Predicting and Understanding Initial Play
AbstractWe 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.
CitationFudenberg, Drew, and Annie Liang. 2019. "Predicting and Understanding Initial Play." American Economic Review, 109 (12): 4112-41. DOI: 10.1257/aer.20180654
- C70 Game Theory and Bargaining Theory: General
- C91 Design of Experiments: Laboratory, Individual