Can Computers Have Sentiments? The Case of Risk Aversion and Utility for Wealth

  • George G. Szpiro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1674)

Abstract

Genetic algorithms display at least one characteristic that is typical of the economic behavior of human decision-makers. I show that if a choice problem involves uncertainty, genetic algorithms may produce results that are consistent with an aversion to risk.

Keywords

Genetic Algorithm Utility Function Risk Aversion Relative Risk Aversion Cash Holding 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Allen, Franklin and Risto Karjalainen [1993] “Using genetic algorithms to find technical trading rules”, Working paper 20-93, Rodney L. White Center for Financial Research, The Wharton School, University of Pennsylvania.Google Scholar
  2. Andreoni, James and John H. Miller [1995] “Auctions with artificial adaptive agents”, Games and Economic Behavior, 10, 39–64.MATHCrossRefGoogle Scholar
  3. Arifovic, Jasmina [1994] “Genetic algorithm learning and the cobweb model”, J. of Economic Dynamics and Control, 18, 3–28.MATHCrossRefGoogle Scholar
  4. Arrow, Kenneth J. [1965] “Aspects of the theory of risk bearing”, Yrjo Jahnssonin Saatio, Helsinki.Google Scholar
  5. Arthur, W. Brian [1991] “Designing economic agents that act like human agents: a behavioral approach to bounded rationality”, American Economic Review: Papers and Proceedings, 353–360.Google Scholar
  6. Goldberg, David E. [1989] “Genetic algorithms in search, optimization and machine learning”, Addison-Wesley, Reading, MA.MATHGoogle Scholar
  7. Holland, John. H. [1975] “Adaptation in natural and artificial systems”, University of Michigan Press, Ann Arbor. 2nd edition 1992, MIT Press.Google Scholar
  8. Holland, John. H., and J. H. Miller [1991] “Artificial adaptive agents in economic theory”, American Economic Review: Papers and Proceedings, 365–370.Google Scholar
  9. Kauffman, Stuart A. [1995] “Escaping the red queen effect”, McKinsey Quarterly 1995 Nr 1, 110–129.Google Scholar
  10. Koza, John R. [1992] “Genetic programming”, MIT Press, Cambridge.MATHGoogle Scholar
  11. Palmer, R. G., W. Brian Arthur, John H. Holland, Blake LeBaron, and Paul Taylor [1994] “Artificial economic life: a simple model of a stockmarket”, Physica D, 75, 264–274.MATHCrossRefGoogle Scholar
  12. Pratt, John W. [1964] “Risk aversion in the small and in the large”, Econometrica, 32, 122–136.MATHCrossRefGoogle Scholar
  13. Langton, Christopher G. [1989] “Artificial life”, Santa Fe Insitute Studies in the Science of Complexity, Christopher Langton (Ed), Addison-Wesley, Reading, MA.Google Scholar
  14. Szpiro, George G. [1986] “Measuring risk aversion: an alternative approach”, Review of Economics and Statistics, 68, 156–159.CrossRefGoogle Scholar
  15. Szpiro, George G. [1992] “Insurance buying gamblers”, Theory and Decision, 32, 203–207.MATHCrossRefGoogle Scholar
  16. Szpiro, George G. [1997a] “The emergence of risk aversion”, Complexity, 2, 1997, 31–39.CrossRefMathSciNetGoogle Scholar
  17. Szpiro, George G. [1997b] “A search for hidden relationships: data mining with genetic algorithms”, Computational Economics, 10, 267–277. 1997.MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • George G. Szpiro
    • 1
  1. 1.Israeli Center for Academic Studies (affiliated with the University of Manchester)Kiriat OnoIsrael

Personalised recommendations