Learning To Be Thoughtless: Social Norms and Individual Computation.

This paper extends the literature on the evolution of norms with an agent-based model capturing a phenomenon that has been essentially ignored, nam ely that individual thought -- or computing -- is often inversely related to the strength of a social norm. In this model, agents learn how to behave (what norm to adopt), but -- under a strategy I term Best Reply to Adaptive Sample Evidence -- they also learn how much to think about how to behave. How much they're thinking affects how they behave, which -- given how others behave -- affects how much they think. In short, there is feedback between the social (inter-agent) and internal (intra-agent) dynamics. In addition, we generate the sylized facts regarding the spatio-temporal evolution of norms: local conformity, global diversity, and punctuated equilibria