An Optimization Approach for Approximate Nash Equilibria

Abstract

In this paper we propose a new methodology for determining approximate Nash equilibria of noncooperative bimatrix games, and based on that, we provide an efficient algorithm that computes $0.3393$-approximate equilibria, the best approximation to date. The methodology is based on the formulation of an appropriate function of pairs of mixed strategies reflecting the maximum deviation of the players' payoffs from the best payoff each player could achieve given the strategy chosen by the other. We then seek to minimize such a function using descent procedures. Because it is unlikely to be able to find global minima in polynomial time, given the recently proven intractability of the problem, we concentrate on the computation of stationary points and prove that they can be approximated arbitrarily closely in polynomial time and that they have the above-mentioned approximation property. Our result provides the best $\epsilon$ to date for polynomially computable $\epsilon$-approximate Nash equilibria of bimatrix games. Furthermore, our methodology for computing approximate Nash equilibria has not been used by others.