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 earlier papers in the pure control case.
Funding Statement
Peter Tankov gratefully acknowledges financial support from the ANR (project EcoREES ANR-19-CE05-0042) and from the FIME Research Initiative.
Acknowledgments
We thank Xiaolu Tan for insightful discussions.
Citation
Roxana Dumitrescu. Marcos Leutscher. Peter Tankov. "Control and optimal stopping Mean Field Games: a linear programming approach." Electron. J. Probab. 26 1 - 49, 2021. https://doi.org/10.1214/21-EJP713
Information