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Learning to Be Thoughtless: Social Norms and Individual Computation


  • Joshua M. Epstein


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.

Suggested Citation

  • Joshua M. Epstein, 2000. "Learning to Be Thoughtless: Social Norms and Individual Computation," Working Papers 00-03-022, Santa Fe Institute.
  • Handle: RePEc:wop:safiwp:00-03-022

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    References listed on IDEAS

    1. Edward L. Glaeser & Bruce Sacerdote & José A. Scheinkman, 1996. "Crime and Social Interactions," The Quarterly Journal of Economics, Oxford University Press, vol. 111(2), pages 507-548.
    2. repec:mes:challe:v:34:y:1991:i:6:p:58-60 is not listed on IDEAS
    3. Young, H Peyton, 1993. "The Evolution of Conventions," Econometrica, Econometric Society, vol. 61(1), pages 57-84, January.
    4. Canning, D., 1990. "Social Equilibrium," Papers 150, Cambridge - Risk, Information & Quantity Signals.
    5. David P. Feldman & James P. Crutchfield, 1997. "Measures of Statistical Complexity: Why?," Working Papers 97-07-064, Santa Fe Institute.
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    Agent-based computational economics; evolution of norms.;

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