Predicting how people play games: Reinforcement learning in experimental games with unique, mixed strategy equilibria

• 成果类型: Review
• 署名作者: Erev, I; Roth, AE
• 署名单位: Technion Israel Institute of Technology; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Harvard University; Harvard University
• 刊物名称: AMERICAN ECONOMIC REVIEW
• ISSN/ISSBN: 0002-8282
• 发表日期: 1998
• 页码: 848-881
• 关键词: STRICTLY COMPETITIVE GAMES; MINIMAX HYPOTHESIS; natural experiment; signaling games; FORM GAMES; MARKET; DECISION; BEHAVIOR; ORGANIZATION; heuristics

摘要

We examine learning in all experiments we could locate involving 100 periods or more of games with a unique equilibrium in mixed strategies, and in a new experiment. We study both the ex post (best fit) descriptive power of learning models, and their ex ante predictive power, by simulating each experiment using parameters estimated from the other experiments. Even a one-parameter reinforcement learning model robustly outperforms the equilibrium predictions. Predictive power is improved by adding forgetting and experimentation, or by allowing greater rationality as in probabilistic fictitious play. Implications for developing a low-rationality, cognitive game theory are discussed.