[Next] [Previous] [Top] [Contents]
Modelling Bounded Rationality using Evolutionary Techniques - Bruce Edmonds
An important special case of the above approach to learning is where the range of operations includes selection and some mechanism for variation, i.e. an evolutionary algorithm. In particular the paradigm of GP is particularly appropriate, due to the structure of the genome. These techniques, however, can not be blindly applied. For example, the efficiency of the learning process is only a secondary concern when seeking to model economic agents by their software cousins, but many of the other features of this approach for modelling learning in an economic agent are appropriate, namely:
- the population of programs can represent a collection of multiple, competing models of the world with which it is concerned;
- there is always at least one maximally fit individual model that can be used to react to events and from which appropriate deductions can be made;
- the models are incrementally developed by the learning mechanism;
- the fitness measure can be tailored to include aspects such as cost and complexity as well as the extent of the agreement with known data;
- the language of representation of the models can be fairly general and expressive, e.g. logical expressions.
Modelling Bounded Rationality using Evolutionary Techniques - Bruce Edmonds - 09 JUN 97
[Next] [Previous] [Top] [Contents]
Generated with CERN WebMaker