Bayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game.
Journal Article (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
- PMC6472387
Electronic International Standard Serial Number (EISSN)
- 2041-1723
Digital Object Identifier (DOI)
- 10.1038/s41467-019-09789-4
Language
- eng
Conference Location
- England