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The objective of this paper is twofold. First, a new model of team optimization is formulated. Second, this model is used to investigate the effects of uncertainty on interaction. A model of team optimization that encompasses the classical team decision problem is introduced. This model is suitable for problems where agents' posterior information is not shared and is possibly inconsistent with the mutual prior information. For a broad class of problems, every agent's dominant beliefs about the posterior information of the other agents are derived. Then, the level of interaction and the level of uncertainty are defined, and the relationship between these two levels is studied. It is shown that the optimal level of interaction decreases as the level of uncertainty increases, and in some cases, the optimal level of interaction tends to zero, suggesting that the optimization problem may be decomposed. The theoretical results are demonstrated on sensor network examples.