Modeling Expectations with GENEFER – an Artificial Intelligence Approach
AbstractEconomic 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
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Society for Computational Economics in its journal Computational Economics.
Volume (Year): 21 (2003)
Issue (Month): 1 (February)
artificial intelligence; bounded rationality; expectation formation; fuzzy systems; software;
Other versions of this item:
- Eric Ringhut & Stefan Kooths, 2003. "Modeling Expectations with GENEFER -- an Artificial Intelligence Approach," Computational Economics, Society for Computational Economics, vol. 21(1_2), pages 173-194, 02.
- Stefan Kooths & Eric Ringhut, 2000. "Modelling Expectations With Genefer- An Artificial Intelligence Approach," Computing in Economics and Finance 2000 80, Society for Computational Economics.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- repec:att:wimass:9625 is not listed on IDEAS
- Arthur, W Brian, 1994. "Inductive Reasoning and Bounded Rationality," American Economic Review, American Economic Association, vol. 84(2), pages 406-11, May.
- Daniel McFadden, 1998.
"Rationality for Economists?,"
98-09-086, Santa Fe Institute.
- 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.
- 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.
- 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.
- 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.
- J. Doyne Farmer, 1999. "Physicists Attempt to Scale the Ivory Towers of Finance," Working Papers 99-10-073, Santa Fe Institute.
- Shu-Heng Chen & Chung-Ching Tai, 2006. "On the Selection of Adaptive Algorithms in ABM: A Computational-Equivalence Approach," Computational Economics, Society for Computational Economics, vol. 28(1), pages 51-69, August.
- Floortje Alkemade & Han Poutré & Hans Amman, 2006. "Robust Evolutionary Algorithm Design for Socio-economic Simulation," Computational Economics, Society for Computational Economics, vol. 28(4), pages 355-370, November.
- Shu-Heng Chen & Chung-Ching Tai, 2006. "Republication: On the Selection of Adaptive Algorithms in ABM: A Computational-Equivalence Approach," Computational Economics, Society for Computational Economics, vol. 28(4), pages 313-331, November.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Guenther Eichhorn) or (Christopher F. Baum).
If references are entirely missing, you can add them using this form.