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Self-organization of knowledge economies

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  • Lafond, Francois

    ()
    (UNU-MERIT / MGSoG)

Abstract

Suppose that homogenous agents fully consume their time to invent new ideas and learn ideas from their friends. If the social network is complete and agents pick friends and ideas of friends uniformly at random, the distribution of ideas’ popularity is an extension of the Yule-Simon distribution. It has a power-law tail, with an upward or downward curvature. For infinite population it converges to the Yule-Simon distribution. The power law is steeper when innovation is high. Diffusion follows S-shaped curves.

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Bibliographic Info

Paper provided by United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT) in its series MERIT Working Papers with number 040.

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Date of creation: 2013
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Handle: RePEc:unm:unumer:2013040

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Keywords: innovation; diffusion; two-mode networks; cumulative advantage; quadratic attachment kernel; power law; Yule-Simon distribution; generalized hypergeometric distribution;

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  1. Geroski, Paul A, 1999. "Models of Technology Diffusion," CEPR Discussion Papers, C.E.P.R. Discussion Papers 2146, C.E.P.R. Discussion Papers.
  2. Sen, Amartya, 2001. "Development as Freedom," OUP Catalogue, Oxford University Press, Oxford University Press, number 9780192893307, October.
  3. Robert E. Lucas Jr. & Benjamin Moll, 2014. "Knowledge Growth and the Allocation of Time," Journal of Political Economy, University of Chicago Press, University of Chicago Press, vol. 122(1), pages 1 - 51.
  4. Cowan,Robin & Jonard,Nicolas, 1999. "Network Structure and the Diffusion of Knowledge," Research Memorandum, Maastricht University, Maastricht Economic Research Institute on Innovation and Technology (MERIT) 026, Maastricht University, Maastricht Economic Research Institute on Innovation and Technology (MERIT).
  5. Paul M Romer, 1999. "Endogenous Technological Change," Levine's Working Paper Archive 2135, David K. Levine.
  6. H. Peyton Young, 2009. "Innovation Diffusion in Heterogeneous Populations: Contagion, Social Influence, and Social Learning," American Economic Review, American Economic Association, American Economic Association, vol. 99(5), pages 1899-1924, December.
  7. Matthew O. Jackson & Brian W. Rogers, 2007. "Meeting Strangers and Friends of Friends: How Random Are Social Networks?," American Economic Review, American Economic Association, American Economic Association, vol. 97(3), pages 890-915, June.
  8. Michael Koenig & Jan Lorenz & Fabrizio Zilibotti, 2012. "Innovation vs. Imitation and the Evolution of Productivity Distributions," Discussion Papers, Stanford Institute for Economic Policy Research 11-008, Stanford Institute for Economic Policy Research.
  9. Jovanovic, Boyan & Rob, Rafael, 1989. "The Growth and Diffusion of Knowledge," Review of Economic Studies, Wiley Blackwell, Wiley Blackwell, vol. 56(4), pages 569-82, October.
  10. Lafond, Francois, 2012. "Learning and the structure of citation networks," MERIT Working Papers, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT) 071, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
  11. Lucas, Robert Jr., 1988. "On the mechanics of economic development," Journal of Monetary Economics, Elsevier, Elsevier, vol. 22(1), pages 3-42, July.
  12. Cohen, Wesley M & Levinthal, Daniel A, 1989. "Innovation and Learning: The Two Faces of R&D," Economic Journal, Royal Economic Society, Royal Economic Society, vol. 99(397), pages 569-96, September.
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