The speed of sequential asymptotic learning

• 成果类型: Article
• 署名作者: Hann-Caruthers, Wade; Martynov, Vadim V.; Tamuz, Omer
• 署名单位: California Institute of Technology
• 刊物名称: JOURNAL OF ECONOMIC THEORY
• ISSN/ISSBN: 0022-0531
• DOI: 10.1016/j.jet.2017.11.009
• 发表日期: 2018
• 页码: 383-409
• 关键词: Social learning; Herd behavior

摘要

In the classical herding literature, agents receive a private signal regarding a binary state of nature, and sequentially choose an action, after observing the actions of their predecessors. When the informativeness of private signals is unbounded, it is known that agents converge to the correct action and correct belief. We study how quickly convergence occurs, and show that it happens more slowly than it does when agents observe signals. However, we also show that the speed of learning from actions can be arbitrarily close to the speed of learning from signals. In particular, the expected time until the agents stop taking the wrong action can be either finite or infinite, depending on the private signal distribution. In the canonical case of Gaussian private signals we calculate the speed of convergence precisely, and show explicitly that, in this case, learning from actions is significantly slower than learning from signals. (C) 2017 Elsevier Inc. All rights reserved.