A learning-based model of repeated games with incomplete information

• 成果类型: Article
• 署名作者: Chong, Juin-Kuan; Camerer, Colin F.; Ho, Teck H.
• 署名单位: California Institute of Technology; Sungkyunkwan University (SKKU); National University of Singapore; 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.2005.03.009
• 发表日期: 2006
• 页码: 340-371
• 关键词: Repeated games; self-tuning experience-weighted attraction learning; Quantal response equilibrium

摘要

This paper tests a learning-based model of strategic teaching in repeated games with incomplete information. The repeated game has a long-run player whose type is unknown to a group of shortrun players. The proposed model assumes a fraction of 'short-run' players follow a one-parameter learning model (self-tuning EWA). In addition, some 'long-run' players are myopic while others are sophisticated and rationally anticipate how short-run players adjust their actions over time and teach the short-run players to maximize their long-run payoffs. All players optimize noisily. The proposed model nests an agent-based quantal-response equilibrium (AQRE) and the standard equilibrium models as special cases. Using data from 28 experimental sessions of trust and entry repeated games, including 8 previously unpublished sessions, the model fits substantially better than chance and much better than standard equilibrium models. Estimates show that most of the long-run players are sophisticated, and short-run players become more sophisticated with experience. (c) 2005 Elsevier Inc. All rights reserved.