IDEAS home Printed from https://ideas.repec.org/a/aea/aecrev/v109y2019i12p4112-41.html
   My bibliography  Save this article

Predicting and Understanding Initial Play

Author

Listed:
  • Drew Fudenberg
  • Annie Liang

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 then observe play in a collection of new "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.

Suggested Citation

  • Drew Fudenberg & Annie Liang, 2019. "Predicting and Understanding Initial Play," American Economic Review, American Economic Association, vol. 109(12), pages 4112-4141, December.
    • Handle: RePEc:aea:aecrev:v:109:y:2019:i:12:p:4112-41
    Note: DOI: 10.1257/aer.20180654
    as

    Download full text from publisher

    File URL: https://www.aeaweb.org/doi/10.1257/aer.20180654
    Download Restriction: no
    File URL: https://www.aeaweb.org/doi/10.1257/aer.20180654.data
    Download Restriction: no
    File URL: https://www.aeaweb.org/doi/10.1257/aer.20180654.appx
    Download Restriction: no
    File URL: https://www.aeaweb.org/doi/10.1257/aer.20180654.ds
    Download Restriction: Access to full text is restricted to AEA members and institutional subscribers.
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Daniel J. Benjamin, 2018. "Errors in Probabilistic Reasoning and Judgment Biases," GRU Working Paper Series GRU_2018_023, City University of Hong Kong, Department of Economics and Finance, Global Research Unit.
    2. 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.
    3. 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.
    4. Noga Alon & Kirill Rudov & Leeat Yariv, 2021. "Dominance Solvability in Random Games," Papers 2105.10743, arXiv.org.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Külpmann, Philipp & Kuzmics, Christoph, 2022. "Comparing theories of one-shot play out of treatment," Journal of Economic Theory, Elsevier, vol. 205(C).
    11. 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.
    12. 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.
    13. 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.
    14. Fulin Guo, 2023. "Experience-weighted attraction learning in network coordination games," Papers 2310.18835, arXiv.org.
    15. Noga Alon & Kirill Rudov & Leeat Yariv, 2021. "Dominance Solvability in Random Games," Working Papers 2021-84, Princeton University. Economics Department..
    16. Drew Fudenberg & Jon Kleinberg & Annie Liang & Sendhil Mullainathan, 2019. "Measuring the Completeness of Theories," Papers 1910.07022, 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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aea:aecrev:v:109:y:2019:i:12:p:4112-41. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Michael P. Albert (email available below). General contact details of provider: https://edirc.repec.org/data/aeaaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.