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Breeding Competitive Strategies

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Author Info

  • David F. Midgley

    (Australian Graduate School of Management, University of New South Wales, Australia 2052)

  • Robert E. Marks

    (Australian Graduate School of Management, University of New South Wales, Australia 2052)

  • Lee C. Cooper

    (Anderson Graduate School of Management, University of California at Los Angeles, Los Angeles, California 90095)

Abstract

We show how genetic algorithms can be used to evolve strategies in oligopolistic markets characterized by asymmetric competition. The approach is illustrated using scanner tracking data of brand actions in a real market. An asymmetric market-share model and a category-volume model are combined to represent market response to the actions of brand managers. The actions available to each artificial brand manager are constrained to four typical marketing actions of each from the historical data. Each brand's strategies evolve through simulations of repeated interactions in a virtual market, using the estimated weekly profits of each brand as measures of its fitness for the genetic algorithm. The artificial agents bred in this environment outperform the historical actions of brand managers in the real market. The implications of these findings for the study of marketing strategy are discussed.

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File URL: http://dx.doi.org/10.1287/mnsc.43.3.257
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Bibliographic Info

Article provided by INFORMS in its journal Management Science.

Volume (Year): 43 (1997)
Issue (Month): 3 (March)
Pages: 257-275

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Handle: RePEc:inm:ormnsc:v:43:y:1997:i:3:p:257-275

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Related research

Keywords: competitive strategies; pricing; asymmetric market-share models; evolutionary algorithms; repeated games;

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Cited by:
  1. Chih-Chi Ni & Shu-Heng Chen, 1999. "Simulating the Ecology of Oligopoly Games with Genetic Algorithms," Computing in Economics and Finance 1999 1012, Society for Computational Economics.
  2. Shu-Heng Chen & Chung-Ching Tai, 2006. "Republication: On the Selection of Adaptive Algorithms in ABM: A Computational-Equivalence Approach," Computational Economics, Society for Computational Economics, vol. 28(4), pages 313-331, November.
  3. Daniel Ladley & Ian Wilkinson & Louise Young, 2013. "The Evolution Of Cooperation In Business: Individual Vs. Group Incentives," Discussion Papers in Economics 13/14, Department of Economics, University of Leicester.
  4. H. Fan, 2012. "Distribution Of Producer Size In Globalized Market," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 15(07), pages 1250076-1-1.
  5. Shu-Heng Chen & Chung-Ching Tai, 2006. "On the Selection of Adaptive Algorithms in ABM: A Computational-Equivalence Approach," Computational Economics, Society for Computational Economics, vol. 28(1), pages 51-69, August.
  6. Robert E. Marks, . "Evolved Perception and Behaviour in Oligopolies," Computing in Economics and Finance 1996 _038, Society for Computational Economics.
  7. Gruca, Thomas S. & Klemz, Bruce R., 2003. "Optimal new product positioning: A genetic algorithm approach," European Journal of Operational Research, Elsevier, vol. 146(3), pages 621-633, May.
  8. Fernando S. Oliveira & Derek W. Bunn & London Business School, 2006. "Modeling the strategic trading of electricity assets," Computing in Economics and Finance 2006 235, Society for Computational Economics.
  9. Steven Kimbrough & Frederic Murphy, 2009. "Learning to Collude Tacitly on Production Levels by Oligopolistic Agents," Computational Economics, Society for Computational Economics, vol. 33(1), pages 47-78, February.
  10. Augusto Rupérez Micola & Albert Banal Estañol & Derek W. Bunn, 2006. "Incentives and Coordination in Vertically Related Energy Markets," CIG Working Papers SP II 2006-02, Wissenschaftszentrum Berlin (WZB), Research Unit: Competition and Innovation (CIG).
  11. Robert E. Marks, 2013. "Validation and Functional Complexity," Discussion Papers 2013-30, School of Economics, The University of New South Wales.
  12. Floortje Alkemade & Han Poutré & Hans Amman, 2006. "Robust Evolutionary Algorithm Design for Socio-economic Simulation," Computational Economics, Society for Computational Economics, vol. 28(4), pages 355-370, November.
  13. Gottfried Haber, 2008. "Monetary and Fiscal Policy Analysis With an Agent-Based Macroeconomic Model," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 228(2+3), pages 276-295, June.
  14. Robert Marks, 2007. "Validating Simulation Models: A General Framework and Four Applied Examples," Computational Economics, Society for Computational Economics, vol. 30(3), pages 265-290, October.
  15. Butel, Lynne & Watkins, Alison, 2000. "Evolving Complex Organizational Structures in New and Unpredictable Environments," Journal of Business Research, Elsevier, vol. 47(1), pages 27-33, January.
  16. Fish, Kelly E. & Johnson, John D. & Dorsey, Robert E. & Blodgett, Jeffery G., 2004. "Using an artificial neural network trained with a genetic algorithm to model brand share," Journal of Business Research, Elsevier, vol. 57(1), pages 79-85, January.
  17. Geoffrey Hodgson & Thorbjoern Knudsen, 2012. "Agreeing on generalised Darwinism: a response to Pavel Pelikan," Journal of Evolutionary Economics, Springer, vol. 22(1), pages 9-18, January.

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