A Polynomial-Time Algorithm for 1/3-Approximate Nash Equilibria in Bimatrix Games

TL;DR

This paper introduces a polynomial-time algorithm that computes a 1/3-approximate Nash equilibrium in bimatrix games, improving the approximation guarantee over previous algorithms and advancing the understanding of computational complexity in game theory.

Contribution

The paper presents a refined algorithm that achieves a 1/3-approximate Nash equilibrium in polynomial time, surpassing the longstanding 0.3393 approximation barrier.

Findings

Achieves a (1/3 + δ)-approximate Nash equilibrium in polynomial time.

Improves the approximation guarantee over the previous state of the art.

Introduces strategy enrichment beyond convex combinations of primal and dual strategies.

Abstract

Since the celebrated PPAD-completeness result for Nash equilibria in bimatrix games, a long line of research has focused on polynomial-time algorithms that compute $\varepsilon$-approximate Nash equilibria. Finding the best possible approximation guarantee that we can have in polynomial time has been a fundamental and non-trivial pursuit on settling the complexity of approximate equilibria. Despite a significant amount of effort, the algorithm of Tsaknakis and Spirakis, with an approximation guarantee of $(0.3393+\delta)$, remains the state of the art over the last 15 years. In this paper, we propose a new refinement of the Tsaknakis-Spirakis algorithm, resulting in a polynomial-time algorithm that computes a $(\frac{1}{3}+\delta)$-Nash equilibrium, for any constant $\delta>0$. The main idea of our approach is to go beyond the use of convex combinations of primal and dual strategies, as defined in the optimization framework of Tsaknakis and Spirakis, and enrich the pool of strategies from which we build the strategy profiles that we output in certain bottleneck cases of the algorithm.