Smoothed Efficient Algorithms and Reductions for Network Coordination Games

TL;DR

This paper analyzes the smoothed complexity of finding pure Nash equilibria in Network Coordination Games, providing polynomial bounds for certain graph structures and establishing reductions to local-max-cut to extend smoothed algorithms.

Contribution

It introduces smoothed complexity bounds for pure Nash equilibrium computation in network coordination games and develops reductions to extend smoothed algorithms across related problems.

Findings

Polynomial smoothed complexity for complete graphs with constant strategies.

Quasi-polynomial smoothed complexity for arbitrary graphs.

Reductions from network coordination games to local-max-cut.

Abstract

Worst-case hardness results for most equilibrium computation problems have raised the need for beyond-worst-case analysis. To this end, we study the smoothed complexity of finding pure Nash equilibria in Network Coordination Games, a PLS-complete problem in the worst case. This is a potential game where the sequential-better-response algorithm is known to converge to a pure NE, albeit in exponential time. First, we prove polynomial (resp. quasi-polynomial) smoothed complexity when the underlying game graph is a complete (resp. arbitrary) graph, and every player has constantly many strategies. We note that the complete graph case is reminiscent of perturbing all parameters, a common assumption in most known smoothed analysis results. Second, we define a notion of smoothness-preserving reduction among search problems, and obtain reductions from $2$-strategy network coordination games to local-max-cut, and from $k$-strategy games (with arbitrary $k$) to local-max-cut up to two flips. The former together with the recent result of [BCC18] gives an alternate $O(n^8)$-time smoothed algorithm for the $2$-strategy case. This notion of reduction allows for the extension of smoothed efficient algorithms from one problem to another. For the first set of results, we develop techniques to bound the probability that an (adversarial) better-response sequence makes slow improvements on the potential. Our approach combines and generalizes the local-max-cut approaches of [ER14,ABPW17] to handle the multi-strategy case: it requires a careful definition of the matrix which captures the increase in potential, a tighter union bound on adversarial sequences, and balancing it with good enough rank bounds. We believe that the approach and notions developed herein could be of interest in addressing the smoothed complexity of other potential and/or congestion games.