Predicting and Understanding Initial Play

• 成果类型: Article
• 署名作者: Fudenberg, Drew; Liang, Annie
• 署名单位: Massachusetts Institute of Technology (MIT); University of Pennsylvania
• 刊物名称: AMERICAN ECONOMIC REVIEW
• ISSN/ISSBN: 0002-8282
• DOI: 10.1257/aer.20180654
• 发表日期: 2019
• 页码: 4112-4141
• 关键词: normal-form games; COGNITIVE HIERARCHY MODEL; bounded rationality; risk-aversion

摘要

We use machine learning to uncover regularities in the initial play of matrix games. We first train a prediction algorithm on data from past experiments. Examining the games where our algorithm predicts correctly, but existing economic models don't, leads us to add a parameter to the best performing model that improves predictive accuracy. We then observe play in a collection of new algorithmically generated games, and learn that we can obtain even better predictions with a hybrid model that uses a decision tree to decide game-by-game which of two economic models to use for prediction.