Modelling Expectations With Genefer- An Artificial Intelligence Approach
AbstractEconomic modelling of financial markets means to model highly complex systems in which expectations can be the dominant driving forces. Therefore it is necessary to focus on how agents form their expectations. We believe that they look for patterns, hypothesize, try, make mistakes, learn and adapt. AgentsÆ bounded rationality leads us to a rule-based approach which we model using Fuzzy Rule-Bases. E. g. if a single agent believes the exchange rate is determined by a set of possible inputs and is asked to put their relationship in words his answer will probably reveal a fuzzy nature like: "IF the inflation rate in the EURO-Zone is low and the GDP growth rate is larger than in the US THEN the EURO will rise against the USD". æLowÆ and ælargerÆ are fuzzy terms which give a gradual linguistic meaning to crisp intervalls in the respective universes of discourse. In order to learn a Fuzzy 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 developed for 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.
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2000 with number 80.
Date of creation: 05 Jul 2000
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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.
- Eric Ringhut & Stefan Kooths, 2003. "Modeling Expectations with GENEFER – an Artificial Intelligence Approach," Computational Economics, Society for Computational Economics, vol. 21(1), pages 173-194, February.
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