Why are there descriptive norms? Because we looked for them

Authored by Ryan Muldoon, Chiara Lisciandra, Stephan Hartmann

Date Published: 2014-12

DOI: 10.1007/s11229-014-0534-y

Sponsors: No sponsors listed

Platforms: NetLogo

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

Abstract

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, which helps to motivate the assumptions made in heuristic models. In our model, the priors formalize the belief that a certain behavior is a regularity. 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 and react to them that generates descriptive norms. In our search for rules, we create them.

Tags

Agent-based modeling Social epistemology norm emergence explanation descriptive norms