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 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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Bibliographic InfoArticle provided by Society for Computational Economics in its journal Computational Economics.
Volume (Year): 18 (2001)
Issue (Month): 1 (August)
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- Leigh Tesfatsion, 2002. "Agent-Based Computational Economics," Computational Economics 0203001, EconWPA, revised 15 Aug 2002.
- Mary A. Burke & Gary M. Fournier & Kislaya Prasad, 2006.
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