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

Fent, Thomas (1999): Using Genetics Based Machine Learning to find Strategies for Product Placement in a dynamic Market. Unpublished.

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.

• Item Type: MPRA Paper
• Institution: Vienna University of Economics and Business Administration
• Language: English
• Keywords: product positioning; market simulation; heterogeneous agents; learning classifier systems; genetic algorithms; adaptive systems modelling
• Subjects: C - Mathematical and Quantitative Methods > C6 - Mathematical Methods and Programming > C63 - Computational Techniques; Simulation Modeling ; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods and Programming > C61 - Optimization Techniques; Programming Models; Dynamic Analysis; D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D83 - Search; Learning; Information and Knowledge; Communication; Belief; D - Microeconomics > D4 - Market Structure and Pricing > D40 - General; M - Business Administration and Business Economics; Marketing; Accounting > M3 - Marketing and Advertising > M31 - Marketing; C - Mathematical and Quantitative Methods > C7 - Game Theory and Bargaining Theory > C73 - Stochastic and Dynamic Games; Evolutionary Games; Repeated Games

References

Brenner, T. (1996). Learning in a repeated decision process: A variation-imitation-decision model. Papers on Economics & Evolution #9603, Max-Planck-Institute for Research into Economic Systems.

Brenner, T. (1998) Can evolutionary algorithms describe learning processes? Journal of Evolutionary Economics, 8:271-283

Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Publishing Company.

Holland, J. H. (1976). Adaption. In Progress in Theoretical Biology IV. ed. Rosen, R. F., New York, Academic Press.

Holland, J. H. (1995). Adaption in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press/Bradford Books edition, Ann Arbor, MI.

Holland, J. H., Holyoak, K. J., Nisbeth, R. E., and Thagard, P. R. (1997) Induction, Processes of Inference, Learning, and Discovery. The MIT Press, Cambridge, Massachusetts, London, England.

Kotler, P., Armstrong, G., Saunders, J., and Wrong, V. (1996). Priciples of Marketing, The European Edition, Prentice Hall Europe, Campus 400, Maylands Avenue, Hemel Hampstead, Herfordshire, HP2 7EZ.

Polani, D. and Uthmann, T. (1999). Dmarks: Eine verteilte Umgebung für agentenbasierte Simulationen von Marktszenarien. In Hohmann, G., editor, Simulationstechnik, 13. Syposium in Weimar, volume 3 of Frontiers in Simulation, pages 391-394. SCS - The Society for Computer Simulation International in cooperation with ASIM - Arbeitsgemeinschaft Simulation.