Computing Evolutionarily Stable Strategies in Imperfect-Information Games

TL;DR

This paper introduces an algorithm to compute evolutionarily stable strategies in symmetric imperfect-information games, capable of identifying all ESSs in nondegenerate cases and some in degenerate cases, with demonstrated scalability.

Contribution

The paper presents a new algorithm for computing ESSs in imperfect-information games, extendable from two-player to multiplayer scenarios, and capable of handling degenerate and nondegenerate cases.

Findings

Algorithm is sound and computes all ESSs in nondegenerate games.

The algorithm is anytime and can be stopped early to find ESSs.

Demonstrated scalability on cancer signaling and random games.

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.