Using Genetics Based Machine Learning to find Strategies for Product Placement in a dynamic Market

Thomas Fent

MPRA Paper from University Library of Munich, Germany

Abstract: In this paper we discuss the necessity of models including complex adaptive systems in order to eliminate the shortcomings of neoclassical models based on equilibrium theory. A simulation model containing artificial adaptive agents is used to explore the dynamics of a market of highly replaceable products. A population consisting of two classes of agents is implemented to observe if methods provided by modern computational intelligence can help finding a meaningful strategy for product placement. During several simulation runs it turned out that the agents using CI-methods outperformed their competitors.

Keywords: product positioning; market simulation; heterogeneous agents; learning classifier systems; genetic algorithms; adaptive systems modelling (search for similar items in EconPapers)
JEL-codes: C61 C63 C73 D40 D83 M31 (search for similar items in EconPapers)
Date: 1999-10
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/2837/1/MPRA_paper_2837.pdf original version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:2837

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().