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Increasing Returns, Learning-By-Doing And Neural Networks

Author

Listed:
  • Nicola De Liso
  • Carmine Lubritto
  • Giovanni Filatrella

Abstract

In this paper we draw an analogy between the process of learning-bydoing and the learning process which develops in a neural network context. The bridging tool we refer to is a dynamic production function whose only variable input is labour. By concentrating on the 'neural network production function' we show that the learning process can lead to increasing returns. The simulations show that when learning is characterized by an upper limit. returns are increasing for some time, while in the long run they go back to the level where they are constant.

Suggested Citation

  • Nicola De Liso & Carmine Lubritto & Giovanni Filatrella, 2001. "Increasing Returns, Learning-By-Doing And Neural Networks," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 10(4), pages 325-337.
    • Handle: RePEc:taf:ecinnt:v:10:y:2001:i:4:p:325-337
    DOI: 10.1080/10438590100000013
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    References listed on IDEAS

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    1. Romer, Paul M, 1986. "Increasing Returns and Long-run Growth," Journal of Political Economy, University of Chicago Press, vol. 94(5), pages 1002-1037, October.
    2. Fellner, William, 1969. "Specific interpretations of learning by doing," Journal of Economic Theory, Elsevier, vol. 1(2), pages 119-140, August.
    3. De Liso, Nicola & Filatrella, Giovanni & Weaver, Nick, 2001. "On endogenous growth and increasing returns: modeling learning-by-doing and the division of labor," Journal of Economic Behavior & Organization, Elsevier, vol. 46(1), pages 39-55, September.
    4. Young, Alwyn, 1993. "Invention and Bounded Learning by Doing," Journal of Political Economy, University of Chicago Press, vol. 101(3), pages 443-472, June.
    5. K. J. Arrow, 1971. "The Economic Implications of Learning by Doing," Palgrave Macmillan Books, in: F. H. Hahn (ed.), Readings in the Theory of Growth, chapter 11, pages 131-149, Palgrave Macmillan.
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