In this work, we present a mathematical model for the emergence of descriptive norms, where the individual decision problem is formalized with the standard Bayesian belief revision machinery. Previous work on the emergence of descriptive norms has relied on heuristic modeling. In this paper we show that with a Bayesian model we can provide a more general picture of the emergence of norms that helps to motivate the assumptions made in heuristic models.In our model, the priors formalize the belief that the behavioral rule is a descriptive norm, the evidence is provided by other group members’ behavior and the likelihood by their reliability. We implement the model in a series of computer simulations and examine the group-level outcomes. We claim that domain-general belief revision helps explain why we look for regularities in social life in the first place. We argue that it is the disposition to look for regularities that generates descriptive norms. In our search for rules, we create them.
This paper is joint work with Ryan Muldoon (U/Penn) and Stephan Hartmann (LMU).