Quantitative convergence rates for extended mean field games with volatility control

TL;DR

This paper establishes quantitative convergence rates for extended mean field games with volatility control, using stochastic maximum principle and propagation of chaos, applicable to linear-quadratic cases.

Contribution

It provides the first explicit convergence rates for Nash equilibria in controlled volatility mean field games under standard monotonicity conditions.

Findings

Derived explicit convergence rates for N-player games to mean field equilibrium.

Proved well-posedness of conditional McKean--Vlasov FBSDEs.

Demonstrated applicability in linear-quadratic models.

Abstract

We investigate the convergence of symmetric stochastic differential games with interactions via control, where the volatility terms of both idiosyncratic and common noises are controlled. We apply the stochastic maximum principle, following the approach of Lauri\`{e}re and Tangpi, to reduce the convergence analysis to the study of forward-backward propagation of chaos. Under the standard monotonicity conditions, we derive quantitative convergence rates for open-loop Nash equilibria of $N$-player stochastic differential games toward the corresponding mean field equilibrium. As a prerequisite, we also establish the well-posedness of the conditional McKean--Vlasov forward-backward stochastic differential equations by the method of continuation. Moreover, we analyze a specific class of linear-quadratic settings to demonstrate the applicability of our main result.