Social human collective decision-making and its applications with brain network models

TL;DR

This paper explores probabilistic models and brain network frameworks to understand social human collective decision-making, providing models, examples, and discussing recent advances, challenges, and future directions.

Contribution

It introduces detailed drift-diffusion and Bayesian models for social decision-making, reviews recent developments with brain network applications, and discusses neuromodulation and reinforcement learning roles.

Findings

Numerical examples illustrate decision-making processes.

Neuromodulation influences collective decisions.

Open problems in social dynamics are identified.

Abstract

In this chapter, we consider probabilistic drift-diffusion models and Bayesian inference frameworks to address this issue, assisting better social human decision-making. We provide details of the models, as well as representative numerical examples, and discuss the decision-making process with a representative example of the escape route decision-making phenomena by further developing the drift-diffusion models and Bayesian inference frameworks. In the latter context, we also give a review of recent developments in human collective decision-making and its applications with brain network models. Furthermore, we provide illustrative numerical examples to discuss the role of neuromodulation, reinforcement learning in decision-making processes. Finally, we call attention to existing challenges, open problems, and promising approaches in studying social dynamics and collective human decision-making, including those arising from nonequilibrium considerations of the associated processes.