Predicting and Understanding Initial Play

Drew Fudenberg and Annie Liang ()
Additional contact information
Annie Liang: Department of Economics, University of Pennsylvania

PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania

Abstract: We take a machine learning approach to the problem of predicting initial play in strategicform games, with the goal of uncovering new regularities in play and improving the predictions of existing theories. The analysis is implemented on data from previous laboratory experiments, and also a new data set of 200 games played on Mechanical Turk. We ï¬ rst use machine learning algorithms to train prediction rules based on a large set of game features. Examination of the games where our algorithm predicts play correctly, but the existing models do not, leads us to introduce a risk aversion parameter that we ï¬ nd signiï¬ cantly improves predictive accuracy. Second, we augment existing empirical models by using play in a set of training games to predict how the models’ parameters vary across new games. This modiï¬ ed approach generates better out-of-sample predictions, and provides insight into how and why the parameters vary. These methodologies are not special to the problem of predicting play in games, and may be useful in other contexts.

Pages: 57 pages
Date: 2017-11-14, Revised 2018-04-30
New Economics Papers: this item is included in nep-big, nep-cmp and nep-exp
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://economics.sas.upenn.edu/system/files/worki ... per%20Submission.pdf (application/pdf)

Related works:
Journal Article: Predicting and Understanding Initial Play (2019)
Working Paper: Predicting and Understanding Initial Play (2018)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pen:papers:18-009

Access Statistics for this paper

More papers in PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania 133 South 36th Street, Philadelphia, PA 19104. Contact information at EDIRC.
Bibliographic data for series maintained by Administrator ().