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How do local agents and global decision makers interact in statistical signal processing problems where autonomous decisions need to be made? When individual agents possess limited sensing, computation, and communication capabilities, can a network of agents achieve sophisticated global behavior? Social learning and Bayesian games are natural settings for addressing these questions. This article presents an overview, novel insights, and a discussion of social learning and Bayesian games in adaptive sensing problems when agents communicate over a network. Two highly stylized examples that demonstrate to the reader the ubiquitous nature of the models, algorithms, and analysis in statistical signal processing are discussed in tutorial fashion.