# Computing Evolutionarily Stable Strategies in Imperfect-Information Games

arXiv: https://arxiv.org/abs/2512.10279 · HTML: https://mlsanity.com/paper/2512.10279/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 11 Dec 2025.

## Builds on (most cited first, 4 of 4)

- [The Exact Computational Complexity of Evolutionarily Stable Strategies](https://mlsanity.com/paper/1805.02226/) (2018)
- [Quadratic Programming Approach for Nash Equilibrium Computation in Multiplayer Imperfect-Information Games](https://mlsanity.com/paper/2509.25618/) (2025)
- [Efficient evolutionary dynamics with extensive-form games](https://mlsanity.com/paper/1304.1456/) (2013)
- [Computing Evolutionarily Stable Strategies in Multiplayer Games](https://mlsanity.com/paper/2511.20859/) (2025)

## Built on by (most cited first, 1 of 1)

- [Evolutionarily Stable Stackelberg Equilibrium](https://mlsanity.com/paper/2603.18385/) (2026)

## Abstract (arXiv)

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 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.