Distributed coordination for seeking the optimal Nash equilibrium of aggregative games

TL;DR

This paper develops a distributed algorithm for multi-agent aggregative games that converges to the optimal Nash equilibrium, balancing individual incentives with social cost optimization.

Contribution

It introduces a novel distributed coordination method using Tikhonov regularization and dynamical averaging to find the optimal Nash equilibrium in aggregative games.

Findings

Algorithm converges to the optimal Nash equilibrium

Effective coordination improves social cost

Simulation validates theoretical results

Abstract

This paper aims to design a distributed coordination algorithm for solving a multi-agent decision problem with a hierarchical structure. The primary goal is to search the Nash equilibrium of a noncooperative game such that each player has no incentive to deviate from the equilibrium under its private objective. Meanwhile, the agents can coordinate to optimize the social cost within the set of Nash equilibria of the underlying game. Such an optimal Nash equilibrium problem can be modeled as a distributed optimization problem with variational inequality constraints. We consider the scenario where the objective functions of both the underlying game and social cost optimization problem have a special aggregation structure. Since each player only has access to its local objectives while cannot know all players' decisions, a distributed algorithm is highly desirable. By utilizing the Tikhonov regularization and dynamical averaging tracking technique, we propose a distributed coordination algorithm by introducing an incentive term in addition to the gradient-based Nash equilibrium seeking, so as to intervene players' decisions to improve the system efficiency. We prove its convergence to the optimal Nash equilibrium of a monotone aggregative game with simulation studies.