The exponential turnpike phenomenon for mean field game systems: weakly monotone drifts and small interactions

TL;DR

This paper establishes the exponential turnpike phenomenon for mean field game systems with weakly monotone drifts, demonstrating convergence to equilibrium under less restrictive conditions than previous works, using probabilistic coupling methods.

Contribution

It proves well-posedness of the ergodic problem and the exponential turnpike property under weak asymptotic monotonicity assumptions, broadening applicability beyond classical monotonicity conditions.

Findings

Exponential convergence to equilibrium for optimal states and controls.

Applicability to non-compact domains like  space.

Extension to systems with non-constant diffusion coefficients.

Abstract

This article aims at quantifying the long time behavior of solutions of mean field PDE systems arising in the theory of Mean Field Games and McKean-Vlasov control. Our main contribution is to show well-posedness of the ergodic problem and the exponential turnpike property of dynamic optimizers, which implies exponential convergence to equilibrium for both optimal states and controls to their ergodic counterparts. In contrast with previous works that require some version of the Lasry-Lions monotonicity condition, our main assumption is a weak form of asymptotic monotonicity on the drift of the controlled dynamics and some basic regularity and smallness conditions on the interaction terms. Our proof strategy is probabilistic and based on the construction of contractive couplings between controlled processes and forward-backward stochastic differential equations. The flexibility of the coupling approach allows us to cover several interesting situations. For example, we do not need to restrict ourselves to compact domains and can work on the whole space $\mathbb{R}^d$, we can cover the case of non-constant diffusion coefficients and we can sometimes show turnpike estimates for the hessians of solutions to the backward equation.