Predicting human cooperation: sensitizing drift-diffusion model to interaction and external stimuli

TL;DR

This paper develops a Bayesian drift-diffusion model to predict human cooperation in social dilemmas, accounting for interaction dynamics and external stimuli, validated with real data and applied to strategic scenarios.

Contribution

It introduces a novel Bayesian extension of the drift-diffusion model that captures how interaction types influence cooperation decisions.

Findings

Model accurately predicts cooperation rates on unseen data.

Interaction and external stimuli significantly affect cooperation dynamics.

Model useful for designing strategies to promote cooperation.

Abstract

As humans perceive and actively engage with the world, we adjust our decisions in response to shifting group dynamics and are influenced by social interactions. This study aims to identify which aspects of interaction affect cooperation-defection choices. Specifically, we investigate human cooperation within the Prisoner's Dilemma game, using the Drift-Diffusion Model to describe the decision-making process. We introduce a novel Bayesian model for the evolution of the model's parameters based on the nature of interactions experienced with other players. This approach enables us to predict the evolution of the population's expected cooperation rate. We successfully validate our model using an unseen test dataset and apply it to explore three strategic scenarios: co-player manipulation, use of rewards and punishments, and time pressure. These results support the potential of our model as a foundational tool for developing and testing strategies aimed at enhancing cooperation, ultimately contributing to societal welfare.