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date: 19 October 2019

Abstract and Keywords

This survey covers models of how agents update behaviors and beliefs using information conveyed through social connections. The chapter begins with sequential social learning models, in which each agent makes a decision once and for all after observing a subset of prior decisions; the discussion is organized around the concepts of diffusion and aggregation of information. Next, the chapter presents the DeGroot framework of average-based repeated updating, whose long- and medium-run dynamics can be completely characterized in terms of measures of network centrality and segregation. Finally, the chapter turns to various models of repeated updating that feature richer optimizing behavior, and concludes by urging the development of network learning theories that can deal adequately with the observed phenomenon of persistent disagreement.

Keywords: social learning, informational herding, cascades, DeGroot, Bayesian, consensus, imitation, homophily, speed of convergence

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