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Bayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game

Author

Listed:
  • Kelsey R. McDonald

    (Duke University
    Duke University
    Duke University)

  • William F. Broderick

    (New York University)

  • Scott A. Huettel

    (Duke University
    Duke University
    Duke University)

  • John M. Pearson

    (Duke University
    Duke University
    Duke University
    Duke University Medical School)

Abstract

Previous studies of strategic social interaction in game theory have predominantly used games with clearly-defined turns and limited choices. Yet, most real-world social behaviors involve dynamic, coevolving decisions by interacting agents, which poses challenges for creating tractable models of behavior. Here, using a game in which humans competed against both real and artificial opponents, we show that it is possible to quantify the instantaneous dynamic coupling between agents. Adopting a reinforcement learning approach, we use Gaussian Processes to model the policy and value functions of participants as a function of both game state and opponent identity. We found that higher-scoring participants timed their final change in direction to moments when the opponent’s counter-strategy was weaker, while lower-scoring participants less precisely timed their final moves. This approach offers a natural set of metrics for facilitating analysis at multiple timescales and suggests new classes of experimental paradigms for assessing behavior.

Suggested Citation

  • Kelsey R. McDonald & William F. Broderick & Scott A. Huettel & John M. Pearson, 2019. "Bayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game," Nature Communications, Nature, vol. 10(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-09789-4
    DOI: 10.1038/s41467-019-09789-4
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    Cited by:

    1. Longbing Cao & Chengzhang Zhu, 2022. "Personalized next-best action recommendation with multi-party interaction learning for automated decision-making," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-22, January.
    2. Wang, Xianjia & Yang, Zhipeng & Liu, Yanli & Chen, Guici, 2023. "A reinforcement learning-based strategy updating model for the cooperative evolution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).

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