Time-inconsistent mean-field stopping problems: A regularized equilibrium approach

Abstract: This paper studies the mean-field Markov decision process (MDP) with the centralized stopping under the non-exponential discount. The problem differs fundamentally from most existing studies on mean-field optimal control/stopping due to its time inconsistency by nature. We look for the subgame perfect relaxed equilibria, namely the randomized stopping policies that satisfy the time-consistent planning with future selves from the perspective of the social planner. On the other hand, unlike many previous studies on time-inconsistent stopping where the decreasing impatience plays a key role, we are interested in the general discount function without imposing any conditions. As a result, the study on the relaxed equilibrium becomes necessary as the pure-strategy equilibrium may not exist in general. We formulate relaxed equilibria as fixed points of a complicated operator, whose existence is challenging by a direct method. To overcome the obstacles, we first introduce the auxiliary problem under the entropy regularization on the randomized policy and the discount function, and establish the existence of the regularized equilibria as fixed points to an auxiliary operator via Schauder fixed point theorem. Next, we show that the regularized equilibrium converges as the regularization parameter $\lambda$ tends to $0$ and the limit corresponds to a fixed point to the original operator, and hence is a relaxed equilibrium. We also establish some connections between the mean-field MDP and the N-agent MDP when $N$ is sufficiently large in our time-inconsistent setting.