Classification Through Thick and Thin: Permissive Norms and Strict Rules

Gillian Hadfield (University of Southern California)
Jens Prufer (Tilburg University)
Vatsalya Srivastava (Tilburg University)

Abstract: Classification institutions - such as social norms, cultural or religious traditions, laws, or regulations - assign a normative label, acceptable or wrongful, to actions. This paper investigates how classifications determine and are determined by the degree of expected compliance and the available tools of enforcement. We construct a game theoretic model with N players of different types to illustrate and compare classifications emerging from social norms with those from a formal legal system. We illustrate how a given classification can lead to compliance and then check for the degree of compliance that it can achieve given the enforcement methods. We show that a trade-off inherent in any attempt at classification is that a strict classification might yield better outcomes when people comply but will make enforcement more difficult. The results illustrate how for a given degree of expected compliance, classifications used by social norms (don’t eat meat) with decentralized enforcement cannot be as strict as the one used by a police action based court system (don’t eat peacock meat). This implies that efficient design of classifications can be used as a policy lever to adjust expectations of compliance with given enforcement capability or to improve enforcement when expected compliance is fixed.