In this paper we generalized static team theory to dynamic team theory, in the context of stochastic differential decision system with decentralized noiseless feedback information structures. We apply Girsanov's theorem to transformed the initial stochastic dynamic team problem to an equivalent team problem , under a reference probability space, with state process and information structures independent of any of the team decisions. Subsequently, we derive team and Person-by-Person (PbP)

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... ty conditions, via the stochastic Pontryagin's maximum principle, consisting of forward and backward stochastic differential equations, and a set of conditional variational Hamiltonians with respect to the information structures of the team members. Finally, we relate the backward stochastic differential equation to the value process of the stochastic team problem.