The Complexity of Equilibrium Refinements in Potential Games

TL;DR

This paper investigates the computational complexity of equilibrium refinements in potential games, revealing both hardness results and efficient algorithms for specific subclasses, and establishing new theoretical connections in game theory.

Contribution

It closes the gap on the complexity of computing equilibrium refinements in potential games, providing hardness results, polynomial-time algorithms for structured classes, and new insights into the landscape of game equilibria.

Findings

Pure perfect equilibrium computation is PLS-complete in concise potential games.

Polynomial-time algorithms exist for symmetric network and matroid congestion games.

Exponential separation in response path length between perfect and Nash equilibria.

Abstract

The complexity of computing equilibrium refinements has been at the forefront of algorithmic game theory research, but it has remained open in the seminal class of potential games; we close this fundamental gap in this paper. We first show that computing a pure(-strategy) perfect or proper equilibrium is $\mathsf{PLS}$-complete in concise potential games in normal form. For pure perfect equilibria, we extend this result to general polytope games, which includes extensive-form games. We next turn to more structured classes of games, namely symmetric network congestion and symmetric matroid congestion games. For both classes, we show that a pure perfect equilibrium can be computed in polynomial time, strengthening the existing results for pure Nash equilibria. More broadly, we make a connection between strongly polynomial-time algorithms and efficient perturbed optimization using fractional interpolation. On the other hand, we establish that, for a certain class of potential games, there is an exponential separation in the length of the best-response path between perfect and Nash equilibria. Finally, for mixed strategies, we prove that computing a point geometrically near a perfect equilibrium requires a doubly exponentially small perturbation even in $3$-player potential games in normal form. As a byproduct, this significantly strengthens and simplifies a seminal result of Etessami and Yannakakis (FOCS '07). On the flip side, in the special case of polymatrix potential games, we show that equilibrium refinements are amenable to perturbed gradient descent dynamics, thereby belonging to the complexity class $\mathsf{CLS}$. This provides a principled and practical way of refining the landscape of gradient descent in constrained optimization.