On the Efficiency of Social Learning

• 成果类型: Article
• 署名作者: Rosenberg, Dinah; Vieille, Nicolas
• 署名单位: Hautes Etudes Commerciales (HEC) Paris
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA15845
• 发表日期: 2019
• 页码: 2141-2168
• 关键词: NETWORKS

摘要

We revisit prominent learning models in which a sequence of agents make a binary decision on the basis of both a private signal and information related to past choices. We analyze the efficiency of learning in these models, measured in terms of the expected welfare. We show that, irrespective of the distribution of private signals, learning efficiency is the same whether each agent observes the entire sequence of earlier decisions or only the previous decision. In addition, we provide a simple condition on the signal distributions that is necessary and sufficient for learning efficiency. This condition fails to hold in many cases of interest. We discuss a number of extensions and variants.