Learning to Be Thoughtless: Social Norms and Individual Computation

Joshua M. Epstein

Working Papers from Santa Fe Institute

Abstract: 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.

Keywords: Agent-based computational economics; evolution of norms. (search for similar items in EconPapers)
Date: 2000-03
New Economics Papers: this item is included in nep-cdm, nep-dge and nep-evo
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:wop:safiwp:00-03-022

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