Learning to be thoughtless

Social norms and individual computation

Research output: Contribution to journalArticle

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 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.

Original languageEnglish (US)
Pages (from-to)9-24
Number of pages16
JournalComputational Economics
Volume18
Issue number1
DOIs
StatePublished - Aug 2001

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Feedback
Social norms
Agent-based model
Punctuated equilibrium
Conformity
Stylized facts

Keywords

  • Agent-based computational economics
  • Evolution of norms

ASJC Scopus subject areas

  • Management Information Systems
  • Chemical Health and Safety
  • Software
  • Safety, Risk, Reliability and Quality

Cite this

Learning to be thoughtless : Social norms and individual computation. / Epstein, Joshua.

In: Computational Economics, Vol. 18, No. 1, 08.2001, p. 9-24.

Research output: Contribution to journalArticle

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