Learning to Be Thoughtless: Social Norms and Individual Computation
AbstractThis 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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Bibliographic InfoPaper provided by Santa Fe Institute in its series Working Papers with number 00-03-022.
Date of creation: Mar 2000
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Agent-based computational economics; evolution of norms.;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2000-04-26 (All new papers)
- NEP-CDM-2000-04-26 (Collective Decision-Making)
- NEP-DGE-2000-04-26 (Dynamic General Equilibrium)
- NEP-EVO-2000-04-26 (Evolutionary Economics)
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