Statistics, Trinity College Dublin, Dublin 2, D02 PN40, Ireland · School of Computer Science and Informatics, University of Liverpool, Liverpool, L69 3DR, UK
Game-theoretic Regulated Decentralized Coordination for Airspace Sector Overload Mitigation
Nov 14, 2025Jaehan Im, Daniel Delahaye, David Fridovich-Keil +1Multi-Agent Coordination Air Traffic Management
The University of Texas at Austin, 110 Inner Campus Drive, Austin, 78712, Texas, United States · École Nationale de lAviation Civile, 7 Avenue Édouard Belin, CS 54005, Toulouse Cedex 4, 31055, France
A Game-Theoretic Spatio-Temporal Reinforcement Learning Framework for Collaborative Public Resource Allocation
Oct 30, 2025Songxin Lei, Qiongyan Wang, Yanchen Zhu +6Multi-Agent Coordination Reinforcement Learning
The Hong Kong University of Science and Technology (Guangzhou) · Peking University · Beijing Institute of Technology +1
Nonlinear Equilibrium Transitions in a Potential Game Model for Federated Learning
Nov 18, 2024Kang Liu, Ziqi Wang, Enrique ZuazuaNash Equilibrium Game Theory
Institut de Mathématiques de Bourgogne, Université Bourgogne Europe, CNRS, Dijon, 21000, France · Chair for Dynamics, Control, Machine Learning and Numerics – Alexander von Humboldt Professorship, Department of Mathematics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstrasse 11, Erlangen, 91058, Germany · Chair of Computational Mathematics, Fundación Deusto, Avenida de las Universidades, 24, Bilbao, 48007, Basque Country, Spain +1

Oct 5, 2026, cs.GTJ/K move · Enter open · S save

Priority Coordination Games: Hodge Decomposition and a Sharp Design Limit

Zhihao Lin, Jianglin Lan, Anh-Tu Nguyen, Yoshinobu Kawahara

Graduate School of Information Science and Technology, The University of Osaka, Suita, Osaka 565-0871, Japan · James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, United Kingdom · LAMIH UMR CNRS 8201 Laboratory, Université Polytechnique Hauts-de-France, 59313 Valenciennes, France+2

Multi-Agent Coordination Game Theory Potential Games

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In decentralised priority coordination, agents announce priority levels and a shared resource serves them in decreasing order, as at an unsignalised intersection; the levels form the decision layer of a hierarchical controller. Such interactions are routinely replaced by a potential game, i.e.\ by a common objective, for analysis and design. This paper determines what that surrogate misses, using the Hodge decomposition of the incentives into a potential component, which a common objective can represent, and a harmonic component, which it cannot. For the linear payoff, both components are obtained in closed form on every conflict graph and for every deterministic tie-breaking protocol: in common units, the harmonic energy is the number of conflicts and the potential energy adds the number of adjacent pairs of conflicts. Consequently, for every rationality parameter, the best common-objective model of the agents' choice log-odds, weighted uniformly over unilateral moves, has a relative squared error of at least

1/(d_{\max}+1)

, where

d_{\max}

is the largest number of conflicts of one agent; for an eight-vehicle intersection it is exactly one fifth, for any number of priority levels. Invisible to strict-improvement dynamics, the missed component is, under low-rationality log-linear learning with uniform revision and to leading order, the stationary probability current, and its energy sets the entropy-production rate. Payoff design cannot remove it: on the complete conflict graph of

N

agents, under a total-order protocol and with at least three priority levels, every nonconstant rank-based payoff leaves a relative error of at least

1/N

, with equality exactly for affine payoffs.

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