A new probabilistic approach for mean field games of optimal stopping

We propose a novel probabilistic formulation for optimal stopping mean field games (OS-MFGs) with randomized strategies.

We characterize mean field equilibria through a new class of coupled forward-backward systems, termed coupled reflected forward-backward McKean--Vlasov stochastic differential equations (MKV-RFBSDEs).

An equilibrium is represented by a quintuple $(X,Y,Z,A,L)$, where $L$ is an adapted, $[0,1]$-valued, non-increasing càdlàg process representing the randomized stopping strategy.

The optimality of randomized stopping strategies is characterized through two novel Skorokhod-type conditions involving $L$.

This characterization is new even for classical optimal stopping problems without mean field interactions.

We rigorously prove an equivalence between solutions of the MKV-RFBSDE system and OS-MFG equilibria in randomized strategies.

We establish the existence of equilibria by applying the Kakutani--Fan--Glicksberg fixed-point theorem to a set-valued best-response correspondence, relying on new stability, compactness, and continuity results for the coupled MKV-RFBSDE system.

We also prove uniqueness under suitable conditions.

Under alternative monotonicity assumptions, we develop a new order-theoretic approach based on Tarski's fixed-point theorem, yielding the existence of extremal equilibria and constructive schemes for the minimal and maximal solutions.

We further show that a mean field equilibrium induces an approximate Nash equilibrium for the associated $N$-player stopping game.

Finally, we connect our probabilistic formulation with the analytical approach characterized by a coupled system of constrained partial differential equations.