Using Genetics Based Machine Learning to find Strategies for Product Placement in a dynamic Market
AbstractIn 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.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 2837.
Date of creation: Oct 1999
Date of revision:
product positioning; market simulation; heterogeneous agents; learning classifier systems; genetic algorithms; adaptive systems modelling;
Find related papers by JEL classification:
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- Thomas Brenner, 1998. "Can evolutionary algorithms describe learning processes?," Journal of Evolutionary Economics, Springer, vol. 8(3), pages 271-283.
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