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Modeling Expectations with GENEFER – an Artificial Intelligence Approach

  • Eric Ringhut
  • Stefan Kooths

Economic modeling of financial markets attempts to model highly complex systems in which expectations can be among the dominant driving forces. It is necessary, then, to focus on how agents form expectations. We believe that they look for patterns, hypothesize, try, make mistakes, learn and adapt. Agents' bounded rationality leads us to a rule-based approach which we model using Fuzzy Rule Bases. For example if a single agent believes the exchange rate is determined by a set of possible inputs and is asked to state his relationship, his answer will probably reveal a fuzzy nature like: IF the inflation rate in the EURO-Zone is low and the GDP growth rate islarger than in the US THEN the EURO will rise against the USD.Low and larger are fuzzy terms which give a graduallinguistic meaning to crisp intervalls in the respective universes of discourse. In order to learn a Fuzzy Rule base from examples we introduce Genetic Algorithms and Artificial Neural Networks as learning operators. These examples can either be empirical data or originate from an economic simulation model. The software GENEFER (GEnetic NEural Fuzzy ExploreR) has been developedfor designing such a Fuzzy Rule Base. The design process is modular and comprises Input Identification, Fuzzification, Rule Base Generating and Rule Base Tuning. The two latter steps make use of genetic and neural learning algorithms for optimizing the Fuzzy Rule Base. Copyright Kluwer Academic Publishers 2003

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Article provided by Society for Computational Economics in its journal Computational Economics.

Volume (Year): 21 (2003)
Issue (Month): 1 (February)
Pages: 173-194

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Handle: RePEc:kap:compec:v:21:y:2003:i:1:p:173-194
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  1. W. Brian Arthur & John H. Holland & Blake LeBaron & Richard Palmer & Paul Taylor, 1996. "Asset Pricing Under Endogenous Expectation in an Artificial Stock Market," Working Papers 96-12-093, Santa Fe Institute.
  2. Beltrametti, Luca & Fiorentini, Riccardo & Marengo, Luigi & Tamborini, Roberto, 1997. "A learning-to-forecast experiment on the foreign exchange market with a classifier system," Journal of Economic Dynamics and Control, Elsevier, vol. 21(8-9), pages 1543-1575, June.
  3. Daniel McFadden, 1998. "Rationality for Economists?," Working Papers 98-09-086, Santa Fe Institute.
  4. Marengo, Luigi & Tordjman, Helene, 1996. "Speculation, Heterogeneity and Learning: A Simulation Model of Exchange Rates Dynamics," Kyklos, Wiley Blackwell, vol. 49(3), pages 407-38.
  5. J. Doyne Farmer, 1999. "Physicists Attempt to Scale the Ivory Towers of Finance," Working Papers 99-10-073, Santa Fe Institute.
  6. Arthur, W Brian, 1994. "Inductive Reasoning and Bounded Rationality," American Economic Review, American Economic Association, vol. 84(2), pages 406-11, May.
  7. 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.
  8. Richard B. Olsen & Michel M. Dacorogna & Ulrich A. Muller, & Olivier V. Pictet, . "Going Back to the Basics - Rethinking Market Efficiency," Working Papers 1992-09-07., Olsen and Associates.
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