How much can we influence the rate of innovation?
Science Advances 5, 1 (2019)
Comparison of the innovation rates for different distributions of product complexity.
Innovation is how organizations drive technological change, but the rate of innovation can vary considerably from one technological domain to another. To understand why some domains flourish more rapidly than others, we studied a model of innovation in which products are built out of components. We derived a conservation law for the average size of the product space as more components are acquired and tested our insights using historical data from language, gastronomy, mixed drinks, and technology. We find that the innovation rate is partly influenceable and partly predetermined, similar to how traits are partly set by nurture and partly set by nature. The predetermined aspect is fixed solely by the distribution of the complexity of products in each domain. Different distributions can produce markedly different innovation rates. This helps explain why some domains show faster innovation than others, despite similar efforts to accelerate them. Our insights also give a quantitative perspective on lean methodology, frugal innovation, and mechanisms to encourage tinkering.
T. Fink, M. Reeves
How well do experience curves predict technological progress? A method for making distributional forecasts
F. Lafond, A. Bailey, J. Bakker, D. Rebois, R. Zadourian, P. McSharry, D. Farmer
Technological Forecasting and Social Change
T. Fink, P. Ghemawat, M. Reeves
T. Fink, M. Reeves, R. Palma, R. Farr
M. Reeves, T. Fink, R. Palma, J. Harnoss
MIT Sloan Management Review
D. Farmer, F. Lafond
Journal of Economic Dynamics and Control
NU-MERIT Working Paper Series
A. Tacchella, M. Cristelli, G. Caldarelli, A. Gabrielli, L. Pietronero
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