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New algorithm computes evolutionarily stable strategies in imperfect-information games

A new algorithm has been developed to compute evolutionarily stable strategies (ESSs) in symmetric perfect-recall extensive-form games, particularly those with imperfect information. This algorithm is designed for two-player games but can be extended to multiplayer scenarios. It is capable of finding all ESSs in nondegenerate games and a subset in degenerate games, offering anytime capabilities for early results and demonstrating scalability through experiments on a cancer signaling game and random games. AI

IMPACT This research could advance AI's capabilities in strategic decision-making and game theory applications.

RANK_REASON The cluster contains a research paper detailing a new algorithm for game theory. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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New algorithm computes evolutionarily stable strategies in imperfect-information games

COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Sam Ganzfried ·

    Computing Evolutionarily Stable Strategies in Imperfect-Information Games

    arXiv:2512.10279v3 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 ca…