A particle system approach towards the global well-posedness of master equations for potential mean field games of control

TL;DR

This paper establishes the global well-posedness of master equations in potential mean field games of control using a particle system approach, providing uniform estimates and convergence results.

Contribution

It introduces a novel particle system method to prove global well-posedness of master equations in potential MFGC, extending previous local results.

Findings

Proved global well-posedness of HJB/master equations for generalized MFC.

Developed a Lipschitz approximation for optimal feedback functions.

Quantified approximation errors for Nash equilibria based on particle systems.

Abstract

This paper studies the $N$-particle systems as well as the HJB/master equations for a class of generalized mean field control (MFC) problems and the corresponding potential mean field games of control (MFGC). A local in time classical solution for the HJB equation is generated via a probabilistic approach based on the mean field maximum principle. Given an extension of the so called displacement convexity condition, we obtain the uniform estimates on the HJB equation for the $N$-particle system. Such estimates imply the displacement convexity/semi-concavity and thus the prior estimates on the solution to the HJB equation for generalized MFC problems. The global well-posedness of HJB/master equation for generalized MFC/potential MFGC is then proved thanks to the local well-posedness and the prior estimates. In view of the nature of the displacement convexity condition, such well-posedness is also true for the degenerated case. Our analysis on the $N$-particle system also induces an Lipschitz approximator to the optimal feedback function in generalized MFC/potential MFGC where an algebraic convergence rate is obtained. Furthermore, an alternative approximate Nash equilibrium is proposed based on the $N$-particle system, where the approximation error is quantified thanks to the aforementioned uniform estimates.