Skip to main content
padlock icon - secure page this page is secure


Buy Article:

$52.00 + tax (Refund Policy)

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.
No References
No Citations
No Supplementary Data
No Article Media
No Metrics

Keywords: Cournot oligopoly; evolutionary algorithms; robust EA design

Document Type: Research Article

Affiliations: 1: Department of Innovation Studies, Utrecht University, Utrecht, The Netherlands 2: Centre for Computer Science and Mathematics (CWI), Amsterdam, The Netherlands; and Eindhoven University of Technology (TU/e), Eindhoven, The Netherlands 3: Utrecht School of Economics, Utrecht University, Utrecht, The Netherlands

Publication date: May 1, 2007

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more