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Technological interdependencies predict innovation dynamics

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  • Anton Pichler
  • Franc{c}ois Lafond
  • J. Doyne Farmer

Abstract

We propose a simple model where the innovation rate of a technological domain depends on the innovation rate of the technological domains it relies on. Using data on US patents from 1836 to 2017, we make out-of-sample predictions and find that the predictability of innovation rates can be boosted substantially when network effects are taken into account. In the case where a technology$'$s neighborhood future innovation rates are known, the average predictability gain is 28$\%$ compared to simpler time series model which do not incorporate network effects. Even when nothing is known about the future, we find positive average predictability gains of 20$\%$. The results have important policy implications, suggesting that the effective support of a given technology must take into account the technological ecosystem surrounding the targeted technology.

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  • Anton Pichler & Franc{c}ois Lafond & J. Doyne Farmer, 2020. "Technological interdependencies predict innovation dynamics," Papers 2003.00580, arXiv.org.
  • Handle: RePEc:arx:papers:2003.00580
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    References listed on IDEAS

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    1. Jeff Alstott & Giorgio Triulzi & Bowen Yan & Jianxi Luo, 2017. "Mapping technology space by normalizing patent networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(1), pages 443-479, January.
    2. Farmer, J. Doyne & Lafond, François, 2016. "How predictable is technological progress?," Research Policy, Elsevier, vol. 45(3), pages 647-665.
    3. Lorenzo Napolitano & Evangelos Evangelou & Emanuele Pugliese & Paolo Zeppini & Graham Room, 2017. "Technology networks: the autocatalytic origins of innovation," Papers 1708.03511, arXiv.org, revised Apr 2018.
    4. François Lafond & Daniel Kim, 2019. "Long-run dynamics of the U.S. patent classification system," Journal of Evolutionary Economics, Springer, vol. 29(2), pages 631-664, April.
    5. Josef Taalbi, 2018. "Evolution and structure of technological systems - An innovation output network," Papers 1811.06772, arXiv.org.
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    Cited by:

    1. Hötte, Kerstin & Pichler, Anton & Lafond, François, 2021. "The rise of science in low-carbon energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).

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