Computational Economics

, Volume 18, Issue 1, pp 9–24 | Cite as

Learning to Be Thoughtless: Social Norms and Individual Computation

  • Joshua M. Epstein


This paper extends the literature on the evolution of norms with anagent-based modelcapturing a phenomenon that has been essentially ignored, namely thatindividual thought – orcomputing – is often inversely related to the strength of a social norm.Once a norm isentrenched, we conform thoughtlessly. In this model, agents learn how tobehave (what normto adopt), but – under a strategy I term Best Reply to Adaptive SampleEvidence – they also learnhow much to think about how to behave. How much they are thinking affects howthey behave,which – given how others behave – affects how much they think. Inshort, there is feedbackbetween the social (inter-agent) and internal (intra-agent) dynamics. Inaddition, we generate thestylized facts regarding the spatio-temporal evolution of norms: localconformity, global diversity,and punctuated equilibria.

agent-based computational economics evolution of norms 


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Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • Joshua M. Epstein
    • 1
    • 2
  1. 1.Economic Studies ProgramThe Brookings InstitutionWashingtonU.S.A.
  2. 2.The External FacultySanta Fe InstituteU.S.A.

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