2022/12/21 by Wei Yi, Yi Wei, Wei, Yi +2
Computer Science · Decision Sciences · Engineering · Mathematics · Physics and Astronomy · #Affine transformation #Algorithm #Artificial intelligence #Best response #Class (philosophy) #Computation #Computer Science and Game Theory (cs.GT) #Computer science #Coordination game #Distributed Control Multi-Agent Systems #Distributed computing #FOS: Computer and information sciences #FOS: Electrical engineering #Feature (linguistics) #Game Theory and Applications #Information Theory (cs.IT) #Mathematical economics #Mathematical optimization #Mathematics #Multiagent Systems (cs.MA) #Nash equilibrium #Nonlinear system #Opinion Dynamics and Social Influence #Robot #Simple (philosophy) #Systems and Control (eess.SY) #Task (project management) #Theoretical computer science #cs.GT #cs.IT #cs.MA #cs.SY #eess.SY #electronic engineering #information engineering #math.IT
paper · pdf · doi:10.48550/arxiv.2212.10968
published in arXiv (Cornell University) (Cornell University) · Submitted to the 57th Conference on Information Science and Systems
arxiv created 2022/12/21 · openalex publication_date 2022/12/21 · arxiv updated 2022/12/22 · openalex created_date 2023/01/04 · openalex updated_date 2026/08/04
Coordination is a desirable feature in multi-agent systems, allowing the execution of tasks that would be impossible by individual agents. We study coordination by a team of strategic agents choosing to undertake one of the multiple tasks. We adopt a stochastic framework where the agents decide between two distinct tasks whose difficulty is randomly distributed and partially observed. We show that a Nash equilibrium with a simple and intuitive linear structure exists for diffuse prior distributions on the task difficulties. Additionally, we show that the best response of any agent to an affine strategy profile can be nonlinear when the prior distribution is not diffuse. Finally, we state an algorithm that allows us to efficiently compute a data-driven Nash equilibrium within the class of affine policies.