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ON SOCIAL LEARNING AND ROBUST EVOLUTIONARY ALGORITHM DESIGN IN THE COURNOT OLIGOPOLY GAME
Author(s) -
Alkemade Floortje,
La Poutré Han,
Amman Hans M.
Publication year - 2007
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.2007.00300.x
Subject(s) - cournot competition , computer science , robustness (evolution) , evolutionary algorithm , genetic algorithm , convergence (economics) , artificial intelligence , field (mathematics) , mathematical optimization , evolutionary computation , cultural algorithm , machine learning , algorithm , mathematics , mathematical economics , economics , population based incremental learning , biochemistry , chemistry , pure mathematics , gene , economic growth
Agent‐based computational economics (ACE) combines elements from economics and computer science. In this article, the focus is on the relation between the evolutionary technique that is used and the economic problem that is modeled. In the field of ACE, economic simulations often derive parameter settings for the genetic algorithm directly from the values of the economic model parameters. This article compares two important approaches that are dominating in ACE and shows that the above practice may hinder the performance of the genetic algorithm and thereby hinder agent learning. More specifically, it is shown that economic model parameters and evolutionary algorithm parameters should be treated separately by comparing the two widely used approaches to social learning with respect to their convergence properties and robustness. This leads to new considerations for the methodological aspects of evolutionary algorithm design within the field of ACE.