IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/2837.html
   My bibliography  Save this paper

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

Author

Listed:
  • Fent, Thomas

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.

Suggested Citation

  • Fent, Thomas, 1999. "Using Genetics Based Machine Learning to find Strategies for Product Placement in a dynamic Market," MPRA Paper 2837, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:2837
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/2837/1/MPRA_paper_2837.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Thomas Brenner, 1998. "Can evolutionary algorithms describe learning processes?," Journal of Evolutionary Economics, Springer, vol. 8(3), pages 271-283.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yildizoglu, Murat, 2002. "Competing R&D Strategies in an Evolutionary Industry Model," Computational Economics, Springer;Society for Computational Economics, vol. 19(1), pages 51-65, February.
    2. Tomas Klos, 1999. "Governance and Matching," Computing in Economics and Finance 1999 341, Society for Computational Economics.
    3. Shu-Heng Chen & Bin-Tzong Chie & Ying-Fang Kao & Ragupathy Venkatachalam, 2019. "Agent-Based Modeling of a Non-tâtonnement Process for the Scarf Economy: The Role of Learning," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 305-341, June.
    4. Jérome VICENTE (GRES-LEREPS), 2003. "From interaction economics to economic geography : theories and evidences (In French)," Cahiers du GRES (2002-2009) 2003-02, Groupement de Recherches Economiques et Sociales.
    5. Guido Buenstorf, 2012. "Introduction," Chapters, in: Guido Buenstorf (ed.), Evolution, Organization and Economic Behavior, chapter 1, Edward Elgar Publishing.
    6. Sylvie Geisendorf, 2011. "Internal selection and market selection in economic Genetic Algorithms," Journal of Evolutionary Economics, Springer, vol. 21(5), pages 817-841, December.
    7. Dahl, F.A., 2005. "The lagging anchor model for game learning--a solution to the Crawford puzzle," Journal of Economic Behavior & Organization, Elsevier, vol. 57(3), pages 287-303, July.
    8. Geoffrey Hodgson & Kainan Huang, 2012. "Evolutionary game theory and evolutionary economics: are they different species?," Journal of Evolutionary Economics, Springer, vol. 22(2), pages 345-366, April.
    9. Sándor Karajz, 2007. "Genetic Algorithms as Optimalisation Procedures," Theory Methodology Practice (TMP), Faculty of Economics, University of Miskolc, vol. 4(01), pages 37-41.
    10. Ulrich Witt, 2013. "The Future of Evolutionary Economics: Why Modalities Matter," Papers on Economics and Evolution 2013-09, Philipps University Marburg, Department of Geography.
    11. Sylvie Geisendorf & Christian Klippert, 2022. "Integrated sustainability policy assessment – an agent-based ecological-economic model," Journal of Evolutionary Economics, Springer, vol. 32(3), pages 1017-1048, July.
    12. Sieg, Gernot, 2001. "A political business cycle with boundedly rational agents," European Journal of Political Economy, Elsevier, vol. 17(1), pages 39-52, March.
    13. repec:dgr:rugsom:99b41 is not listed on IDEAS

    More about this item

    Keywords

    product positioning; market simulation; heterogeneous agents; learning classifier systems; genetic algorithms; adaptive systems modelling;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D40 - Microeconomics - - Market Structure, Pricing, and Design - - - General
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:2837. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.