Special Session 182: Recent developments on mathematical finance, stochastic control and related topics

Major-Minor Mean Field Game of Stopping: An Entropy Regularization Approach
Jiacheng Zhang
the Chinese University of Hong Kong
Hong Kong
Co-Author(s):    Xiang Yu, Keyu Zhang, Zhou Zhou
Abstract:
This paper studies a discrete-time major-minor mean field game of stopping where the major player can choose either an optimal control or stopping time. We look for the relaxed equilibrium as a randomized stopping policy, which is formulated as a fixed point of a set-valued mapping, whose existence is challenging by direct arguments. To overcome the difficulties caused by the presence of a major player, we propose to study an auxiliary problem by considering entropy regularization in the major player`s problem while formulating the minor players` optimal stopping problems as linear programming over occupation measures. We first show the existence of regularized equilibria as fixed points of some simplified set-valued operator using the Kakutani-Fan-Glicksberg fixed-point theorem. Next, we prove that the regularized equilibrium converges as the regularization parameter \lambda tends to 0, and the limit corresponds to a fixed point of the original operator, thereby confirming the existence of a relaxed equilibrium in the original mean field game problem. We also extend this entropy regularization method to the mean-field game problem where the minor players choose optimal controls.