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

Author

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  • Murat Yildizoglu

    (GREThA - Groupe de Recherche en Economie Théorique et Appliquée - UB - Université de Bordeaux - CNRS - Centre National de la Recherche Scientifique)

Abstract

In this methodological work I explore the possibility of explicitly modelling expectations 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," Post-Print hal-00160360, HAL.
    • Handle: RePEc:hal:journl:hal-00160360
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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 & 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.
    3. 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.
    4. Kwasnicki, Witold, 1998. "Skewed distributions of firm sizes--an evolutionary perspective," Structural Change and Economic Dynamics, Elsevier, vol. 9(1), pages 135-158, March.
    5. G. Silverberg & B. Verspagen, 1995. "Evolutionary Theorizing on Economic Growth," Working Papers wp95078, International Institute for Applied Systems Analysis.
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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(4), 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. Aßmuth, Pascal, 2014. "Credit Constrained R&D Spending and Technological Change," Center for Mathematical Economics Working Papers 532, Center for Mathematical Economics, Bielefeld University.
    5. Leiponen, Aija & Drejer, Ina, 2007. "What exactly are technological regimes?: Intra-industry heterogeneity in the organization of innovation activities," Research Policy, Elsevier, vol. 36(8), pages 1221-1238, October.
    6. Murat YILDIZOGLU, 2009. "Evolutionary approaches of economic dynamics (In French)," Cahiers du GREThA (2007-2019) 2009-16, Groupe de Recherche en Economie Théorique et Appliquée (GREThA).

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    More about this item

    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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