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A Percolation Model of Innovation in Complex Technology

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Author Info
Silverberg,Gerald
Verspagen,Bart (MERIT)

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Abstract

Innovations are known to arrive more highly clustered than if they werepurely random, and their rate of arrival has been increasing nearlyexponentially for several centuries. Their distribution of importance ishighly skewed and appears to obey a power law or lognormal distribution.Technological change has been seen by many scholars as followingtechnological trajectories and being subject to ‘paradigm’ shifts fromtime to time. To address these empirical observations, we introduce acomplex technology space based on percolation theory. This space issearched randomly in local neighborhoods of the current best-practicefrontier. Numerical simulations demonstrate that with increasing radiusof search, the probability of becoming deadlocked declines and the meanrate of innovation increases until a plateau is reached. Thedistribution of innovation sizes is highly skewed and heavy tailed. Forpercolation probabilities near the critical value, it seems to resemblean infinite-variance Pareto distribution in the tails. For highervalues, the lognormal appears to be preferred.

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Paper provided by Maastricht : MERIT, Maastricht Economic Research Institute on Innovation and Technology in its series Research Memoranda with number 032.

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Date of creation: 2002
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Handle: RePEc:dgr:umamer:2002032

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Keywords: microeconomics ;

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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.:
  1. Vega-Redondo, Fernando, 1994. "Technological Change and Path Dependence: A Co-evolutionary Model on a Directed Graph," Journal of Evolutionary Economics, Springer, vol. 4(1), pages 59-80, March.
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  2. F. M. Scherer & Dietmar Harhoff & J, rg Kukies, 2000. "Uncertainty and the size distribution of rewards from innovation," Journal of Evolutionary Economics, Springer, vol. 10(1), pages 175-200. [Downloadable!] (restricted)
  3. Scherer, F. M. & Harhoff, Dietmar, 2000. "Technology policy for a world of skew-distributed outcomes," Research Policy, Elsevier, vol. 29(4-5), pages 559-566, April. [Downloadable!] (restricted)
  4. William D. Nordhaus, 2001. "The Progress of Computing," Cowles Foundation Discussion Papers 1324, Cowles Foundation, Yale University. [Downloadable!]
  5. Silverberg, Gerald & Lehnert, Doris, 1993. "Long waves and 'evolutionary chaos' in a simple Schumpeterian model of embodied technical change," Structural Change and Economic Dynamics, Elsevier, vol. 4(1), pages 9-37, June. [Downloadable!] (restricted)
  6. B. Verspagen & G. Silverberg, 2000. "Breaking the waves: a poisson regression approach to schumpeterian clustering of basic innovations," ECIS Working Papers 00.16, Eindhoven Centre for Innovation Studies, Eindhoven University of Technology. [Downloadable!]
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  7. Dietmar Harhoff & Francis Narin & F. M. Scherer & Katrin Vopel, 1999. "Citation Frequency And The Value Of Patented Inventions," The Review of Economics and Statistics, MIT Press, vol. 81(3), pages 511-515, August. [Downloadable!] (restricted)
  8. F. M. Scherer, 1998. "The Size Distribution of Profits from Innovation," Annales d'Economie et de Statistique, ADRES, issue 49-50, pages 20, Janvier-J. [Downloadable!]
  9. Nelson, Richard R. & Winter, Sidney G., 1977. "In search of useful theory of innovation," Research Policy, Elsevier, vol. 6(1), pages 36-76, January. [Downloadable!] (restricted)
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  10. Manuel Trajtenberg, 1990. "A Penny for Your Quotes: Patent Citations and the Value of Innovations," RAND Journal of Economics, The RAND Corporation, vol. 21(1), pages 172-187, Spring. [Downloadable!] (restricted)
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  1. Silverberg,Gerald, 2003. "Long Waves: Conceptual, Empirical and Modelling Issues," Research Memoranda 015, Maastricht : MERIT, Maastricht Economic Research Institute on Innovation and Technology. [Downloadable!]
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