Quantitative convergence for displacement monotone Mean Field Games of control

TL;DR

This paper proves quantitative convergence of Nash equilibria in N-player stochastic differential games within Mean Field Games of Controls, handling non-separable Hamiltonians and common noise through a novel fixed-point analysis.

Contribution

It introduces a rigorous analysis of a complex fixed-point problem in MFGC, establishing convergence results for both open and closed-loop equilibria under general conditions.

Findings

Quantitative convergence of open-loop Nash equilibria.

Convergence of closed-loop equilibria with regularity estimates.

Framework accommodates models with common noise.

Abstract

In this paper we establish quantitative convergence results for both open and closed-loop Nash equilibria of N-player stochastic differential games in the setting of Mean Field Games of Controls (MFGC), a class of models where interactions among agents occur through both states and controls. Our analysis covers a general class of non-separable Hamiltonians satisfying a displacement monotonicity condition, along with mild regularity and growth conditions at infinity. A major novelty of our work is the rigorous treatment of a nontrivial fixed-point problem on a space of measures, which arises naturally in the MFGC formulation. Unlike prior works that either restrict to separable Hamiltonians - rendering the fixed-point map trivial - or assume convergence or regularity properties of the fixed point map, we develop a detailed structural analysis of this equation and its N-player analogue. This leads to new regularity results for the fixed-point maps and, in turn, to quantitative convergence of open-loop equilibria. We further derive sharp a priori estimates for the N-player Nash system, enabling us to control the discrepancy between open and closed-loop strategies, and thus to conclude the convergence of closed-loop equilibria. Our framework also accommodates common noise in a natural way.