Computing Evolutionarily Stable Strategies in Imperfect-Information Games
This provides a method for analyzing strategic stability in complex games, such as biological modeling, but is incremental as it builds on existing game theory concepts.
The authors tackled the problem of computing evolutionarily stable strategies (ESSs) in symmetric imperfect-information games, presenting an algorithm that is sound for nondegenerate games and scalable in experiments on cancer signaling and random games.
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.