Computational intelligence determines effective rationality

Article

Abstract

Rationality is a fundamental concept in economics. Most researchers will accept that human beings are not fully rational. Herbert Simon suggested that we are “bounded rational”. However, it is very difficult to quantify “bounded rationality”, and therefore it is difficult to pinpoint its impact to all those economic theories that depend on the assumption of full rationality. Ariel Rubinstein proposed to model bounded rationality by explicitly specifying the decision makers’ decision-making procedures. This paper takes a computational point of view to Rubinstein’s approach. From a computational point of view, decision procedures can be encoded in algorithms and heuristics. We argue that, everything else being equal, the effective rationality of an agent is determined by its computational power — we refer to this as the computational intelligence determines effective rationality (CIDER) theory. This is not an attempt to propose a unifying definition of bounded rationality. It is merely a proposal of a computational point of view of bounded rationality. This way of interpreting bounded rationality enables us to (computationally) reason about economic systems when the full rationality assumption is relaxed.

Keywords

Rationality bounded rationality computational intelligence economics computational intelligence determines effective rationality (CIDER) theory 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    E. F. Fama. Efficient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance, vol. 25, no. 2, pp. 383–417, 1970.CrossRefGoogle Scholar
  2. [2]
    F. Black. Noise. Journal of Finance, vol. 41, no. 3, pp. 529–543, 1986.CrossRefGoogle Scholar
  3. [3]
    D. Kahneman, M. W. Riepe. Aspects of Investor Psychology. Journal of Portfolio Management, vol. 24, no. 4, pp. 52–65, 1998.CrossRefGoogle Scholar
  4. [4]
    R. Shiller. Do Stock Prices Move Too Much to be Justified by Subsequent Changes in Dividents? Americal Economic Review, vol. 71, no. 3, pp. 421–436, 1981.Google Scholar
  5. [5]
    A. Shleifer. Inefficient Markets: An Introduction to Behavioral Finance, Oxford University Press, UK, 2000.Google Scholar
  6. [6]
    E. L. Lawler, J. K. Lenstra, A. H. G. Rinnooy Kan, D. B. Shmoys. The Travelling Salesman Problem: A Guided Tour of Combinatorial Optimization, John Wiley & Sons, New York, USA, 1985.Google Scholar
  7. [7]
    S. Lin, B. W. Kernighan. An Effective Heuristic Algorithm for the Traveling-Salesman Problem. Operations Research, vol. 21, no. 2, pp. 498–516, 1973.MATHMathSciNetCrossRefGoogle Scholar
  8. [8]
    C. Voudouris, E. P. K. Tsang. Guided Local Search and Its Application to the Traveling Salesman Problem. European Journal of Operational Research, vol.113, no. 2, pp. 469–499, 1999.MATHCrossRefGoogle Scholar
  9. [9]
    H. A. Simon. Models of Man, Wiley, New York, USA, 1957.MATHGoogle Scholar
  10. [10]
    G. Gigerenzer, R. Selten. Bounded Rationality: The Adaptive Toolbox, MIT Press, USA, 2001.Google Scholar
  11. [11]
    A. Rubinstein. Modeling Bounded Rationality, MIT Press, USA, 1998.Google Scholar
  12. [12]
    A. Newell, H. A. Simon. Human Problem Solving, Prentice Hall, Englewood Cliffs, NJ, USA, 1972.Google Scholar
  13. [13]
    S. Russell, P. Norvig. Artificial Intelligence: A Modern Approach, Prentice Hall, NJ, USA, 1995.MATHGoogle Scholar
  14. [14]
    F. Rossi, P. Van Beek, T. Walsh. Handbook of Constraint Programming, Elsevier, 2006.Google Scholar
  15. [15]
    E. P. K. Tsang. Foundations of Constraint Satisfaction, Academic Press, London and San Diego, 1993.Google Scholar
  16. [16]
    E. K. Burke, G. Kendall. Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, Springer, New York, USA, 2005.MATHGoogle Scholar
