Predicting Cooperation with Learning Models

• 成果类型: Article
• 署名作者: Fudenberg, Drew; Rehbinder, Gustav arreskog
• 署名单位: Massachusetts Institute of Technology (MIT); Uppsala University
• 刊物名称: AMERICAN ECONOMIC JOURNAL-MICROECONOMICS
• ISSN/ISSBN: 1945-7669
• DOI: 10.1257/mic.20220148
• 发表日期: 2024
• 页码: 1-32
• 关键词: infinitely repeated games; PRISONERS; strategies; EVOLUTION

摘要

We use simulations of a simple learning model to predict cooperation rates in the experimental play of the indefinitely repeated prisoner's dilemma. We suppose that learning and the game parameters only influence play in the initial round of each supergame, and that after these rounds, play depends only on the outcome of the previous round. We find that our model predicts out -of -sample cooperation at least as well as models with more parameters and harder -to -interpret machine learning algorithms. Our results let us predict the effect of session length and help explain past findings on the role of strategic uncertainty. (JEL C57, C72, C73, D83, D91)