Observational learning with position uncertainty

• 成果类型: Article
• 署名作者: Monzon, Ignacio; Rapp, Michael
• 署名单位: Collegio Carlo Alberto; Australian National University
• 刊物名称: JOURNAL OF ECONOMIC THEORY
• ISSN/ISSBN: 0022-0531
• DOI: 10.1016/j.jet.2014.09.012
• 发表日期: 2014
• 页码: 375-402
• 关键词: Social learning; Complete learning; information aggregation; Herds; position uncertainty; Observational learning

摘要

Observational learning is typically examined when agents have precise information about their position in the sequence of play. We present a model in which agents are uncertain about their positions. Agents sample the decisions of past individuals and receive a private signal about the state of the world. We show that social learning is robust to position uncertainty. Under any sampling rule satisfying a stationarity assumption, learning is complete if signal strength is unbounded. In cases with bounded signal strength, we provide a lower bound on information aggregation: individuals do at least as well as an agent with the strongest signal realizations would do in isolation. Finally, we show in a simple environment that position uncertainty slows down learning but not to a great extent. (c) 2014 Elsevier Inc. All rights reserved.