PROBABILISTIC MODELLING OF NORM COMPLIANCE IN COLLECTIVE BEHAVIOUR

Abstract

Community norms, the common rules keeping groups running, shape everything from school clubs to entire nations. Here, I present a fresh model showing how folks weigh the decision to obey those rules, using a structure that embraces the everyday twists and turns of social life. Unlike older models that treated everyone as the same, our framework lets us see how differences between individuals, uncertain situations, and hidden forces like peer pressure, the threat of punishment, and how fair people think a rule is push behavior in different directions. We used surveys, experiments with realistic scenarios, and simulations that treat choices as probabilities rather than certainties. The combined data allowed us to estimate when and why compliance is more likely in different contexts. By linking rational choice ideas with social norm theories, the model turns compliance into a set of conditional probabilities that evolve as conditions change. Key results show that compliance does not change smoothly; instead, certain groups cross a threshold all at once, creating sudden shifts and the spread of behavior through contagion. The results guide how policymakers should craft regulations, how public health campaigns should frame messages, and how teams should coordinate in emergencies. This research thus adds a rich, predictive toolbox to behavioral science, bridging theory and practical action in any setting where collective action is essential.