STRATEGIC LEARNING AND THE TOPOLOGY OF SOCIAL NETWORKS

• 成果类型: Article
• 署名作者: Mossel, Elchanan; Sly, Allan; Tamuz, Omer
• 署名单位: University of Pennsylvania; University of California System; University of California Berkeley; California Institute of Technology
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA12058
• 发表日期: 2015
• 页码: 1755-1794
• 关键词: communication; consensus; KNOWLEDGE

摘要

We consider a group of strategic agents who must each repeatedly take one of two possible actions. They learn which of the two actions is preferable from initial private signals and by observing the actions of their neighbors in a social network. We show that the question of whether or not the agents learn efficiently depends on the topology of the social network. In particular, we identify a geometric egalitarianism condition on the social network that guarantees learning in infinite networks, or learning with high probability in large finite networks, in any equilibrium. We also give examples of nonegalitarian networks with equilibria in which learning fails.