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, namely that individual thought--or computing--is often inversely related to the strength of a social norm. Once a norm is entrenched, we confirm thoughtlessly. 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) anbd internal (intra-agent) dynamics. In addition, we generalte the stylized facts regarding the spatio-temporal evolution of norms: local conformity, global diversity, and punctuated equilibria.
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"Crime and Social Interactions,"
The Quarterly Journal of Economics,
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