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.Wethenobserveplayinacollectionofnew“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.
|Original language||English (US)|
|Number of pages||30|
|Journal||American Economic Review|
|State||Published - Dec 2019|
ASJC Scopus subject areas
- Economics and Econometrics