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Competing R&D Strategies in an Evolutionary Industry Model

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  • Yildizoglu, Murat

Abstract

This article aims to test the relevance of learning through genetic algorithms, in contrast to 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): simulations results clearly show that learning is a source of technological and social efficiency as well as a means for market domination. Copyright 2002 by Kluwer Academic Publishers

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  • 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.
    • Handle: RePEc:kap:compec:v:19:y:2002:i:1:p:51-65
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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, 2009. "Robust Evolutionary Algorithm Design for Socio-Economic Simulation: A Correction," Computational Economics, Springer;Society for Computational Economics, vol. 33(1), pages 99-101, February.
    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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