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: C63 C61 D83 D40 M31 C73 (search for similar items in EconPapers)
Date: 1999-10
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Persistent link: http://EconPapers.repec.org/RePEc:pra:mprapa:2837

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