Predicting and Understanding Initial Play
39 Pages Posted: 28 Nov 2017 Last revised: 16 Jun 2019
Date Written: March 13, 2019
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 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. Finally, we explore the usefulness of crowd-sourced predictions for making better predictions, and for discovering additional relevant game features.
Keywords: prediction, machine learning, behavioral game theory, cognitive hierarchy
JEL Classification: C72, D03, C51, C91
Suggested Citation: Suggested Citation