MODELLING EXPECTATIONS WITH GENEFER- AN ARTIFICIAL INTELLIGENCE APPROACH

Economic 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.

Complex dynamics and adaptive fuzzy rule-based expectations - economic simulations with GENEFER

On last yearÌs conference in Barcelona the authors presented an innovative expectation formation hypothesis. The assumption of fully rational agents is rejected and replaced by a bounded rationality approach that is modelled by means of a fuzzy rule-base. These rules as well as their components (antecedents and consequents) dynamically adapt to a changing economic environment. The technical realization is done by the software GENEFER (Genetic Neural Fuzzy Explorer). An early version of this software was presented last year. Since then the authors have significantly extended its abilities and implemented a COM interface, so that it is applicable to any simulation in any other programming language. Our recent research concentrated on economic applications of GENEFER to demonstrate its expectations modelling and learning abilities: 1. Modelling inflation expectations within a macroeconomic business cycle model 2. Modelling agents' expectations in an artificial multiple agent foreign exchange market 3. Computing business cycle indicators with real world data The latter is on our current research agenda and we plan to obtain first results within the near future. These results will give first insights into GENEFER's forecasting abilities. All these current research projects along with a brief software documentation can be found on our website www.genefer.de. We would be happy to present the most interesting application on this yearÌs conference in Yale.Expectation formation, Genetic Algorithms, Neural Networks, Fuzzy Rule-Base

Modeling Expectations with GENEFER – an Artificial Intelligence Approach

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 2003artificial intelligence, bounded rationality, expectation formation, fuzzy systems, software,