  17. [17]
    H. Hoos, E. P. K. Tsang. Local Search for Constraint Satisfaction. Handbook of Constraint Programming, F. Rossi, P. Van Beek, T. Walsh (eds.), Elsevier, pp. 245–277, 2006.Google Scholar
  18. [18]
    P. Larranaga, J. A. Lozano. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation, Kluwer Academic Publishers, New York, USA, 2001.Google Scholar
  19. [19]
    D. E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning, Reading, Addison-Wesley Pubulish Company, MA, USA, 1989.Google Scholar
  20. [20]
    J. H. Holland. Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, USA, 1975.Google Scholar
  21. [21]
    A. Rubinstein. Perfect Equilibrium in a Bargaining Model. Econometrica, vol. 50, no. 1, pp. 97–110, 1982.MATHCrossRefMathSciNetGoogle Scholar
  22. [22]
    A. Muthoo. Bargaining Theory with Applications, Cambridge University Press, UK, 1999.MATHGoogle Scholar
  23. [23]
    N. Jin, E. P. K. Tsang. Co-adaptive Strategies for Sequential Bargaining Problems with Discount Factors and Outside Options. In Proceedings of Congress on Evolutionary Computation, Vancouver, BC, Canada, pp. 2149–2156, 2006.Google Scholar
  24. [24]
    E. P. K. Tsang, N. Jin. An Incentive Method to Handle Constraints in Evolutionary Algorithms with a Case Study. In Proceedings of European Conference on Genetic Programming, Lecture Notes in Computer Science, Springer, Budapest, vol. 3905, pp. 133–144, 2006.Google Scholar
  25. [25]
    T. Gosling, N. Jin, E. P. K. Tsang. Games, Supply Chains and Automatic Strategy Discovery Using Evolutionary Computation. Handbook of Research on Nature-inspired Computing for Economics and Management, J. P. Rennard (ed.), Idea Group Publishing, vol 2,Chapter 38, pp. 572–588, 2006.Google Scholar
  26. [26]
    R. Selten. Boundedly Rational Qualitative Reasoning on Comparative Statics. Bounded Rationality, the Adaptive Toolbox, G. Gigerenzer, R. Selten (eds.), MIT Press, USA, pp. 1–8, 2001.Google Scholar
  27. [27]
    B. A. Kabadjova, A. Krause, E. P. K. Tsang. Competition among Payment Networks Using Generalized Population Based Incremental Learning. In Proceedings of the 12th International Conference on Computing in Economics and Finance, Limassol, Cyprus, pp. 22–24, 2006.Google Scholar
  28. [28]
    B. A. Kabadjova. Artificial Payment Card Market: An Agent Based Approach, Ph.D. dissertation, Centre for Computational Finance and Economic Agents (CCFEA), University of Esesx, UK, 2007.Google Scholar
  29. [29]
    S. Baluja. Population-based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimisation and Competitive Learning, Technical Report CS-94-163, Carnegie Mellon University, USA, 1994.Google Scholar
  30. [30]
    M. Kern. Parameter Adaptation in Heuristic Search: A Population-based Approach, Ph.D. dissertation, University of Essex, UK, 2005.Google Scholar
  31. [31]
    T. Gosling, E. P. K. Tsang. Tackling the Simple Supply Chain Model. In Proceedings of IEEE Congress on Evolutionary Computation, Vancouver, Canada, pp. 2179–2186, 2006.Google Scholar
  32. [32]
    S. Martinez-Jaramillo. Artificial Financial Markets: An Agent Based Approach to Reproduce Stylized Facts and to Study the Red Queen Effect, Ph. D. dissertation, Centre for Computational Finance and Economic Agents (CCFEA), University of Essex, UK, 2007.Google Scholar
  33. [33]
    S. Martinez-Jaramillo, E. P. K. Tsang. An Heterogeneous, Endogenous and Co-evolutionary GP-based Financial Market. IEEE Transactions on Evolutionary Computation, to be published.Google Scholar

Copyright information

© Institute of Automation, Chinese Academy of Sciences 2008

Authors and Affiliations

  1. 1.Centre for Computational Finance and Economic AgentsUniversity of EssexColchesterUK

Personalised recommendations