Reworking the Standard Model of Competitive Markets: The Role of Fuzzy Logic and Genetic Algorithms in Modelling Complex Non-Linear Economic System
AbstractSome 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.
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Bibliographic InfoPaper provided by University of Manchester, Institute for Development Policy and Management (IDPM) in its series General Discussion Papers with number 30569.
Date of creation: 2004
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competition; markets; Walrasian Crier; equilibrium; fuzzy logic; genetic algorithms; evolutionary algorithms; Industrial Organization;
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- W. Brian Arthur & John H. Holland & Blake LeBaron & Richard Palmer & Paul Taylor, 1996.
"Asset Pricing Under Endogenous Expectation in an Artificial Stock Market,"
96-12-093, Santa Fe Institute.
- 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.
- Tesfatsion, Leigh S., 2001.
"Introduction to the Special Issue on Agent-Based Computational Economics,"
Staff General Research Papers
1915, Iowa State University, Department of Economics.
- 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.
- Tesfatsion, Leigh S., 2001. "Introduction to the Special Issue on Agent-Based Computational Economics," Staff General Research Papers 10038, Iowa State University, Department of Economics.
- 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.
- 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.
- 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.
- James Bullard & John Duffy, 1994.
"A model of learning and emulation with artificial adaptive agents,"
1994-014, Federal Reserve Bank of St. Louis.
- 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.
- 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.
- 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.
- Brian J. Loasby, 2000. "Market institutions and economic evolution," Journal of Evolutionary Economics, Springer, vol. 10(3), pages 297-309.
- Carl Chiarella & Xue-Zhong He, 2001.
"Dynamics of Beliefs and Learning Under aL Processes - The Heterogeneous Case,"
Research Paper Series
55, Quantitative Finance Research Centre, University of Technology, Sydney.
- 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.
- 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.
- Siegfried Berninghaus & Werner Güth & Hartmut Kliemt, 2003. "From teleology to evolution," Journal of Evolutionary Economics, Springer, vol. 13(4), pages 385-410, October.
- 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.
- 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.
- 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 223-46, April.
- 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.
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