Computing Evolutionarily Stable Strategies in Imperfect-Information Games

Fuente: arXiv
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Main Author: Ganzfried, Sam
Format: Preprint
Published: 2025
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author Ganzfried, Sam
author_facet Ganzfried, Sam
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2512_10279
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Computing Evolutionarily Stable Strategies in Imperfect-Information Games
Ganzfried, Sam
Computer Science and Game Theory
Artificial Intelligence
Multiagent Systems
Theoretical Economics
Populations and Evolution
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.
title Computing Evolutionarily Stable Strategies in Imperfect-Information Games
topic Computer Science and Game Theory
Artificial Intelligence
Multiagent Systems
Theoretical Economics
Populations and Evolution
url https://arxiv.org/abs/2512.10279