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Competing R&D Strategies in an Evolutionary Industry Model

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  • Murat Yildizoglu

Abstract

This article aims to test the relevance of learning through Genetic Algorithms, in opposition with 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): the results of simulations clearly show that learning is a source of technological and social efficiency as well as a mean for market domination.

Suggested Citation

  • 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.
    • Handle: RePEc:ulp:sbbeta:9914
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    References listed on IDEAS

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    1. Jonard, N. & Yfldizoglu, M., 1998. "Technological diversity in an evolutionary industry model with localized learning and network externalities," Structural Change and Economic Dynamics, Elsevier, vol. 9(1), pages 35-53, March.
    2. Murat Yildizoglu & Nicolas Jonard, 1998. "Evolution and Diversity in an Industry Model with Localized Learning and Network Externalities," Post-Print hal-00125275, HAL.
    3. Thomas Brenner, 1998. "Can evolutionary algorithms describe learning processes?," Journal of Evolutionary Economics, Springer, vol. 8(3), pages 271-283.
    4. Gerald Silverberg & Giovanni Dosi & Luigi Orsenigo, 2000. "Innovation, Diversity and Diffusion: A Self-Organisation Model," Chapters, in: Innovation, Organization and Economic Dynamics, chapter 14, pages 410-432, Edward Elgar Publishing.
    5. 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.
    6. 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.
    7. Vriend, Nicolaas J., 2000. "An illustration of the essential difference between individual and social learning, and its consequences for computational analyses," Journal of Economic Dynamics and Control, Elsevier, vol. 24(1), pages 1-19, January.
    8. 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.
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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. 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.
    3. Herbert Dawid & Philipp Harting, 2012. "Capturing Firm Behavior in Agent-based Models of Industry Evolution and Macroeconomic Dynamics," Chapters, in: Guido Buenstorf (ed.), Evolution, Organization and Economic Behavior, chapter 6, Edward Elgar Publishing.
    4. Floortje Alkemade & Han Poutré & Hans Amman, 2006. "Robust Evolutionary Algorithm Design for Socio-economic Simulation," Computational Economics, Springer;Society for Computational Economics, vol. 28(4), pages 355-370, November.
    5. Safarzynska, Karolina & van den Bergh, Jeroen C.J.M., 2011. "Beyond replicator dynamics: Innovation-selection dynamics and optimal diversity," Journal of Economic Behavior & Organization, Elsevier, vol. 78(3), pages 229-245, May.
    6. Aßmuth, Pascal, 2014. "Credit Constrained R&D Spending and Technological Change," Center for Mathematical Economics Working Papers 532, Center for Mathematical Economics, Bielefeld University.
    7. Pascal Aßmuth, 2018. "The Impact of Credit Rating on Innovation in a Two-Sector Evolutionary Model," Computational Economics, Springer;Society for Computational Economics, vol. 52(3), pages 839-872, October.
    8. CATTARUZZO Sebastiano, 2020. "On R&D sectoral intensities and convergence clubs," JRC Working Papers on Corporate R&D and Innovation 2020-01, Joint Research Centre.
    9. Karolina Safarzyńska & Jeroen Bergh, 2013. "An evolutionary model of energy transitions with interactive innovation-selection dynamics," Journal of Evolutionary Economics, Springer, vol. 23(2), pages 271-293, April.
    10. Herbert Dawid & Marc Reimann, 2005. "Evaluating Market Attractiveness: Individual Incentives Versus Industry Profitability," Computational Economics, Springer;Society for Computational Economics, vol. 24(4), pages 321-355, June.
    11. Diego d'Andria & Ivan Savin, 2015. "Motivating innovation in a knowledge economy with tax incentives," Jena Economics Research Papers 2015-004, Friedrich-Schiller-University Jena.
    12. Witold Kwasnicki, 2002. "Evolutionary models’ comparative analysis. Methodology proposition based on selected neo-schumpeterian models of industrial dynamics," Microeconomics 0203002, University Library of Munich, Germany.
    13. Diego d’Andria, 2019. "Tax policy and entrepreneurial entry with information asymmetry and learning," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 26(5), pages 1211-1229, October.
    14. 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).
    15. d’Andria, D. & Savin, I., 2018. "A Win-Win-Win? Motivating innovation in a knowledge economy with tax incentives," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 38-56.

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