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Competing R&D Strategies in an Evolutionary Industry Model

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  • Murat Yildizoglu

    () (Louis Pasteur University)

Abstract

Early evolutionary models of industry dynamics have used very simple ways of modeling bounded rationality. In the precursory work of Nelson and Winter (1982), for example, R&D decisions of firms are given by a fixed rule: firms invest in each period a fixed proportion of their capital stock in imitative and innovative R&D. Recent models have introduced more elaborate ways of modeling learning with bounded rationality, implicitly through replicator dynamics or simple adaptive mechanisms or explicitly through genetic algorithms or classifiers. Oltra & Yildizoglu (1998) provides a thorough analysis of different alternatives and proposes a general approach. In this work, I adopt a simpler framework to study the role of learning in industry dynamics. I use a simplified version of the initial model of Nelson and Winter (1982) that aims to neutralize the effects of the very peculiar capital-investment decision used in this model. With this version and its well-specified dynamics, I study the confrontation of two different types of investment behavior in Research and Development. The first corresponds to an updated version of Nelson and Winter's fixed-rule behavior: in each period, each firm invests a fixed proportion of its cash-flow on R&D. The second type of behavior includes learning: firms try to adapt their R&D/Cash-Flow ratio to the conditions of the industry. Learning is modeled here through the use of genetic algorithms by this type of firm. Both types of firms coexist initially in the industry. This simple framework is used to answer several questions that can be grouped under two headings: 1) The use of fixed R&D rules does not contradict the empirical evidence. One effectively observes quite stable R&D/CF ratios in industries, but it is important to study if this type of behavior is coherent with the presence of learning or if it can be endogenously generated in evolutionary models. 2) More theoretically, it is important to see if the explicit inclusion of learning in industry models is worthwhile: Does it enrich our understanding of technology dynamics? Does it suggest a competitive edge for strategies strongly based on learning? Does learning give a better chance of success in the long term? These questions are studied in a simulation program developed in Java. A first version of the program is already available in my web site.

Suggested Citation

  • Murat Yildizoglu, 1999. "Competing R&D Strategies in an Evolutionary Industry Model," Computing in Economics and Finance 1999 343, Society for Computational Economics.
  • Handle: RePEc:sce:scecf9:343
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    References listed on IDEAS

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    1. Thomas Brenner, 1998. "Can evolutionary algorithms describe learning processes?," Journal of Evolutionary Economics, Springer, vol. 8(3), pages 271-283.
    2. Silverberg, Gerald & Dosi, Giovanni & Orsenigo, Luigi, 1988. "Innovation, Diversity and Diffusion: A Self-organisation Model," Economic Journal, Royal Economic Society, vol. 98(393), pages 1032-1054, December.
    3. 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.
    4. 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.
    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. 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.
    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.
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    Cited by:

    1. 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.
    2. Diego d'Andria & Ivan Savin, 2015. "Motivating innovation in a knowledge economy with tax incentives," Jena Economic Research Papers 2015-004, Friedrich-Schiller-University Jena.
    3. 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.
    4. Dosi, Giovanni & Nelson, Richard R., 2010. "Technical Change and Industrial Dynamics as Evolutionary Processes," Handbook of the Economics of Innovation, Elsevier.
    5. 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.
    6. Herbert Dawid & Philipp Harting, 2012. "Capturing Firm Behavior in Agent-based Models of Industry Evolution and Macroeconomic Dynamics," Chapters,in: Evolution, Organization and Economic Behavior, chapter 6 Edward Elgar Publishing.
    7. 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.
    8. 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.
    9. 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.
    10. Aßmuth, Pascal, 2015. "Credit constrained R&D spending and technological change," Center for Mathematical Economics Working Papers 532, Center for Mathematical Economics, Bielefeld University.
    11. 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.
    12. repec:eee:tefoso:v:127:y:2018:i:c:p:38-56 is not listed on IDEAS

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