Regret, uncertainty, and bounded rationality in norm-driven decisions

Area of Science:

• Behavioral Economics
• Epidemiology
• Computational Social Science

Background:

• Understanding vaccination behavior during epidemics is crucial for public health.
• Existing models often lack integrated psychological and social factors.
• Individual decision-making is influenced by emotions, cognition, and social context.

Purpose of the Study:

• To develop and analyze an agent-based model simulating vaccination behavior.
• To investigate the interplay of regret, uncertainty, and social norms on individual choices.
• To explore how these factors affect collective outcomes during epidemics.

Main Methods:

• An agent-based model integrating anticipated regret, evolving social norms, and uncertainty-dependent trust.
• A unified learning framework capturing probabilistic choices influenced by various factors.
• Simulations of the Susceptible-Infected-Recovered (SIR) process to analyze outcomes.

Main Results:

• Choice precision's impact on collective outcomes is context-dependent, optimal at intermediate levels in a learning-only model.
• Social norms stabilize behavior, mitigating the negative effects of high precision choices and decision noise.
• Regret and uncertainty have non-linear effects; moderate levels promote adaptive behavior, while extremes are destabilizing.
• Social norms, particularly injunctive and descriptive norms, enhance cooperation and compensate for incomplete information.

Conclusions:

• Emotion, cognition, and social norms jointly regulate preventive behaviors during epidemics.
• Social norms play a critical role in promoting cooperation and adaptive decision-making under uncertainty.
• The model provides a psychologically grounded framework for understanding complex public health behaviors.