Competing R&D Strategies in an Evolutionary Industry Model
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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Volume (Year): 19 (2002)
Issue (Month): 1 (February)
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References listed on IDEAS
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- Thomas Brenner, 1998. "Can evolutionary algorithms describe learning processes?," Journal of Evolutionary Economics, Springer, vol. 8(3), pages 271-283.
- Nicolas Jonard & Murat Yildizoglu, .
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- 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.
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- 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.
- 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.
- GÃ©rard 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.
- 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.
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