Quickest time change detection with social learning

摘要

How does local and global decision making interact in detection theory? This paper considers multi-agent quickest time change detection with social learning. We show that the optimal decision exhibits a remarkable multi-threshold behavior within the space of Bayesian distributions. For small change probabilities, an explicit characterization of this behavior is obtained in terms of fixed points of the posterior update.