Bayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game

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.

A statistical pipeline for identifying physical features that differentiate classes of 3D shapes

“…The key objective of SINATRA is to use the topological features in X to find the physical 3D properties that best explain the variation across shape classes. To do so, we use kernel regression, where the utility of generalized nonparametric statistical models is well established due to their ability to account for various complex data structures (Cheng et al (2019), Heckerman et al (2016), McDonald et al (2019), Rodriguez-Nieva and Scheurer (2019), Swain et al (2016), Zhang, Dai and Jordan (2011)). Generally, kernel methods posit that f lives within a reproducing kernel Hilbert space (RKHS) defined by some (nonlinear) covariance function, which implicitly account for higher-order interactions between features, leading to more complete classifications of data (Jiang and Reif (2015), Pillai et al (2007), Schölkopf, Herbrich and Smola (2001)).…”

Continuous decisions

“…(a) Example: penalty kick task A recent study from our group illustrates this sort of integration of continuous control with sparse strategy changes. In the penalty kick task, monkeys and humans played a competitive video game against conspecifics that required continuous joystick input ( [71,72], figure 4). The game is based on the idea of a penalty shot in hockey.…”

“…Blue traces indicate trials played against a human bar opponent, red traces those played against a computer. Adapted from[71,72].…”

Disease Classification in Health Care Systems With Game Theory Approach

“…The work presented in [23] is based on a computational modeling framework that is efficient enough to model social dynamics that are involved in the human decision-making process. This study is an attempt to model the challenges of real-time social interactions that affect decision-making.…”