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Human Strategic Decision Making in Parametrized Games

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  • Sam Ganzfried

    (Ganzfried Research, Miami Beach, FL 33139, USA)

Abstract

Many real-world games contain parameters which can affect payoffs, action spaces, and information states. For fixed values of the parameters, the game can be solved using standard algorithms. However, in many settings agents must act without knowing the values of the parameters that will be encountered in advance. Often the decisions must be made by a human under time and resource constraints, and it is unrealistic to assume that a human can solve the game in real time. We present a new framework that enables human decision makers to make fast decisions without the aid of real-time solvers. We demonstrate applicability to a variety of situations including settings with multiple players and imperfect information.

Suggested Citation

  • Sam Ganzfried, 2022. "Human Strategic Decision Making in Parametrized Games," Mathematics, MDPI, vol. 10(7), pages 1-23, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:7:p:1147-:d:785979
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    References listed on IDEAS

    as
    1. Sam Ganzfried & Max Chiswick, 2019. "Most Important Fundamental Rule of Poker Strategy," Papers 1906.09895, arXiv.org, revised Feb 2020.
    2. Page, Frank, 2015. "Parameterized games, minimal Nash correspondences, and connectedness," LSE Research Online Documents on Economics 65102, London School of Economics and Political Science, LSE Library.
    3. János Flesch & Arkadi Predtetchinski, 2020. "Parameterized games of perfect information," Annals of Operations Research, Springer, vol. 287(2), pages 683-699, April.
    4. Sam Ganzfried & Farzana Yusuf, 2017. "Computing Human-Understandable Strategies: Deducing Fundamental Rules of Poker Strategy," Games, MDPI, vol. 8(4), pages 1-13, November.
    Full references (including those not matched with items on IDEAS)

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