Computational Complexity of Multi-Player Evolutionarily Stable Strategies

TL;DR

This paper investigates the computational complexity of finding evolutionarily stable strategies (ESS) in multi-player symmetric games, revealing high complexity levels and establishing connections to advanced complexity classes.

Contribution

It extends complexity results from two-player to multi-player games, linking ESS decision problems to the second level of the real polynomial time hierarchy.

Findings

Deciding the existence of an ESS is hard for a class denoted as exists D . forall R.

Deciding whether a specific strategy is an ESS is complete for orall R.

Deciding the existence of a locally superior strategy (LSS) is similarly complex.

Abstract

In this paper we study the computational complexity of computing an evolutionary stable strategy (ESS) in multi-player symmetric games. For two-player games, deciding existence of an ESS is complete for {\Sigma} 2 , the second level of the polynomial time hierarchy. We show that deciding existence of an ESS of a multi-player game is closely connected to the second level of the real polynomial time hierarchy. Namely, we show that the problem is hard for a complexity class we denote as \exists D . \forall R and is a member of \exists\forall R, where the former class restrict the latter by having the existentially quantified variables be Boolean rather then real-valued. As a special case of our results it follows that deciding whether a given strategy is an ESS is complete for \forall R. A concept strongly related to ESS is that of a locally superior strategy (LSS). We extend our results about ESS and show that deciding existence of an LSS of a multiplayer game is likewise hard for \exists D \forall R and a member of \exists\forall R, and as a special case that deciding whether a given strategy is an LSS is complete for \forall R.