Some models of information aggregation and consensus in networks
Seminar Room 1, Newton Institute
AbstractA primary function of many engineered and social networks is to aggregate the information obtained by the nodes of a network. We discuss a few of the models and thrusts that have been studied, starting with a model of "social learning" by rational (Bayesian) agents, and its connections with information fusion models in the engineering literature. We then consider a set of agents (processors, decision makers, sensors, etc.) who reach consensus through an iterative process involving the exchange and averaging of local values. We discuss a number of models, application contexts, and convergence results, and the connections with Markov chain theory.
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