Social networks : rational learning and information aggregation

[摘要] (cont.) We show that asymptotic learning does not occur in many classes of network topologies, but, surprisingly, it happens in a family of stochastic networks that has infinitely many agents observing the actions of neighbors that are not sufficiently persuasive. Finally, we characterize equilibria in a generalized environment with heterogeneity of preferences and show that, contrary to a nave intuition, greater diversity (heterogeneity) 3 facilitates asymptotic learning when agents observe the full history of past actions. In contrast, we show that heterogeneity of preferences hinders information aggregation when each agent observes only the action of a single neighbor.

[发布机构] Massachusetts Institute of Technology