Breeding competitive strategies

Breeding competitive strategies

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Article ID: iaor1998601
Country: United States
Volume: 43
Issue: 3
Start Page Number: 257
End Page Number: 275
Publication Date: Mar 1997
Journal: Management Science
Authors: , ,
Keywords: game theory
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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