Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/78334
Authors: 
Riechmann, Thomas
Year of Publication: 
1998
Series/Report no.: 
Diskussionspapiere der Wirtschaftswissenschaftlichen Fakultät, Universität Hannover 219
Abstract: 
This paper tries to connect the theory of genetic algorithm (GA) learning to evolutionary game theory. It is shown that economic learning via genetic algorithms can be described as a specific form of evolutionary game. It will be pointed out that GA learning results in a series of near Nash equilibria which during the learning process build up to finally reach a neighborhood of an evolutionarily stable state. In order to clarify this point, a concept of evolutionary stability of genetic populations will be developed. Thus, in a second part of the paper it becomes possible to explain both, the reasons for the specific dynamics of standard GA learning models and the different kind of dynamics of GA learning models, which use extensions to the standard GA.
Subjects: 
learning
computational economics
genetic algorithms
evolutionary dynamics
JEL: 
C63
C73
D83
Document Type: 
Working Paper

Files in This Item:
File
Size
98.55 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.