Predicting and Understanding Initial Play

39 Pages Posted: 28 Nov 2017 Last revised: 16 Jun 2019

See all articles by Drew Fudenberg

Drew Fudenberg

Massachusetts Institute of Technology (MIT)

Annie Liang

Northwestern University

Date Written: March 13, 2019

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 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

Fudenberg, Drew and Liang, Annie, Predicting and Understanding Initial Play (March 13, 2019). PIER Working Paper No. 18-009, Available at SSRN: https://ssrn.com/abstract=3076682 or http://dx.doi.org/10.2139/ssrn.3076682

Drew Fudenberg

Massachusetts Institute of Technology (MIT) ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

Annie Liang (Contact Author)

Northwestern University ( email )

2001 Sheridan Road
Evanston, IL 60208
United States

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
379
Abstract Views
1,822
Rank
143,584
PlumX Metrics