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 is larger than in the US THEN the EURO will rise against the USD”. ‘Low’ and ‘larger’ 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.