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Capturing the Complexity of Human Strategic Decision-Making with Machine Learning

Author

Listed:
  • Jian-Qiao Zhu
  • Joshua C. Peterson
  • Benjamin Enke
  • Thomas L. Griffiths
Abstract
Understanding how people behave in strategic settings–where they make decisions based on their expectations about the behavior of others–is a longstanding problem in the behavioral sciences. We conduct the largest study to date of strategic decision-making in the context of initial play in two-player matrix games, analyzing over 90,000 human decisions across more than 2,400 procedurally generated games that span a much wider space than previous datasets. We show that a deep neural network trained on these data predicts people’s choices better than leading theories of strategic behavior, indicating that there is systematic variation that is not explained by those theories. We then modify the network to produce a new, interpretable behavioural model, revealing what the original network learned about people: their ability to optimally respond and their capacity to reason about others are dependent on the complexity of individual games. This context-dependence is critical in explaining deviations from the rational Nash equilibrium, response times, and uncertainty in strategic decisions. More broadly, our results demonstrate how machine learning can be applied beyond prediction to further help generate novel explanations of complex human behavior.

Suggested Citation

  • Jian-Qiao Zhu & Joshua C. Peterson & Benjamin Enke & Thomas L. Griffiths, 2024. "Capturing the Complexity of Human Strategic Decision-Making with Machine Learning," CESifo Working Paper Series 11296, CESifo.
    • Handle: RePEc:ces:ceswps:_11296
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    File URL: https://www.cesifo.org/DocDL/cesifo1_wp11296.pdf
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    References listed on IDEAS

    as
    1. Benjamin Enke & Cassidy Shubatt, 2023. "Quantifying Lottery Choice Complexity," CESifo Working Paper Series 10644, CESifo.
    2. Ernst Fehr & Klaus M. Schmidt, 1999. "A Theory of Fairness, Competition, and Cooperation," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 114(3), pages 817-868.
    3. Harsanyi John C., 1995. "A New Theory of Equilibrium Selection for Games with Incomplete Information," Games and Economic Behavior, Elsevier, vol. 10(2), pages 318-332, August.
    4. Drew Fudenberg & Annie Liang, 2019. "Predicting and Understanding Initial Play," American Economic Review, American Economic Association, vol. 109(12), pages 4112-4141, December.
    5. McKelvey Richard D. & Palfrey Thomas R., 1995. "Quantal Response Equilibria for Normal Form Games," Games and Economic Behavior, Elsevier, vol. 10(1), pages 6-38, July.
    6. Costa-Gomes, Miguel & Crawford, Vincent P & Broseta, Bruno, 2001. "Cognition and Behavior in Normal-Form Games: An Experimental Study," Econometrica, Econometric Society, vol. 69(5), pages 1193-1235, September.
    7. McKelvey, Richard D & Palfrey, Thomas R, 1992. "An Experimental Study of the Centipede Game," Econometrica, Econometric Society, vol. 60(4), pages 803-836, July.
    8. Weizsacker, Georg, 2003. "Ignoring the rationality of others: evidence from experimental normal-form games," Games and Economic Behavior, Elsevier, vol. 44(1), pages 145-171, July.
    9. Murnighan, J Keith & Roth, Alvin E & Schoumaker, Francoise, 1988. "Risk Aversion in Bargaining: An Experimental Study," Journal of Risk and Uncertainty, Springer, vol. 1(1), pages 101-124, March.
    10. Nagel, Rosemarie, 1995. "Unraveling in Guessing Games: An Experimental Study," American Economic Review, American Economic Association, vol. 85(5), pages 1313-1326, December.
    11. Vincent P. Crawford & Miguel A. Costa-Gomes & Nagore Iriberri, 2013. "Structural Models of Nonequilibrium Strategic Thinking: Theory, Evidence, and Applications," Journal of Economic Literature, American Economic Association, vol. 51(1), pages 5-62, March.
    12. Chris L. Baker & Julian Jara-Ettinger & Rebecca Saxe & Joshua B. Tenenbaum, 2017. "Rational quantitative attribution of beliefs, desires and percepts in human mentalizing," Nature Human Behaviour, Nature, vol. 1(4), pages 1-10, April.
    13. Drew Fudenberg & Jon Kleinberg & Annie Liang & Sendhil Mullainathan, 2022. "Measuring the Completeness of Economic Models," Journal of Political Economy, University of Chicago Press, vol. 130(4), pages 956-990.
    14. Stahl, Dale II & Wilson, Paul W., 1994. "Experimental evidence on players' models of other players," Journal of Economic Behavior & Organization, Elsevier, vol. 25(3), pages 309-327, December.
    15. Robert Bell & Thomas M. Cover, 1988. "Game-Theoretic Optimal Portfolios," Management Science, INFORMS, vol. 34(6), pages 724-733, June.
    16. Benjamin Enke & Cassidy Shubatt, 2023. "Quantifying Lottery Choice Complexity," NBER Working Papers 31677, National Bureau of Economic Research, Inc.
    17. Devetag, Giovanna & Warglien, Massimo, 2008. "Playing the wrong game: An experimental analysis of relational complexity and strategic misrepresentation," Games and Economic Behavior, Elsevier, vol. 62(2), pages 364-382, March.
    18. Benjamin Enke & Thomas Graeber, 2023. "Cognitive Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 138(4), pages 2021-2067.
    19. Colin F. Camerer & Teck-Hua Ho & Juin-Kuan Chong, 2004. "A Cognitive Hierarchy Model of Games," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 119(3), pages 861-898.
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    Keywords

    behavioural game theory; large scale experiment; machine learning; behavioral economics; complexity;
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