Long time behaviour of Mean Field Games with fractional diffusion
Olav Ersland, Espen Robstad Jakobsen, Alessio Porretta
TL;DR
The paper analyzes long-time behavior of mean field games driven by non-Gaussian noise via fractional diffusion, establishing an exponential turnpike to a unique stationary ergodic state under Lasry–Lions monotonicity. The approach combines nonlocal HJB and Fokker–Planck analyses with truncation, fixed-point arguments, and duality, yielding uniform-in-time regularity and stability, and culminating in a quantitative turnpike inequality. This provides the first exponential turnpike result for MFGs with Lévy processes lacking Brownian components and highlights the role of a confining drift in ensuring ergodicity. The results have implications for understanding and computing long-horizon equilibria in large populations under jump-type dynamics and nonlocal interactions.
Abstract
In this paper we study the long time behaviour of mean field games systems with fractional diffusion, modeling the case that the individual dynamics of the players is driven by independent jump processes and controlled through the drift term, while being confined by an external field in order to guarantee ergodicity. In the case of globally Lipschitz, locally uniformly convex Hamiltonian, and weakly coupled costs satisfying the Lasry-Lions monotonicity condition, we prove that there is a unique solution $(u_T,m_T)$ to the mean field game problem in $(0,T)$ and we show that, if $T$ is sufficiently large, $(u_T,m_T)$ satisfies the so-called turnpike property, namely it is exponentially close to the (unique) stationary ergodic state for any proportionally long intermediate time.
