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

This study introduces an agent-based model to examine how regret, uncertainty, and social norms interact to shape vaccination behavior during epidemics. The model integrates three behavioral mechanisms-anticipated regret, evolving norms, and uncertainty-dependent trust-within a unified learning framework. Grounded in psychology and behavioral economics, it captures how individuals make probabilistic choices influenced by material payoffs, fear, trust, and social approval. Simulations of the Susceptible-Infected-Recovered process show that choice precision has context-dependent effects. In the learning-only baseline, collective outcomes are best at intermediate precision, where agents respond sufficiently to risk while remaining flexible enough to adapt. When social norms are included, this relationship changes: normative feedback stabilizes behavior and reduces the cost of highly precise choices, while high decision noise remains harmful. Regret exerts a dual influence-moderate levels encourage adaptive self-correction, while excessive regret or greed destabilize choices. Uncertainty has a similarly non-linear effect: moderate ambiguity promotes caution, but too much uncertainty disrupts coordination. Social norms restore cooperation by compensating for incomplete information. Personal norms guide behavior when individuals have reliable information and feel confident in their judgments. Injunctive norms-signals of others' approval-become more influential under uncertainty, while descriptive norms, which arise from observing others' actions, provide informational cues that help people decide what to do when direct knowledge is limited. Overall, the model provides a psychologically grounded, computationally explicit account of how emotion, cognition, and social norms jointly govern preventive behavior during epidemics.