Social Learning and Innovation Cycles (revision of DP#1516, The Dynamics of Innovation)
AbstractWe study social learning and innovation in an overlapping generations model, emphasizing the trade-off between marginal innovation (combining existing technologies) and radical innovation (breaking new ground). We characterize both short-term and long-term dynamics of innovation, and the intergenerational accumulation of knowledge. Innovation cycles emerge endogenously, but the number of cycles is finite almost surely, and radical innovation terminates infinite time. We identify a negative relationship between past successes and the magnitude of radical innovation, combining insights from the multi-armed bandit literature with a spatial representation of innovation. Past successes reduce the incremental value of experimentation, and result in less ambitious innovation. In our framework, patents promote radical innovation through two channels: by increasing the expected benefit of radical innovation and by increasing the cost of marginal innovation. Our analysis suggests that sustaining radical innovation in the long-run requires external intervention.
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Bibliographic InfoPaper provided by Northwestern University, Center for Mathematical Studies in Economics and Management Science in its series Discussion Papers with number 1546.
Date of creation: 06 Feb 2012
Date of revision:
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Postal: Center for Mathematical Studies in Economics and Management Science, Northwestern University, 580 Jacobs Center, 2001 Sheridan Road, Evanston, IL 60208-2014
Web page: http://www.kellogg.northwestern.edu/research/math/
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This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-02-20 (All new papers)
- NEP-INO-2012-02-20 (Innovation)
- NEP-KNM-2012-02-20 (Knowledge Management & Knowledge Economy)
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- Daron Acemoglu & Gino Gancia & Fabrizio Zilibotti, 2010.
"Competing engines of growth: innovation and standardization,"
IEW - Working Papers
483, Institute for Empirical Research in Economics - University of Zurich.
- Acemoglu, Daron & Gancia, Gino & Zilibotti, Fabrizio, 2012. "Competing engines of growth: Innovation and standardization," Journal of Economic Theory, Elsevier, vol. 147(2), pages 570-601.e3.
- Daron Acemoglu & Gino Gancia & Fabrizio Zilibotti, 2010. "Competing Engines of Growth: Innovation and Standardization," NBER Working Papers 15958, National Bureau of Economic Research, Inc.
- Daron Acemoglu & Gino Gancia & Fabrizio Zilibotti, 2010. "Competing Engines of Growth: Innovation and Standardization," Levine's Working Paper Archive 661465000000000243, David K. Levine.
- Daron Acemoglu & Gino Gancia & Fabrizio Zilibotti, 2010. "Competing engines of growth: Innovation and standardization," Economics Working Papers 1358, Department of Economics and Business, Universitat Pompeu Fabra, revised Aug 2010.
- Aghion, P. & Bolton, P. & Harris, C. & Jullien, B., 1990.
"Optimal Learning By Experimentation,"
DELTA Working Papers
90-10, DELTA (Ecole normale supérieure).
- McLennan, Andrew, 1984. "Price dispersion and incomplete learning in the long run," Journal of Economic Dynamics and Control, Elsevier, vol. 7(3), pages 331-347, September.
- Steven Callander, 2011. "Searching and Learning by Trial and Error," American Economic Review, American Economic Association, vol. 101(6), pages 2277-2308, October.
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