Set Values for Mean Field Games

TL;DR

This paper introduces the concept of set values in mean field games to analyze multiple equilibria, establishing properties like dynamic programming and convergence, thus broadening understanding beyond unique equilibrium scenarios.

Contribution

It proposes the set value approach for mean field games with multiple equilibria and proves key properties like time consistency and convergence without requiring uniqueness.

Findings

Set value satisfies dynamic programming principle.

Convergence of N-player game set values to the mean field set value.

Illustrations in finite state and continuous models.

Abstract

In this paper we study mean field games with possibly multiple mean field equilibria. Instead of focusing on the individual equilibria, we propose to study the set of values over all possible equilibria, which we call the set value of the mean field game. When the mean field equilibrium is unique, typically under certain monotonicity conditions, our set value reduces to the singleton of the standard value function which solves the master equation. The set value is by nature unique, and we shall establish two crucial properties: (i) the dynamic programming principle, also called time consistency; and (ii) the convergence of the set values of the corresponding $N$-player games, which can be viewed as a type of stability result. To our best knowledge, this is the first work in the literature which studies the dynamic value of mean field games without requiring the uniqueness of mean field equilibria. We emphasize that the set value is very sensitive to the type of the admissible controls. In particular, for the convergence one has to restrict to corresponding types of equilibria for the N-player game and for the mean field game. We shall illustrate this point by investigating three cases, two in finite state space models and the other in a continuous time model with controlled diffusions.