IDEAS home Printed from https://ideas.repec.org/p/sce/scecf2/200.html
   My bibliography  Save this paper

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

Author

Listed:
  • Murat Yildizoglu

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).
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item
(This abstract was borrowed from another version of this item.)

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
    as

    Download full text from publisher

    File URL: http://beagle.montesquieu.u-bordeaux.fr/ifrede/e3i/publications/2001/2001-2.pdf
    File Function: main text
    Download Restriction: no
    ---><---

    Other versions of this item:

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Aßmuth, Pascal, 2014. "Credit Constrained R&D Spending and Technological Change," Center for Mathematical Economics Working Papers 532, Center for Mathematical Economics, Bielefeld University.
    4. 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).
    5. Fioretti, Guido, 2006. "Recognising investment opportunities at the onset of recoveries," Research in Economics, Elsevier, vol. 60(2), pages 69-84, June.
    6. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Sandra Silva, 2009. "On evolutionary technological change and economic growth: Lakatos as a starting point for appraisal," Journal of Evolutionary Economics, Springer, vol. 19(1), pages 111-135, February.
    3. Yildizoglu, Murat, 2002. "Competing R&D Strategies in an Evolutionary Industry Model," Computational Economics, Springer;Society for Computational Economics, vol. 19(1), pages 51-65, February.
    4. Kurt Dopfer, 2012. "The origins of meso economics," Journal of Evolutionary Economics, Springer, vol. 22(1), pages 133-160, January.
    5. Vanessa Oltra & Maider Saint Jean, 2005. "The dynamics of environmental innovations: three stylised trajectories of clean technology," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 14(3), pages 189-212.
    6. Witold Kwasnicki, 1996. "Innovation regimes, entry and market structure," Journal of Evolutionary Economics, Springer, vol. 6(4), pages 375-409.
    7. Windrum, Paul, 1999. "Simulation models of technological innovation: A Review," Research Memorandum 005, Maastricht University, Maastricht Economic Research Institute on Innovation and Technology (MERIT).
    8. Silverberg, Gerald, 1997. "Evolutionary modeling in economics : recent history and immediate prospects," Research Memorandum 008, Maastricht University, Maastricht Economic Research Institute on Innovation and Technology (MERIT).
    9. Alp Eren Yurtseven & Mehmet Teoman Pamukçu, 2022. "Innovation patterns in firms and intra-industry heterogeneity empirical evidence from Turkey," Evolutionary and Institutional Economics Review, Springer, vol. 19(2), pages 645-679, September.
    10. Fulvio Castellacci, 2011. "Theoretical Models of Heterogeneity, Growth and Competitiveness: Insights from the Mainstream and Evolutionary Economics Paradigms," Chapters, in: Miroslav N. Jovanović (ed.), International Handbook on the Economics of Integration, Volume II, chapter 5, Edward Elgar Publishing.
    11. Esben Sloth Andersen, 1996. "The Evolution of an Industrial Sector with a Varying Degree of Roundaboutness of Production," DRUID Working Papers 96-13, DRUID, Copenhagen Business School, Department of Industrial Economics and Strategy/Aalborg University, Department of Business Studies.
    12. Dosi, Giovanni & Nelson, Richard R., 2010. "Technical Change and Industrial Dynamics as Evolutionary Processes," Handbook of the Economics of Innovation, in: Bronwyn H. Hall & Nathan Rosenberg (ed.), Handbook of the Economics of Innovation, edition 1, volume 1, chapter 0, pages 51-127, Elsevier.
    13. Giovanni Dosi & Sébastien Lechevalier & Angelo Secchi, 2010. "Interfirm heterogeneity: nature, sources and consequences for industrial dynamics. An introduction," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00642680, HAL.
    14. Karolina Safarzyńska & Jeroen Bergh, 2010. "Evolutionary models in economics: a survey of methods and building blocks," Journal of Evolutionary Economics, Springer, vol. 20(3), pages 329-373, June.
    15. 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).
    16. G. Silverberg & B. Verspagen, 1995. "Evolutionary Theorizing on Economic Growth," Working Papers wp95078, International Institute for Applied Systems Analysis.
    17. Sydney Winter & Giovanni Dosi, 2000. "Interpreting Economic Change: Evolution, Structures and Games," LEM Papers Series 2000/08, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    18. Esben Sloth Andersen, 1999. "Multisectoral Growth and National Innovation Systems," Nordic Journal of Political Economy, Nordic Journal of Political Economy, vol. 25, pages 33-52.
    19. Michael Peneder, 2003. "Industry Classifications: Aim, Scope and Techniques," Journal of Industry, Competition and Trade, Springer, vol. 3(1), pages 109-129, March.
    20. Gilberto Tadeu Lima, 2000. "Market concentration and technological innovation in a dynamic model of growth and distribution," BNL Quarterly Review, Banca Nazionale del Lavoro, vol. 53(215), pages 447-475.

    More about this item

    Keywords

    Neural networks; Genetic algorithms; Bounded rationality; learning; expectations; innovation dynamics;
    All these keywords.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sce:scecf2:200. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F. Baum (email available below). General contact details of provider: https://edirc.repec.org/data/sceeeea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.