Computational Economics

, Volume 18, Issue 1, pp 9–24

Learning to Be Thoughtless: Social Norms and Individual Computation

  • Joshua M. Epstein
Article

Abstract

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 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Axelrod, R. (1984). The Evolution of Cooperation. Basic Books, New York.Google Scholar
  2. Axelrod, R. (1996). An evolutionary approach to norms. American Political Science Review, 80(4), 1095–1111.Google Scholar
  3. Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press, Princeton NJ.Google Scholar
  4. Bicchieri, C., Jeffrey, R. and Skyrms, B. (eds.) (1997). The Dynamics of Norms. Cambridge University Press, New York.Google Scholar
  5. Canning, D. (1994). Learning and social equilibrium in large populations. In A. Kirman and M. Salmon (eds.), Learning and Rationality in Economics. Blackwell, Oxford.Google Scholar
  6. Feldman, D.P. and Crutchfield, J.P. (1997). Measures of statistical complexity: why? Santa Fe Institute Working Paper 97-07-064.Google Scholar
  7. Feldman, R.M. and Valdez-Flores, C. (1996). Applied Probability and Stochastic Processes. PWS Publishing Company, Boston.Google Scholar
  8. Freund, J.E. (1992). Mathematical Statistics, 5th Edition. Prentice Hall, Upper Saddle River, NJ.Google Scholar
  9. Glaeser, E.L., Sacerdote, B. and Scheinkman, J. (1996). Crime and social interactions. Quarterly Journal of Economics, 111, 507–548.Google Scholar
  10. Judd, K.L. (1998). Numerical Methods in Economics. The MIT Press, Cambridge, MA.Google Scholar
  11. Kandori, M., Mailath, G. and Rob, R. (1993). Learning, mutation, and long-run equilibria in games. Econometrica, 61, 29–56.Google Scholar
  12. Kirman, A. and Salmon, M. (eds.) (1994). Learning and Rationality in Economics. Blackwell, Oxford.Google Scholar
  13. Law, A.M. and Kelton, W.D. (1991). Simulation Modeling and Analysis. McGraw-Hill, Inc., New York.Google Scholar
  14. Lewis, D. (1969). Convention: A Philosophical Study. Harvard University Press, Cambridge, MA.Google Scholar
  15. Ullman-Margalit, E. (1977). The Emergence of Norms. Oxford University Press, Oxford.Google Scholar
  16. Vega-Redondo, F. (1996). Evolution, Games, and Economic Behaviour. Oxford University Press, New York.Google Scholar
  17. Young, H.P. (1993a). The evolution of conventions. Econometrica, 61, 57–84.Google Scholar
  18. Young, H.P. (1993b). An evolutionary model of bargaining. Journal of Economic Theory, 59, 145–68.Google Scholar
  19. Young, H.P. (1995). The economics of convention. Journal of Economic Perspectives, 10, 105–22.Google Scholar
  20. Young, H.P. (1998). Individual Strategy and Social Structure. Princeton University Press, Princeton, NJ.Google Scholar

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.

Personalised recommendations