Risk-sensitive linear-quadratic-Gaussian graphon mean-field games

TL;DR

This paper develops a framework for risk-sensitive mean-field games on infinite networks, establishing well-posedness, decentralized strategies, and epsilon-Nash equilibrium with novel error analysis, supported by a numerical example.

Contribution

It introduces a new approach to risk-sensitive graphon mean-field games, proving well-posedness and equilibrium properties using fixed point and continuity methods.

Findings

Well-posedness of the mean-field game equations is established.

Decentralized strategies form an epsilon-Nash equilibrium.

A numerical example illustrates the theoretical results.

Abstract

This paper investigates a class of linear-quadratic-Gaussian risk-sensitive graphon mean-field games, involving an asymptotically infinite population of heterogeneous agents distributed across an asymptotically infinite network, where each agent aims to minimize an exponential cost functional reflecting its risk sensitivity. Following the Nash certainty equivalence methodology, an auxiliary risk-sensitive optimal control problem is constructed and further combined with a consistency condition to determine decentralized strategies of the agents. The well-posedness of the resulting graphon mean-field game equation system, consisting of a family of fully coupled forward-backward differential equations, is established by a fixed point approach under a contraction condition, and by the method of continuity under an operator monotonicity condition, respectively. To prove the epsilon-Nash equilibrium property of the obtained decentralized strategies, one faces significant challenge since the usual L^2 error estimates on mean-field approximations are no longer adequate due to unboundedness of the integrand in the exponentiated cost. The proof will be accomplished by establishing certain exponentiated error estimates instead of L^2 error estimates. Finally, a numerical example is provided to illustrate our results.