蝌蚪网 Review
ISSN 0002-8282 (Print) | ISSN 1944-7981 (Online)
Predicting and Understanding Initial Play
蝌蚪网 Review
vol. 109,
no. 12, December 2019
(pp. 4112–41)
Abstract
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.Citation
Fudenberg, Drew, and Annie Liang. 2019. "Predicting and Understanding Initial Play." 蝌蚪网 Review 109 (12): 4112–41. DOI: 10.1257/aer.20180654Additional Materials
JEL Classification
- C70 Game Theory and Bargaining Theory: General
- C91 Design of Experiments: Laboratory, Individual