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Predicting and Understanding Initial Play

Author

Listed:
  • Drew Fudenberg

    (Department of Economics, MIT)

  • Annie Liang

    (Department of Economics, University of Pennsylvania)

Abstract
We take a machine learning approach to the problem of predicting initial play in strategic-form 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 use two approaches to uncover new regularities in play and improve the predictions of existing theories. First, we 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 find significantly 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 modified 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.

Suggested Citation

  • Drew Fudenberg & Annie Liang, 2017. "Predicting and Understanding Initial Play," PIER Working Paper Archive 17-026, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 04 Jan 2018.
    • Handle: RePEc:pen:papers:17-026
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    Citations

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    Cited by:

    1. Shoshan, Vered & Hazan, Tamir & Plonsky, Ori, 2023. "BEAST-Net: Learning novel behavioral insights using a neural network adaptation of a behavioral model," OSF Preprints kaeny, Center for Open Science.
    2. Terje Lensberg & Klaus Reiner Schenk-Hoppe, 2019. "Evolutionary Stable Solution Concepts for the Initial Play," Economics Discussion Paper Series 1916, Economics, The University of Manchester.
    3. Noga Alon & Kirill Rudov & Leeat Yariv, 2021. "Dominance Solvability in Random Games," Papers 2105.10743, arXiv.org.
    4. Lensberg, Terje & Schenk-Hoppé, Klaus Reiner, 2021. "Cold play: Learning across bimatrix games," Journal of Economic Behavior & Organization, Elsevier, vol. 185(C), pages 419-441.
    5. Jian-Qiao Zhu & Joshua C. Peterson & Benjamin Enke & Thomas L. Griffiths, 2024. "Capturing the Complexity of Human Strategic Decision-Making with Machine Learning," Papers 2408.07865, arXiv.org.
    6. Daniel J. Benjamin, 2018. "Errors in Probabilistic Reasoning and Judgment Biases," NBER Working Papers 25200, National Bureau of Economic Research, Inc.
    7. Külpmann, Philipp & Kuzmics, Christoph, 2022. "Comparing theories of one-shot play out of treatment," Journal of Economic Theory, Elsevier, vol. 205(C).
    8. Nir Chemaya & Daniel Martin, 2023. "Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals," Papers 2311.14720, arXiv.org, revised Jan 2024.
    9. Paul Feldman & John Rehbeck, 2022. "Revealing a preference for mixtures: An experimental study of risk," Quantitative Economics, Econometric Society, vol. 13(2), pages 761-786, May.
    10. Daniele Condorelli & Massimiliano Furlan, 2024. "Deep Learning to Play Games," Papers 2409.15197, arXiv.org.
    11. Noga Alon & Kirill Rudov & Leeat Yariv, 2021. "Dominance Solvability in Random Games," Working Papers 2021-84, Princeton University. Economics Department..
    12. Drew Fudenberg & Jon Kleinberg & Annie Liang & Sendhil Mullainathan, 2019. "Measuring the Completeness of Theories," Papers 1910.07022, arXiv.org.
    13. Gorny, Paul M. & Groos, Eva & Strobel, Christina, 2024. "Do Personalized AI Predictions Change Subsequent Decision-Outcomes? The Impact of Human Oversight," MPRA Paper 121065, University Library of Munich, Germany.
    14. Isaiah Andrews & Drew Fudenberg & Lihua Lei & Annie Liang & Chaofeng Wu, 2022. "The Transfer Performance of Economic Models," Papers 2202.04796, arXiv.org, revised Jul 2024.
    15. Christoph Kuzmics & Daniel Rodenburger, 2020. "A case of evolutionarily stable attainable equilibrium in the laboratory," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 70(3), pages 685-721, October.
    16. Drew Fudenberg & Wayne Gao & Annie Liang, 2020. "How Flexible is that Functional Form? Quantifying the Restrictiveness of Theories," Papers 2007.09213, arXiv.org, revised Aug 2023.
    17. Fulin Guo, 2023. "Experience-weighted attraction learning in network coordination games," Papers 2310.18835, arXiv.org.

    More about this item

    JEL classification:

    • C70 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - General
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior

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