Equivalence of decentralized stochastic dynamic decision systems via Girsanov´s measure transformation

In this paper we present two methods for decentralized optimization of continuous and discrete-time stochastic dynamic decision systems, with multiple decision makers having nonclassical information structures. For both methods we apply Girsanov´s change of measure to transform such problems into equivalent optimization problems, under a reference probability measure, with corresponding observations and information structures available for decisions, which are not affected by any of the decisions.