Competing R&D Strategies in an Evolutionary Industry Model
AbstractThis 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 InfoArticle provided by Society for Computational Economics in its journal Computational Economics.
Volume (Year): 19 (2002)
Issue (Month): 1 (February)
Other versions of this item:
- Murat Yildizoglu, 1999. "Competing R&D Strategies in an Evolutionary Industry Model," Working Papers of BETA 9914, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
- Murat Yildizoglu, 1999. "Competing R&D Strategies in an Evolutionary Industry Model," Computing in Economics and Finance 1999 343, Society for Computational Economics.
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.:
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- 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.
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