Maximum principle for discrete time mean-field stochastic optimal control problems

TL;DR

This paper develops a new maximum principle for discrete-time mean-field stochastic control problems, introducing novel backward stochastic equations to derive optimality conditions, with applications to economic decision-making.

Contribution

It presents a new version of the maximum principle for discrete-time mean-field stochastic control, incorporating discrete backward stochastic equations with mean-field terms.

Findings

Derived necessary and sufficient optimality conditions.

Introduced discrete backward stochastic equations with mean-field.

Validated results through an economic optimization example.

Abstract

In this paper, we study the optimal control of a discrete-time stochastic differential equation (SDE) of mean-field type, where the coefficients can depend on both a function of the law and the state of the process. We establish a new version of the maximum principle for discrete-time stochastic optimal control problems. Moreover, the cost functional is also of the mean-field type. This maximum principle differs from the classical principle since we introduce new discrete-time backward (matrix) stochastic equations. Based on the discrete-time backward stochastic equations where the adjoint equations turn out to be discrete backward SDEs with mean field, we obtain necessary first-order and sufficient optimality conditions for the stochastic discrete optimal control problem. To verify, we apply the result to production and consumption choice optimization problem.