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We consider the problem of distributed learning, where a network of agents collectively aim to agree on a hypothesis that best explains a set of distributed observations of conditionally independent random processes. We propose a distributed algorithm and establish consistency, as well as a nonasymptotic, explicit, and geometric convergence rate for the concentration of the beliefs around the set...
We discuss collective decision-making and learning capabilities of social networks in the presence of uncertainty. We present a discrete-time decision-making model for a network of agents in an uncertain environment wherein no agent has a model of the environment evolution. The environment impact on the agent network is captured through a sequence of cost functions, where the costs are revealed to...
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