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Learning to Be Thoughtless: Social Norms and Individual Computation

  • Epstein, Joshua M
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    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 conform 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 are 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 stylized facts regarding the spatio-temporal evolution of norms: local conformity, global diversity, and punctuated equilibria. Copyright 2001 by Kluwer Academic Publishers

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    File URL: http://journals.kluweronline.com/issn/0927-7099/contents
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    Article provided by Society for Computational Economics in its journal Computational Economics.

    Volume (Year): 18 (2001)
    Issue (Month): 1 (August)
    Pages: 9-24

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    Handle: RePEc:kap:compec:v:18:y:2001:i:1:p:9-24
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