Distributed Decision-Making for Community Structured Networks

Traditional social learning frameworks consider environments with a homogeneous state where each agent receives observations conditioned on the same hypothesis. In this work, we study the distributed hypothesis testing problem for graphs with a community structure, assuming that each cluster receives data conditioned on some different true state. This situation arises in many scenarios, such as when sensors are spatially distributed, or when individuals in a social network have differing views or opinions. We show that the adaptive social learning strategy is not only superior in nonstationary environments, but also allows each cluster to discover its own truth.