Bayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game.

Published online

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • McDonald, KR; Broderick, WF; Huettel, SA; Pearson, JM

Published Date

  • April 18, 2019

Published In

Volume / Issue

  • 10 / 1

Start / End Page

  • 1808 -

PubMed ID

  • 31000712

Pubmed Central ID

  • 31000712

Electronic International Standard Serial Number (EISSN)

  • 2041-1723

Digital Object Identifier (DOI)

  • 10.1038/s41467-019-09789-4

Language

  • eng

Conference Location

  • England