IDEAS home Printed from https://ideas.repec.org/p/ags/idpmgd/30569.html
   My bibliography  Save this paper

Reworking the Standard Model of Competitive Markets: The Role of Fuzzy Logic and Genetic Algorithms in Modelling Complex Non-Linear Economic System

Author

Listed:
  • Smith, Peter

Abstract

Some aspects of economic systems (eg, nonlinearity, qualitative variables) are intractable when incorporated into models. The widespread practice of excluding them (or greatly limiting their role) produces deviations of unknown size and form between the resulting models and the reality they purport to represent. To explore this issue, and the extent to which a change in methodology can improve tractability, a combination of two techniques, fuzzy logic and genetic algorithms, was applied to the problem of how the sellers in a freely competitive market, if initially trading at different prices, can find their way to supply/demand equilibrium. A multi-agent model was used to simulate the evolution of autonomously- learnt rule-governed behaviour, (i), under perfect competition, and (ii), in a more commercially realistic environment. During the learning process, markets may lack a true equilibrium price, and therefore sellers in such a model cannot be price-takers in the conventional sense; instead, it was stipulated that they would set an asking price, buyers would shop around for cheap supply, and the sellers would revise their pricing policy according to its profitability. Each firm's pricing policy was embedded in a fuzzy ruleset; the rulesets were improved over time by successive passes of the genetic algorithm, using profit level as a measure of Darwinian fitness. The simulated evolution was repeated over a random sample of 10 markets. Under perfect competition, sellers' asking prices converged onto the theoretical equilibrium price. This performance was maintained when either uncertainty in demand or a more commercially realistic set of dynamics was introduced. However, when both these features were introduced simultaneously, different, substantially lower equilibrium prices were reached. In both cases, autonomous learning by the sellers suppressed the instability that might have been expected to result from the introduction of a number of nonlinearities. Other possible applications of the methodology are discussed, along with some of its implications.

Suggested Citation

  • Smith, Peter, 2004. "Reworking the Standard Model of Competitive Markets: The Role of Fuzzy Logic and Genetic Algorithms in Modelling Complex Non-Linear Economic System," General Discussion Papers 30569, University of Manchester, Institute for Development Policy and Management (IDPM).
  • Handle: RePEc:ags:idpmgd:30569
    as

    Download full text from publisher

    File URL: http://purl.umn.edu/30569
    Download Restriction: no

    References listed on IDEAS

    as
    1. Bullard, James & Duffy, John, 1998. "A model of learning and emulation with artificial adaptive agents," Journal of Economic Dynamics and Control, Elsevier, vol. 22(2), pages 179-207, February.
    2. Chiarella, Carl & He, Xue-Zhong, 2003. "Dynamics of beliefs and learning under aL-processes -- the heterogeneous case," Journal of Economic Dynamics and Control, Elsevier, vol. 27(3), pages 503-531, January.
    3. Dechert, W.D. & Hommes, C.H., 1999. "Complex Nonlinear Dynamics and Computational Methods," CeNDEF Working Papers 99-01, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    4. R. G. Lipsey & Kelvin Lancaster, 1956. "The General Theory of Second Best," Review of Economic Studies, Oxford University Press, vol. 24(1), pages 11-32.
    5. LeBaron, Blake, 2000. "Agent-based computational finance: Suggested readings and early research," Journal of Economic Dynamics and Control, Elsevier, vol. 24(5-7), pages 679-702, June.
    6. Brian J. Loasby, 2000. "Market institutions and economic evolution," Journal of Evolutionary Economics, Springer, vol. 10(3), pages 297-309.
    7. Dawid, Herbert, 1999. "On the convergence of genetic learning in a double auction market," Journal of Economic Dynamics and Control, Elsevier, vol. 23(9-10), pages 1545-1567, September.
    8. Lettau, Martin, 1997. "Explaining the facts with adaptive agents: The case of mutual fund flows," Journal of Economic Dynamics and Control, Elsevier, vol. 21(7), pages 1117-1147, June.
    9. Arifovic, Jasmina & Gencay, Ramazan, 2000. "Statistical properties of genetic learning in a model of exchange rate," Journal of Economic Dynamics and Control, Elsevier, vol. 24(5-7), pages 981-1005, June.
    10. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
    11. Marco Valente & Andrea Bassanini & Luigi Marengo & Giovanni Dosi, 1999. "Norms as emergent properties of adaptive learning: The case of economic routines," Journal of Evolutionary Economics, Springer, vol. 9(1), pages 5-26.
    12. Tay, Nicholas S. P. & Linn, Scott C., 2001. "Fuzzy inductive reasoning, expectation formation and the behavior of security prices," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 321-361, March.
    13. Vriend, Nicolaas J., 2000. "An illustration of the essential difference between individual and social learning, and its consequences for computational analyses," Journal of Economic Dynamics and Control, Elsevier, vol. 24(1), pages 1-19, January.
    14. Newbery, David M G & Stiglitz, Joseph E, 1982. "The Choice of Techniques and the Optimality of Market Equilibrium with Rational Expectations," Journal of Political Economy, University of Chicago Press, vol. 90(2), pages 223-246, April.
    15. Arthur, W.B. & Holland, J.H. & LeBaron, B. & Palmer, R. & Tayler, P., 1996. "Asset Pricing Under Endogenous Expectations in an Artificial Stock Market," Working papers 9625, Wisconsin Madison - Social Systems.
    16. Tesfatsion, Leigh, 2001. "Introduction to the special issue on agent-based computational economics," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 281-293, March.
    17. Michael Kopel & Herbert Dawid, 1998. "On economic applications of the genetic algorithm: a model of the cobweb type," Journal of Evolutionary Economics, Springer, vol. 8(3), pages 297-315.
    18. Werner G, th & Bezalel Peleg, 2001. "When will payoff maximization survive? An indirect evolutionary analysis," Journal of Evolutionary Economics, Springer, vol. 11(5), pages 479-499.
    19. Smith, Peter C. & van Ackere, Ann, 2002. "A note on the integration of system dynamics and economic models," Journal of Economic Dynamics and Control, Elsevier, vol. 26(1), pages 1-10, January.
    20. Siegfried Berninghaus & Werner Güth & Hartmut Kliemt, 2003. "From teleology to evolution," Journal of Evolutionary Economics, Springer, vol. 13(4), pages 385-410, October.
    21. Negroni, Giorgio, 2003. "Adaptive expectations coordination in an economy with heterogeneous agents," Journal of Economic Dynamics and Control, Elsevier, vol. 28(1), pages 117-140, October.
    Full references (including those not matched with items on IDEAS)

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:idpmgd:30569. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (AgEcon Search). General contact details of provider: http://edirc.repec.org/data/idmanuk.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.