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Competing R&D Strategies in an Evolutionary Industry Model

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  • Yildizoglu, Murat

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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Bibliographic Info

Article provided by Society for Computational Economics 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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  1. 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, UDS, Strasbourg.
  2. Thomas Brenner, 1998. "Can evolutionary algorithms describe learning processes?," Journal of Evolutionary Economics, Springer, vol. 8(3), pages 271-283.
  3. 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.
  4. 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.
  5. Gérard 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(2), pages 3.
  6. 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.
  7. 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.
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Cited by:
  1. Witold Kwasnicki, 2002. "Evolutionary models’ comparative analysis. Methodology proposition based on selected neo-schumpeterian models of industrial dynamics," Microeconomics 0203002, EconWPA.
  2. Murat YILDIZOGLU (GREQAM, CNRS, UMR 6579) & Marc-Alexandre SENEGAS (GREThA, CNRS, UMR 5113) & Isabelle SALLE (GREThA, CNRS, UMR 5113) & Martin ZUMPE (GREThA, CNRS, UMR 5113), 2011. "Learning the optimal buffer-stock consumption rule of Carroll," Cahiers du GREThA 2011-11, Groupe de Recherche en Economie Théorique et Appliquée.
  3. Karolina Safarzyńska & Jeroen Bergh, 2013. "An evolutionary model of energy transitions with interactive innovation-selection dynamics," Journal of Evolutionary Economics, Springer, vol. 23(2), pages 271-293, April.
  4. 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.
  5. Murat YILDIZOGLU (Université Aix-Marseille3), 2009. "Evolutionary approaches of economic dynamics (In French)," Cahiers du GREThA 2009-16, Groupe de Recherche en Economie Théorique et Appliquée.
  6. Herbert Dawid & Marc Reimann, 2005. "Evaluating Market Attractiveness: Individual Incentives Versus Industry Profitability," Computational Economics, Society for Computational Economics, vol. 24(4), pages 321-355, June.

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