Bayesian learning in social networks

• 成果类型: Article
• 署名作者: Gale, D; Kariv, S
• 署名单位: New York University
• 刊物名称: GAMES AND ECONOMIC BEHAVIOR
• ISSN/ISSBN: 0899-8256
• DOI: 10.1016/S0899-8256(03)00144-1
• 发表日期: 2003
• 页码: 329-346
• 关键词: NETWORKS; social learning; Herd behavior; Informational cascades

摘要

We extend the standard model of social learning in two ways. First, we introduce a social network and assume that agents can only observe the actions of agents to whom they are connected by this network. Secondly, we allow agents to choose a different action at each date. If the network satisfies a connectedness assumption, the initial diversity resulting from diverse private information is eventually replaced by uniformity of actions, though not necessarily of beliefs, in finite time with probability one. We look at particular networks to illustrate the impact of network architecture on speed of convergence and the optimality of absorbing states. Convergence is remarkably rapid, so that asymptotic results are a good approximation even in the medium run. (C) 2003 Elsevier Inc. All rights reserved.