Spatial Branch-and-Bound for Computing Multiplayer Nash Equilibrium

TL;DR

This paper introduces a novel spatial branch-and-bound algorithm for computing multiplayer Nash equilibria, addressing computational challenges and outperforming existing methods in terms of scalability and accuracy.

Contribution

It formulates Nash equilibrium computation as a polynomial complementarity problem and develops a complete, sound spatial branch-and-bound algorithm for it.

Findings

Outperforms existing complete methods in empirical tests.

Provides a qualitative analysis of the approach's expected performance.

Establishes a relationship between solutions of the formulation and approximate Nash equilibria.

Abstract

Equilibria of realistic multiplayer games constitute a key solution concept both in practical applications, such as online advertising auctions and electricity markets, and in analytical frameworks used to study strategic voting in elections or assess policy impacts in integrated assessment models. However, efficiently computing these equilibria requires games to have a carefully designed structure and satisfy numerous restrictions; otherwise, the computational complexity becomes prohibitive. In particular, finding even approximate Nash equilibria in general-sum normal-form games with two or more players is known to be PPAD-complete. Current state-of-the-art algorithms for computing Nash equilibria in multiplayer normal-form games either suffer from poor scalability due to their reliance on non-convex optimization solvers, or lack guarantees of convergence to a true equilibrium. In this paper, we propose a formulation of the Nash equilibrium computation problem as a polynomial complementarity problem and develop a complete and sound spatial branch-and-bound algorithm based on this formulation. We provide a qualitative analysis arguing why one should expect our approach to perform well, and show the relationship between approximate solutions to our formulation and that of computing an approximate Nash equilibrium. Empirical evaluations demonstrate that our algorithm substantially outperforms existing complete methods.