Classifying Convergence Complexity of Nash Equilibria in Graphical Games Using Distributed Computing Theory

TL;DR

This paper connects the complexity of Nash equilibria in graphical games to local graph algorithms, providing new bounds on convergence times and insights into the efficiency of best-response dynamics.

Contribution

It introduces a novel framework linking graphical game equilibria to locally verifiable labelings, enabling the derivation of lower bounds on convergence times and inefficiency measures.

Findings

Distributed convergence can be provably slow.

Established bounds on time-constrained inefficiency of best responses.

Provided insights into strategy-proof algorithm convergence.

Abstract

Graphical games are a useful framework for modeling the interactions of (selfish) agents who are connected via an underlying topology and whose behaviors influence each other. They have wide applications ranging from computer science to economics and biology. Yet, even though a player's payoff only depends on the actions of their direct neighbors in graphical games, computing the Nash equilibria and making statements about the convergence time of "natural" local dynamics in particular can be highly challenging. In this work, we present a novel approach for classifying complexity of Nash equilibria in graphical games by establishing a connection to local graph algorithms, a subfield of distributed computing. In particular, we make the observation that the equilibria of graphical games are equivalent to locally verifiable labelings (LVL) in graphs; vertex labelings which are verifiable with a constant-round local algorithm. This connection allows us to derive novel lower bounds on the convergence time to equilibrium of best-response dynamics in graphical games. Since we establish that distributed convergence can sometimes be provably slow, we also introduce and give bounds on an intuitive notion of "time-constrained" inefficiency of best responses. We exemplify how our results can be used in the implementation of mechanisms that ensure convergence of best responses to a Nash equilibrium. Our results thus also give insight into the convergence of strategy-proof algorithms for graphical games, which is still not well understood.