Mean field stochastic control under sublinear expectation

TL;DR

This paper develops a stochastic maximum principle for mean-field control problems driven by $G$-Brownian motion, addressing the challenges of differentiating $G$-expectations and establishing necessary and sufficient optimality conditions.

Contribution

It introduces a Pontryagin maximum principle for mean-field control under $G$-expectation, including new methods for differentiating $G$-expectations of parameterized random variables.

Findings

Derived necessary optimality conditions under convex control sets.

Established sufficiency of conditions with additional convexity assumptions.

Addressed differentiation of $G$-expectations in the context of controlled stochastic systems.

Abstract

Our work is devoted to the study of Pontryagin's stochastic maximum principle for a mean-field optimal control problem under Peng's $G$-expectation. The dynamics of the controlled state process is given by a stochastic differential equation driven by a $G$-Brownian motion, whose coefficients depend not only on the control, the controlled state process but also on its law under the $G$-expectation. Also the associated cost functional is of mean-field type. Under the assumption of a convex control state space we study the stochastic maximum principle, which gives a necessary optimality condition for control processes. Under additional convexity assumptions on the Hamiltonian it is shown that this necessary condition is also a sufficient one. The main difficulty which we have to overcome in our work consists in the differentiation of the $G$-expectation of parameterized random variables. As particularly delicate it turns out to handle with the $G$-expectation of a function of the controlled state process inside the running cost of the cost function. For this we have to study a measurable selection theorem for set-valued functions whose values are subsets of the representing set of probability measures for the $G$-expectation.