IDEAS home Printed from https://ideas.repec.org/p/jrp/jrpwrp/2009-039.html
   My bibliography  Save this paper

The Random Part in Network Evolution

Author

Listed:
  • Thomas Grebel

    () (Economics Department, University of Jena)

Abstract

Economic behavior strives for efficiency. Therefore, also evolving network structures should be a result of such a goal-oriented behavior. Traditionally, networks were assumed to be only temporary phenomena, since the prevailing organizational forms that comply with the efficiency postulate are either firms or markets. Having a goal in mind, however, does not incur a set of unique choices of action, especially in situations under high uncertainty when engaging in invention networks. Consequently, there is no uniqueness in network structures. There is a random part in network evolution driven by generic mechanisms. A percolation model is used to model the generic development of invention networks. A Monte-Carlo simulation underlines the expectable patterns of network evolution. Moreover, it is tried to align the generic part of the story to the operant level where entrepreneurial behavior and market selection takes over the dominant role in network formation.

Suggested Citation

  • Thomas Grebel, 2009. "The Random Part in Network Evolution," Jena Economic Research Papers 2009-039, Friedrich-Schiller-University Jena.
  • Handle: RePEc:jrp:jrpwrp:2009-039
    as

    Download full text from publisher

    File URL: http://zs.thulb.uni-jena.de/receive/jportal_jparticle_00150055
    Download Restriction: no

    References listed on IDEAS

    as
    1. Crepon, B. & Duguet, E. & Mairesse, J., 1998. "Research Investment, Innovation and Productivity: An Econometric Analysis at the Firm Level," Papiers d'Economie Mathématique et Applications 98.15, Université Panthéon-Sorbonne (Paris 1).
    2. Francesco Crespi & Mario Pianta, 2008. "Diversity in innovation and productivity in Europe," Journal of Evolutionary Economics, Springer, vol. 18(3), pages 529-545, August.
    3. Stephen D. Oliner & Daniel E. Sichel, 2000. "The Resurgence of Growth in the Late 1990s: Is Information Technology the Story?," Journal of Economic Perspectives, American Economic Association, vol. 14(4), pages 3-22, Fall.
    4. Mariacristina Piva & Marco Vivarelli, 2002. "The Skill Bias: Comparative evidence and an econometric test," International Review of Applied Economics, Taylor & Francis Journals, vol. 16(3), pages 347-357.
    5. Hans Loof & Almas Heshmati, 2006. "On the relationship between innovation and performance: A sensitivity analysis," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 15(4-5), pages 317-344.
    6. Bronwyn H. Hall, 2010. "Measuring the Returns to R&D: The Depreciation Problem," NBER Chapters,in: Contributions in Memory of Zvi Griliches, pages 341-381 National Bureau of Economic Research, Inc.
    7. Nadiri, M Ishaq & Prucha, Ingmar R, 1996. "Estimation of the Depreciation Rate of Physical and R&D Capital in the U.S. Total Manufacturing Sector," Economic Inquiry, Western Economic Association International, vol. 34(1), pages 43-56, January.
    8. Peters, Bettina & Lööf, Hans & Janz, Norbert, 2003. "Firm Level Innovation and Productivity: Is there a Common Story Across Countries?," ZEW Discussion Papers 03-26, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
    9. Francesco Daveri, 2002. "The New Economy in Europe, 1992--2001," Oxford Review of Economic Policy, Oxford University Press, vol. 18(3), pages 345-362.
    10. Tor Jakob Klette & Samuel Kortum, 2004. "Innovating Firms and Aggregate Innovation," Journal of Political Economy, University of Chicago Press, vol. 112(5), pages 986-1018, October.
    11. Conte, Andrea & Vivarelli, Marco, 2005. "One or Many Knowledge Production Functions? Mapping Innovative Activity Using Microdata," IZA Discussion Papers 1878, Institute for the Study of Labor (IZA).
    12. Jacques Mairesse & Mohamed Sassenou, 1991. "R&D Productivity: A Survey of Econometric Studies at the Firm Level," NBER Working Papers 3666, National Bureau of Economic Research, Inc.
    13. Azele Mathieu & Bruno van Pottelsberghe de la Potterie, 2010. "A Note on the Drivers of R&D Intensity," Research in World Economy, Research in World Economy, Sciedu Press, vol. 1(1), pages 56-65, November.
    14. Bruno Crepon & Emmanuel Duguet & Jacques Mairesse, 1998. "Research, Innovation And Productivity: An Econometric Analysis At The Firm Level," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 7(2), pages 115-158.
    15. Wakelin, Katharine, 2001. "Productivity growth and R&D expenditure in UK manufacturing firms," Research Policy, Elsevier, vol. 30(7), pages 1079-1090, August.
    16. Hall, Bronwyn H. & Mairesse, Jacques, 1995. "Exploring the relationship between R&D and productivity in French manufacturing firms," Journal of Econometrics, Elsevier, vol. 65(1), pages 263-293, January.
    17. Mariacristina Piva & Marco Vivarelli, 2004. "The determinants of the skill bias in Italy: R&D, organisation or globalisation?," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 13(4), pages 329-347.
    18. Zvi Griliches, 1998. "Issues in Assessing the Contribution of Research and Development to Productivity Growth," NBER Chapters,in: R&D and Productivity: The Econometric Evidence, pages 17-45 National Bureau of Economic Research, Inc.
    19. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    20. Kevin J. Stiroh, 2002. "Information Technology and the U.S. Productivity Revival: What Do the Industry Data Say?," American Economic Review, American Economic Association, vol. 92(5), pages 1559-1576, December.
    21. Olivier Blanchard, 2004. "The Economic Future of Europe," Journal of Economic Perspectives, American Economic Association, vol. 18(4), pages 3-26, Fall.
    22. Arellano, M, 1987. "Computing Robust Standard Errors for Within-Groups Estimators," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 49(4), pages 431-434, November.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    R&D cooperation; percolation theory; knowledge diffusion; networks;

    JEL classification:

    • A10 - General Economics and Teaching - - General Economics - - - General
    • B10 - Schools of Economic Thought and Methodology - - History of Economic Thought through 1925 - - - General
    • B21 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Microeconomics
    • B25 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Historical; Institutional; Evolutionary; Austrian; Stockholm School
    • B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology
    • B52 - Schools of Economic Thought and Methodology - - Current Heterodox Approaches - - - Historical; Institutional; Evolutionary
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • I10 - Health, Education, and Welfare - - Health - - - General
    • O10 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - General
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

    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:jrp:jrpwrp:2009-039. 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: (Markus Pasche). General contact details of provider: http://www.jenecon.de .

    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.

    We have no references for this item. You can help adding them by using 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.