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Breeding Competitive Strategies


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  • David F. Midgley
  • Robert E. Marks
  • Lee G. Cooper


We are interested in the strategic implications of asymmetric competition. Previous work (Carpenter, Cooper, Hanssens and Midgley [CCHM] 1988) has estimated the Nash-equilibrium prices and advertising expenditures for asymmetric market-share models in the extreme cases of no competitive reaction and optimal competitive reaction. There are, however, three important limitations to building marketing plans on either of these competitive scenarios. First, such static, single-period strategies do not provide insight into the actions undertaken over time by major manufacturers and retailers. Strategies such as ad pulsing versus more continuous exposures, or every-day-low-pricing versus deep discounting are played out over time. As we called in the CCHM study, it is time to investigate dynamic, multiperiod strategies. Second, major sources of asymmetries are missing from the CCHM equilibrium analysis. There are two main sources of asymmetries. Asymmetries can arise from stable, cross-competitive effects. The price-tiers hypothesis (Blattberg and Wisniewski 1989), for example, indicates that when national brands go on sale they exert a stable, competitive pressure on regional brands that the regional brands can not counter with their own price reductions. When regional brands go on sale they exert pressure on the economy and private-label brands that these brands can not return. These are asymmetric cross-competitive effects. One brand on sale by itself might gain much more than if it was promoted along with four other brands in the category. The distinctiveness of a brand's offering can produce asymmetric competition in a particular choice context (Nakanishi et al. 1974, Cooper and Nakanishi 1983 and 1988). While the CCHM study incorporated measures of distinctiveness into their development of methods for reflecting asymmetric competition, the equilibrium analysis used a simpler model that did not account for this source of asymmetries. Third, the CCHM effort studied market share, while the great swings in sales levels we observe in retail scanner data encourage us to study the strategic implications of asymmetric sales response. We want to investigate multi-period strategies, when the market response is fundamentally asymmetric in both sales volume and market share. There are major barriers to traditional avenues of investigation. Mathematical exploration is hampered because sources of asymmetry explicitly violate the global0-convexity requirements of normative economic models. One major alternative to mathematical exploration is multi-period simulations, such as Axelrod's first tournament (Axelrod 1984) or the fader/Hauser tournament (Fader and Hauser 1988). While these have the advantage of allowing strategies to be played out over time, so far they have only been undertaken with symmetric and hypothetical market-response functions. We want to use asymmetric market-response functions that characterize brand behavior in real, markets to study the evolution of robust strategies. Genetic algorithms (Holland 1975) provide a mechanism by which we can pursue the study of the evolution of robust strategies. The next section describes genetic algorithms and how adaptable they are to the study of marketing strategy. This is followed by a discussion of a regional U.S. coffee market previously analyzed from a perspective of asymmetric competition (Cooper 1988, Cooper and Nakanishi 1988). Data from an asymmetric model of this market are then used along with a genetic algorithm to breed simple artificial agents for this market. We will demonstrate that in the limited tests we can feasibly conduct these agents outperform the historical actions of brand managers in this regioal market. Finally, we will discuss the reasons why this might be so and what canb e done to extend our approach. While we will primarily focus on one set of market modeling techniques, and one particular market, it is important to stress that the methods we propose have greater applicability. Indeed they canb e used in any marketing situation where there is a good representation of the profit consequences of competitive marketing actions. This representation might be in the form of an explicity linear or nonlinear model or it might be more of a "black-box" representation (e.g., numerical approximation or neural net). Given such a profit function, artificial agents can be formulated and genetically optimized to play multiperiod dynamic games in a robust and profitable manner. Our emphasis on asymmetric market modeling in the CCHM tradition, and on a regional coffee market, simply provides one case illustration of the overall approach.

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Bibliographic Info

Paper provided by Santa Fe Institute in its series Working Papers with number 95-06-052.

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Date of creation: Jun 1995
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Handle: RePEc:wop:safiwp:95-06-052

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Cited by:
  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. Marks, Robert, 1998. "Evolved perception and behaviour in oligopolies," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1209-1233, August.
  7. 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.
  8. 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).
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. Robert E. Marks, 2013. "Validation and Functional Complexity," Discussion Papers 2013-30, School of Economics, The University of New South Wales.
  15. 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.
  16. 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.
  17. 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.


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