Advanced Search
MyIDEAS: Login

Competing R&D Strategies in an Evolutionary Industry Model

Contents:

Author Info

  • 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.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
File URL: http://cournot.u-strasbg.fr/yildi/files/learnind.pdf
Our checks indicate that this address may not be valid because: 500 Can't connect to cournot.u-strasbg.fr:80 (10060). If this is indeed the case, please notify (Christopher F. Baum)
File Function: main text
Download Restriction: no

Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 1999 with number 343.

as in new window
Length:
Date of creation: 01 Mar 1999
Date of revision:
Handle: RePEc:sce:scecf9:343

Contact details of provider:
Postal: CEF99, Boston College, Department of Economics, Chestnut Hill MA 02467 USA
Fax: +1-617-552-2308
Web page: http://fmwww.bc.edu/CEF99/
More information through EDIRC

Related research

Keywords:

Other versions of this item:

This paper has been announced in the following NEP Reports:

References

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
as in new window
  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. Thomas Brenner, 1998. "Can evolutionary algorithms describe learning processes?," Journal of Evolutionary Economics, Springer, vol. 8(3), pages 271-283.
  3. 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.
  4. Silverberg, Gerald & Dosi, Giovanni & Orsenigo, Luigi, 1988. "Innovation, Diversity and Diffusion: A Self-organisation Model," Economic Journal, Royal Economic Society, vol. 98(393), pages 1032-54, December.
  5. 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.
  6. 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.
  7. 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 3.
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 in new window

Cited by:
  1. Witold Kwasnicki, 2002. "Evolutionary models’ comparative analysis. Methodology proposition based on selected neo-schumpeterian models of industrial dynamics," Microeconomics 0203002, EconWPA.
  2. Floortje Alkemade & Han Poutré & Hans Amman, 2006. "Robust Evolutionary Algorithm Design for Socio-economic Simulation," Computational Economics, Society for Computational Economics, vol. 28(4), pages 355-370, November.
  3. Murat Yildizoglu & Marc-Alexandre Sénégas & Isabelle Salle & Martin Zumpe, 2011. "Learning the optimal buffer-stock consumption rule of Carroll," Working Papers halshs-00573689, HAL.
  4. Herbert Dawid & Marc Reimann, 2005. "Evaluating Market Attractiveness: Individual Incentives Versus Industry Profitability," Computational Economics, Society for Computational Economics, vol. 24(4), pages 321-355, June.
  5. 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.

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:sce:scecf9:343. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum).

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 references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.

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