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Connecting adaptive behaviour and expectations in models of innovation: The Potential Role of Artificial Neural Networks

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  • Murat Yildizoglu

Abstract

In this methodological work I explore the possibility of explicitly modelling expec-tations conditioning the R&D decisions of firms. In order to isolate this problem from the controversies of cognitive science, I propose a black box strategy through the concept of internal model . The last part of the article uses artificial neural networks to model the expectations of firms in a model of industry dynamics based on Nelson & Winter (1982).
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Suggested Citation

  • Murat Yildizoglu, 2002. "Connecting adaptive behaviour and expectations in models of innovation: The Potential Role of Artificial Neural Networks," Computing in Economics and Finance 2002 200, Society for Computational Economics.
  • Handle: RePEc:sce:scecf2:200
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    File URL: http://beagle.montesquieu.u-bordeaux.fr/ifrede/e3i/publications/2001/2001-2.pdf
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    References listed on IDEAS

    as
    1. Winter, Sidney G., 1984. "Schumpeterian competition in alternative technological regimes," Journal of Economic Behavior & Organization, Elsevier, vol. 5(3-4), pages 287-320.
    2. Kwasnicki, Witold, 1998. "Skewed distributions of firm sizes--an evolutionary perspective," Structural Change and Economic Dynamics, Elsevier, vol. 9(1), pages 135-158, March.
    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. 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 1-3.
    5. G. Silverberg & B. Verspagen, 1995. "Evolutionary Theorizing on Economic Growth," Working Papers wp95078, International Institute for Applied Systems Analysis.
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    Citations

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    Cited by:

    1. Yıldızoğlu, Murat & Sénégas, Marc-Alexandre & Salle, Isabelle & Zumpe, Martin, 2014. "Learning The Optimal Buffer-Stock Consumption Rule Of Carroll," Macroeconomic Dynamics, Cambridge University Press, vol. 18(04), pages 727-752, June.
    2. Roos, Michael W. M., 2015. "The macroeconomics of radical uncertainty," Ruhr Economic Papers 592, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    3. Fioretti, Guido, 2006. "Recognising investment opportunities at the onset of recoveries," Research in Economics, Elsevier, vol. 60(2), pages 69-84, June.
    4. 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.
    5. Aßmuth, Pascal, 2015. "Credit constrained R&D spending and technological change," Center for Mathematical Economics Working Papers 532, Center for Mathematical Economics, Bielefeld University.

    More about this item

    Keywords

    Neural networks; Genetic algorithms; Bounded rationality; learning; expectations; innovation dynamics;

    JEL classification:

    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights
    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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