Modeling other minds: Bayesian inference explains human choices in group decision-making.

Area of Science:

• Cognitive Science
• Social Neuroscience
• Behavioral Economics

Background:

• Social decision-making requires predicting others' behavior, often relying on 'theory of mind'.
• Predicting behavior becomes complex in large, anonymous groups.
• Existing models struggle to capture human behavior in such scenarios.

Purpose of the Study:

• To develop and validate a predictive model for human behavior in large, anonymous group decision-making.
• To investigate the cognitive mechanisms underlying group interaction predictions.

Main Methods:

• Utilized the volunteer's dilemma, a group decision-making task.
• Developed a Bayesian model based on partially observable Markov decision processes (POMDPs).
• Compared the POMDP model's predictions against existing models and empirical human behavior.

Main Results:

• The proposed Bayesian POMDP model significantly outperformed existing models in quantitatively predicting human behavior.
• The model accurately predicted outcomes of group interactions in the volunteer's dilemma.
• Demonstrated that humans employ Bayesian inference to model group dynamics.

Conclusions:

• Humans appear to use Bayesian inference to model the 'mind of the group' in large, anonymous social contexts.
• This cognitive strategy involves predicting others' decisions and simulating the impact of one's own actions.
• The findings offer a novel computational framework for understanding complex social decision-making.