Inertia in social learning from a summary statistic

• 成果类型: Article
• 署名作者: Larson, Nathan
• 署名单位: American University
• 刊物名称: JOURNAL OF ECONOMIC THEORY
• ISSN/ISSBN: 0022-0531
• DOI: 10.1016/j.jet.2015.06.003
• 发表日期: 2015
• 页码: 596-626
• 关键词: Social learning; asymptotics; Slow learning; Echo chamber

摘要

We model normal-quadratic social learning with agents who observe a summary statistic over past actions, rather than complete action histories. Because an agent with a summary statistic cannot correct for the fact that earlier actions influenced later ones, even a small presence of old actions in the statistic can introduce very persistent errors. Depending on how fast these old actions fade from view, social learning can either be as fast as if agents' private information were pooled (rate n) or it can slow to a crawl (rate ln n). Consistent with Vives (1993), the fastest possible rate of learning falls to rate n((1/3)) if actions are also observed with noise, but may be much slower. Increasing the sample size of the summary statistic does not lead to faster asymptotic learning and may reduce short run welfare. (C) 2015 Published by Elsevier Inc.