aiqbench analysis
This research introduces a novel algorithm for computing evolutionarily stable strategies in symmetric extensive-form games with imperfect information, addressing complex multi-agent scenarios where traditional Nash equilibrium methods fall short. By demonstrating scalability on both synthetic random games and an imperfect-information cancer signaling model, the work advances theoretical game theory directly applicable to robust AI agent design and strategic planning under uncertainty.
Computing Evolutionarily Stable Strategies in Imperfect-Information Games
arXiv:2512.10279v5 Announce Type: replace-cross Abstract: We present an algorithm for computing evolutionarily stable strategies (ESSs) in symmetric perfect-recall extensive-form games of imperfect information. Our main algorithm is for two-player games, and we describe how it can be extended to multiplayer games. The algorithm is sound and computes all ESSs in nondegenerate games and a subset of them in degenerate games which contain an infinite continuum of symmetric Nash equilibria. The algorithm is anytime and can be stopped early to find one or more ESSs. We experiment on an imperfect-information cancer signaling game as well as random games to demonstrate scalability.
Abstract:We present an algorithm for computing evolutionarily stable strategies (ESSs) in symmetric perfect-recall extensive-form games of imperfect information. Our main algorithm is for two-player games, and we describe how it can be extended to multiplayer games. The algorithm is sound and computes all ESSs in nondegenerate games and a subset of them in degenerate games which contain an infinite continuum of symmetric Nash equilibria. The algorithm is anytime and can be stopped early to find one or more ESSs. We experiment on an imperfect-information cancer signaling game as well as random games to demonstrate scalability.
Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Theoretical Economics (econ.TH); Populations and Evolution (q-bio.PE)
arXiv:2512.10279 [cs.GT]
(or arXiv:2512.10279v5 [cs.GT] for this version)
Full text under the publisher's open licence · 122 words
