Congested observational learning

• 成果类型: Article
• 署名作者: Eyster, Erik; Galeotti, Andrea; Kartik, Navin; Rabin, Matthew
• 署名单位: University of London; London School Economics & Political Science; University of Essex; Columbia University; University of California System; University of California Berkeley
• 刊物名称: GAMES AND ECONOMIC BEHAVIOR
• ISSN/ISSBN: 0899-8256
• DOI: 10.1016/j.geb.2014.06.006
• 发表日期: 2014
• 页码: 519-538
• 关键词: Social learning; congestion; Queueing; information aggregation

摘要

We study observational learning in environments with congestion costs: an agent's payoff from choosing an action decreases as more predecessors choose that action. Herds cannot occur if congestion on every action can get so large that an agent prefers a different action regardless of his beliefs about the state. To the extent that switching away from the more popular action reveals private information, it improves learning. The absence of herding does not guarantee complete (asymptotic) learning, however, as information cascades can occur through perpetual but uninformative switching between actions. We provide conditions on congestion costs that guarantee complete learning and conditions that guarantee bounded learning. Learning can be virtually complete even if each agent has only an infinitesimal effect on congestion costs. We apply our results to markets where congestion costs arise through responsive pricing and to queuing problems where agents dislike waiting for service. (C) 2014 Elsevier Inc. All rights reserved.