Bayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game
Year: 2019
Type: article
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.
Source: Nature Communications
Authors: Kelsey R. McDonald, William F. Broderick, Scott A. Huettel, John M. Pearson
Institutions: Duke University, New York University, Duke Medical Center
Language: English
Topic: Game Theory and Applications
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $5,200
OpenAlex ID: W2951755790