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Breeding Competitive Strategies

Author

Listed:
  • 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.

Suggested Citation

  • David F. Midgley & Robert E. Marks & Lee C. Cooper, 1997. "Breeding Competitive Strategies," Management Science, INFORMS, vol. 43(3), pages 257-275, March.
  • Handle: RePEc:inm:ormnsc:v:43:y:1997:i:3:p:257-275
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    File URL: http://dx.doi.org/10.1287/mnsc.43.3.257
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    Citations

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    Cited by:

    1. Klemz, Bruce R., 1999. "Using genetic algorithms to assess the impact of pricing activity timing," Omega, Elsevier, vol. 27(3), pages 363-372, June.
    2. Herbert Dawid & Philipp Harting, 2012. "Capturing Firm Behavior in Agent-based Models of Industry Evolution and Macroeconomic Dynamics," Chapters,in: Evolution, Organization and Economic Behavior, chapter 6 Edward Elgar Publishing.
    3. 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.
    4. Micola, Augusto Rupérez & Banal-Estañol, Albert & Bunn, Derek W., 2008. "Incentives and coordination in vertically related energy markets," Journal of Economic Behavior & Organization, Elsevier, vol. 67(2), pages 381-393, August.
    5. repec:wsi:ijitdm:v:11:y:2012:i:05:n:s0219622012500277 is not listed on IDEAS
    6. 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.
    7. Rajkumar Venkatesan & Trichy V. Krishnan & V. Kumar, 2004. "Evolutionary Estimation of Macro-Level Diffusion Models Using Genetic Algorithms: An Alternative to Nonlinear Least Squares," Marketing Science, INFORMS, vol. 23(3), pages 451-464, August.
    8. Tay, Nicholas S.P. & Lusch, Robert F., 2005. "A preliminary test of Hunt's General Theory of Competition: using artificial adaptive agents to study complex and ill-defined environments," Journal of Business Research, Elsevier, vol. 58(9), pages 1155-1168, September.
    9. Recchioni, Maria Cristina & Tedeschi, Gabriele & Gallegati, Mauro, 2015. "A calibration procedure for analyzing stock price dynamics in an agent-based framework," Journal of Economic Dynamics and Control, Elsevier, vol. 60(C), pages 1-25.
    10. Marks, Robert, 1998. "Evolved perception and behaviour in oligopolies," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1209-1233, August.
    11. Malgorzata Latuszynska & Agata Wawrzyniak & Barbara Wasikowska & Fatimah Furaji, 2012. "Application of rough sets to identify the behavior rules of consumer for the purposes of multi-agent simulation model (Zastosowanie zbiorow przyblizonych do wykrywania regul zachowania konsumentow na ," Problemy Zarzadzania, University of Warsaw, Faculty of Management, vol. 10(38), pages 104-123.
    12. Robert Marks, 2007. "Validating Simulation Models: A General Framework and Four Applied Examples," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 265-290, October.
    13. 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.
    14. 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.
    15. Keen, Steve, 2003. "Standing on the toes of pygmies:," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 108-116.
    16. 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.
    17. K. Sudhir, 2001. "Structural Analysis of Manufacturer Pricing in the Presence of a Strategic Retailer," Marketing Science, INFORMS, pages 244-264.
    18. Haber Gottfried, 2008. "Monetary and Fiscal Policy Analysis With an Agent-Based Macroeconomic Model," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 228(2-3), pages 276-295, April.
    19. Ladley, Daniel & Wilkinson, Ian & Young, Louise, 2015. "The impact of individual versus group rewards on work group performance and cooperation: A computational social science approach," Journal of Business Research, Elsevier, vol. 68(11), pages 2412-2425.
    20. Robert E. Marks, 2013. "Validation and Functional Complexity," Discussion Papers 2013-30, School of Economics, The University of New South Wales.
    21. repec:eee:touman:v:62:y:2017:i:c:p:335-349 is not listed on IDEAS
    22. 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.
    23. 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.

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