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Competing R&D Strategies in an Evolutionary Industry Model

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Yildizoglu, Murat

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Abstract

This article aims to test the relevance of learning through genetic algorithms, in contrast to fixed R&D rules, in a simplified version of the evolutionary industry model of Nelson and Winter. These two R&D strategies are compared from the points of view of industry performance (welfare) and firms' relative performance (competitive edge): simulations results clearly show that learning is a source of technological and social efficiency as well as a means for market domination. Copyright 2002 by Kluwer Academic Publishers

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Article provided by Springer in its journal Computational Economics.

Volume (Year): 19 (2002)
Issue (Month): 1 (February)
Pages: 51-65
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Handle: RePEc:kap:compec:v:19:y:2002:i:1:p:51-65

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Web page: http://www.springerlink.com/link.asp?id=100248

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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Jonard, N. & Yfldizoglu, M., 1998. "Technological diversity in an evolutionary industry model with localized learning and network externalities," Structural Change and Economic Dynamics, Elsevier, vol. 9(1), pages 35-53, March. [Downloadable!] (restricted)
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  2. Silverberg, Gerald & Dosi, Giovanni & Orsenigo, Luigi, 1988. "Innovation, Diversity and Diffusion: A Self-organisation Model," Economic Journal, Royal Economic Society, vol. 98(393), pages 1032-54, December. [Downloadable!] (restricted)
  3. Vriend, Nicolaas J., 2000. "An illustration of the essential difference between individual and social learning, and its consequences for computational analyses," Journal of Economic Dynamics and Control, Elsevier, vol. 24(1), pages 1-19, January. [Downloadable!] (restricted)
  4. Kwasnicki, Witold & Kwasnicka, Halina, 1992. "Market, innovation, competition: An evolutionary model of industrial dynamics," Journal of Economic Behavior & Organization, Elsevier, vol. 19(3), pages 343-368, December. [Downloadable!] (restricted)
  5. Thomas Brenner, 1998. "Can evolutionary algorithms describe learning processes?," Journal of Evolutionary Economics, Springer, vol. 8(3), pages 271-283. [Downloadable!] (restricted)
  6. Vanessa Oltra & Murat Yildizoglu, 1999. "Non Expectations and Adaptive Behaviours: the Missing Trade-off in Models of Innovation," Working Papers of BETA 9915, Bureau d'Economie Théorique et Appliquée, ULP, Strasbourg. [Downloadable!]
  7. Gerard Ballot & Erol Taymaz, 1999. "Technological Change, Learning and Macro-Economic Coordination: an Evolutionary Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 2. [Downloadable!]
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Herbert Dawid & Marc Reimann, 2005. "Evaluating Market Attractiveness: Individual Incentives Versus Industry Profitability," Computational Economics, Springer, vol. 24(4), pages 321-355, June. [Downloadable!] (restricted)
  2. Witold Kwasnicki, 2002. "Evolutionary models’ comparative analysis. Methodology proposition based on selected neo-schumpeterian models of industrial dynamics," Microeconomics 0203002, EconWPA. [Downloadable!]
  3. Floortje Alkemade & Han Poutré & Hans Amman, 2006. "Robust Evolutionary Algorithm Design for Socio-economic Simulation," Computational Economics, Springer, vol. 28(4), pages 355-370, November. [Downloadable!] (restricted)
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