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Learning To Be Thoughtless: Social Norms and Individual Computation

Author

Listed:
  • Epstein, J.M.

Abstract

This paper extends the literature on the evolution of norms with an agent-based model capturing a phenomenon that has been essentially ignored, nam ely that individual thought -- or computing -- is often inversely related to the strength of a social norm. 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) and internal (intra-agent) dynamics. In addition, we generate the sylized facts regarding the spatio-temporal evolution of norms: local conformity, global diversity, and punctuated equilibria

Suggested Citation

  • Epstein, J.M., 1999. "Learning To Be Thoughtless: Social Norms and Individual Computation," Papers 6, Brookings Institution - Working Papers.
    • Handle: RePEc:fth:brooki:6
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    More about this item

    Keywords

    BEHAVIOUR ; SOCIAL NORMS;

    JEL classification:

    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • C70 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - General

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