Learning and behavioral stability An economic interpretation of genetic algorithms
AbstractThis article tries to connect two separate strands of literature concerning genetic algorithms. On the one hand, extensive research took place in mathematics and closely related sciences in order to find out more about the properties of genetic algorithms as stochastic processes. On the other hand, recent economic literature uses genetic algorithms as a metaphor for social learning. This paper will face the question of what an economist can learn from the mathematical branch of research, especially concerning the convergence and stability properties of the genetic algorithm. It is shown that genetic algorithm learning is a compound of three different learning schemes. First, each particular scheme is analyzed. Then it is shown that it is the combination of the three schemes that gives genetic algorithm learning its special flair: A kind of stability somewhere in between asymptotic convergence and explosion.
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Bibliographic InfoArticle provided by Springer in its journal Journal of Evolutionary Economics.
Volume (Year): 9 (1999)
Issue (Month): 2 ()
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Web page: http://link.springer.de/link/service/journals/00191/index.htm
Other versions of this item:
- Riechmann, Thomas, 1997. "Learning and Behavoiral Stability - An Economic Interpretation of Genetic Algorithms," Hannover Economic Papers (HEP) dp-209, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
- C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
- D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
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