Uniform-in-Time Convergence Rates to a Nonlinear Markov Chain for Mean-Field Interacting Jump Processes

TL;DR

This paper proves uniform-in-time convergence rates for a finite particle system to its mean field limit, establishing error bounds and stability conditions for nonlinear Markov chains in jump processes.

Contribution

It provides a rigorous proof of uniform convergence rates for empirical measures to the mean field limit, using a master equation approach and stability analysis.

Findings

Error between empirical and mean field measures is of order 1/N uniformly in time

Exponential stability of the mean field system is characterized by the linearized Kolmogorov equation

Results apply to mean field games and conditions for stationary distribution existence

Abstract

We consider a system of $N$ particles interacting through their empirical distribution on a finite state space in continuous time. In the formal limit as $N\to\infty$, the system takes the form of a nonlinear (McKean--Vlasov) Markov chain. This paper rigorously establishes this limit. Specifically, under the assumption that the mean field system has a unique, exponentially stable stationary distribution, we show that the weak error between the empirical measures of the $N$-particle system and the law of the mean field system is of order $1/N$ uniformly in time. Our analysis makes use of a master equation for test functions evaluated along the measure flow of the mean field system, and we demonstrate that the solutions of this master equation are sufficiently regular. We then show that exponential stability of the mean field system is implied by exponential stability for solutions of the linearized Kolmogorov equation with a source term. Finally, we show that our results can be applied to the study of mean field games and give a new condition for the existence of a unique stationary distribution for a nonlinear Markov chain.