Control and optimal stopping Mean Field Games: a linear programming approach

TL;DR

This paper introduces a linear programming framework for mean-field games involving control and optimal stopping, providing existence results, numerical methods, and establishing equivalence with martingale approaches.

Contribution

It extends the linear programming approach to mean-field games with control and stopping, offering a general, flexible, and numerically amenable method with theoretical guarantees.

Findings

Proved existence of solutions under weak assumptions

Established equivalence with controlled martingale solutions

Facilitated numerical implementation of mean-field games

Abstract

We develop the linear programming approach to mean-field games in a general setting. This relaxed control approach allows to prove existence results under weak assumptions, and lends itself well to numerical implementation. We consider mean-field game problems where the representative agent chooses both the optimal control and the optimal time to exit the game, where the instantaneous reward function and the coefficients of the state process may depend on the distribution of the other agents. Furthermore, we establish the equivalence between mean-field games equilibria obtained by the linear programming approach and the ones obtained via the controlled/stopped martingale approach, another relaxation method used in a few previous papers in the case when there is only control.