Centralized Versus Decentralized Team Games of Distributed Stochastic Differential Decision Systems with Noiseless Information Structures-Part II: Applications

TL;DR

This paper derives optimal decentralized strategies for distributed stochastic differential systems with noiseless information, illustrating how information sharing reduces computational complexity through examples of nonlinear and linear quadratic team games.

Contribution

It applies stochastic maximum principle and backward-forward stochastic differential equations to derive explicit decentralized strategies, highlighting the impact of information signaling.

Findings

Closed-form optimal strategies for some cases

Information signaling reduces computational complexity

Illustrative examples of nonlinear and linear quadratic team games

Abstract

In this second part of our two-part paper, we invoke the stochastic maximum principle, conditional Hamiltonian and the coupled backward-forward stochastic differential equations of the first part [1] to derive team optimal decentralized strategies for distributed stochastic differential systems with noiseless information structures. We present examples of such team games of nonlinear as well as linear quadratic forms. In some cases we obtain closed form expressions of the optimal decentralized strategies. Through the examples, we illustrate the effect of information signaling among the decision makers in reducing the computational complexity of optimal decentralized decision strategies.