Networked Anti-coordination Games Meet Graphical Dynamical Systems: Equilibria and Convergence
Keywords:MAS: Agent/AI Theories and Architectures, MAS: Coordination and Collaboration, MAS: Other Foundations of Multiagent Systems
AbstractEvolutionary anti-coordination games on networks capture real-world strategic situations such as traffic routing and market competition. Two key problems concerning evolutionary games are the existence of a pure Nash equilibrium (NE) and the convergence time. In this work, we study these two problems for anti-coordination games under sequential and synchronous update schemes. For each update scheme, we examine two decision modes based on whether an agent considers its own previous action (self essential) or not (self non-essential) in choosing its next action. Using a relationship between games and dynamical systems, we show that for both update schemes, finding an NE can be done efficiently under the self non-essential mode but is computationally intractable under the self essential mode. We then identify special cases for which an NE can be obtained efficiently. For convergence time, we show that the dynamics converges in a polynomial number of steps under the synchronous scheme; for the sequential scheme, the convergence time is polynomial only under the self non-essential mode. Through experiments, we empirically examine the convergence time and the equilibria for both synthetic and real-world networks.
How to Cite
Qiu, Z., Chen, C., Marathe, M. V., Ravi, S. S., Rosenkrantz, D. J., Stearns, R. E., & Vullikanti, A. (2023). Networked Anti-coordination Games Meet Graphical Dynamical Systems: Equilibria and Convergence. Proceedings of the AAAI Conference on Artificial Intelligence, 37(10), 11663-11671. https://doi.org/10.1609/aaai.v37i10.26378
AAAI Technical Track on Multiagent Systems