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Modeling Adaptive Learning: R&D Strategies in the Model of Nelson & Winter (1982)

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  • Murat Yildizoglu

Abstract

This article aims to test the relevance of learning through Genetic Algorithms (GA) and Learning Classifier Systems (LCS), in opposition with fixed R&D rules, in a simplified version of the evolutionary industry model of Nelson and Winter. These three R&D strategies are compared from the points of view of industry performance (welfare): the results of simulations clearly show that learning is a source of technological and social efficiency.

Suggested Citation

  • Murat Yildizoglu, 2001. "Modeling Adaptive Learning: R&D Strategies in the Model of Nelson & Winter (1982)," Working Papers 2001-1, Equipe Industries Innovation Institutions, Université Bordeaux IV, France.
  • Handle: RePEc:iii:wpeiii:2001-1
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    File URL: http://beagle.u-bordeaux4.fr/ifrede/e3i/publications/2001/2001-1.pdf
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    Cited by:

    1. 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.

    More about this item

    Keywords

    Learning; Learning Classifier Systems; Bounded Rationality; Technical Progress; Innovation;

    JEL classification:

    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights
    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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