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In this paper we generalized static team theory to dynamic team theory, inthe context of stochastic differential decision system with decentralizednoiseless feedback information structures.  We apply Girsanov's theorem to transformed the initial stochastic dynamicteam problem to an equivalent team problem, under a reference probabilityspace, with state process and information structures independent of any of theteam decisions. Subsequently, we show, under certain conditions, thatcontinuous-time and discrete-time stochastic dynamic team problems, can betransformed to equivalent static team problems, although computing the optimalteam strategies using this method might be computational intensive. Therefore,we propose an alternative method, by deriving team and Person-by-Person (PbP)optimality conditions, via the stochastic Pontryagin's maximum principle,consisting of forward and backward stochastic differential equations, and a setof conditional variational Hamiltonians with respect to the informationstructures of the team members.  Finally, we relate the backward stochastic differential equation to the valueprocess of the stochastic team problem.

Dynamic Team Theory of Stochastic Differential Decision Systems with  Decentralized Noiseless Feedback Information Structures via Girsanov's  Measure Transformation