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Technology networks: the autocatalytic origins of innovation

Author

Listed:
  • Lorenzo Napolitano
  • Evangelos Evangelou
  • Emanuele Pugliese
  • Paolo Zeppini

    (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis (... - 2019) - COMUE UCA - COMUE Université Côte d'Azur (2015 - 2019) - CNRS - Centre National de la Recherche Scientifique - UCA - Université Côte d'Azur)

  • Graham Room

Abstract

We analyse the autocatalytic structure of technological networks and evaluate its significance for the dynamics of innovation patenting. To this aim, we define a directed network of technological fields based on the International Patents Classification, in which a source node is connected to a receiver node via a link if patenting activity in the source field anticipates patents in the receiver field in the same region more frequently than we would expect at random. We show that the evolution of the technology network is compatible with the presence of a growing autocatalytic structure, i.e. a portion of the network in which technological fields mutually benefit from being connected to one another. We further show that technological fields in the core of the autocatalytic set display greater fitness, i.e. they tend to appear in a greater number of patents, thus suggesting the presence of positive spillovers as well as positive reinforcement. Finally, we observe that core shifts take place whereby different groups of technology fields alternate within the autocatalytic structure; this points to the importance of recombinant innovation taking place between close as well as distant fields of the hierarchical classification of technological fields.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Lorenzo Napolitano & Evangelos Evangelou & Emanuele Pugliese & Paolo Zeppini & Graham Room, 2018. "Technology networks: the autocatalytic origins of innovation," Post-Print halshs-01952447, HAL.
  • Handle: RePEc:hal:journl:halshs-01952447
    Note: View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-01952447
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    References listed on IDEAS

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    1. Andrea Zaccaria & Matthieu Cristelli & Andrea Tacchella & Luciano Pietronero, 2014. "How the Taxonomy of Products Drives the Economic Development of Countries," Papers 1408.2138, arXiv.org.
    2. Petros Gkotsis & Antonio Vezzani, 2016. "Technological diffusion as a recombinant process," JRC Working Papers on Corporate R&D and Innovation 2016-07, Joint Research Centre (Seville site).
    3. Carolina Castaldi & Koen Frenken & Bart Los, 2015. "Related Variety, Unrelated Variety and Technological Breakthroughs: An analysis of US State-Level Patenting," Regional Studies, Taylor & Francis Journals, vol. 49(5), pages 767-781, May.
    4. Koen Frenken & Luis R. Izquierdo & Paolo Zeppini, 2012. "Recombinant Innovation and Endogenous Transitions," Working Papers 12-01, Eindhoven Center for Innovation Studies, revised Jan 2012.
    5. Letterie, Wilko & Hagedoorn, John & van Kranenburg, Hans & Palm, Franz, 2008. "Information gathering through alliances," Journal of Economic Behavior & Organization, Elsevier, vol. 66(2), pages 176-194, May.
    6. 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.
    7. Péter Érdi & Kinga Makovi & Zoltán Somogyvári & Katherine Strandburg & Jan Tobochnik & Péter Volf & László Zalányi, 2013. "Prediction of emerging technologies based on analysis of the US patent citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(1), pages 225-242, April.
    8. Fabio Saracco & Riccardo Di Clemente & Andrea Gabrielli & Tiziano Squartini, 2015. "Randomizing bipartite networks: the case of the World Trade Web," Papers 1503.05098, arXiv.org, revised Jun 2015.
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    Cited by:

    1. Ott, Ingrid & Savin, Ivan & Konop, Chris, 2021. "Tracing the evolution of service robotics: Insights from a topic modeling approach," Kiel Working Papers 2180, Kiel Institute for the World Economy (IfW).
    2. Emanuele Pugliese & Lorenzo Napolitano & Matteo Chinazzi & Guido Chiarotti, 2019. "The Emergence of Innovation Complexity at Different Geographical and Technological Scales," Papers 1909.05604, arXiv.org.
    3. Lorenzo Napolitano & Angelica Sbardella & Davide Consoli & Nicolo Barbieri & Francois Perruchas, 2020. "Green Innovation and Income Inequality: A Complex System Analysis," SPRU Working Paper Series 2020-11, SPRU - Science Policy Research Unit, University of Sussex Business School.
    4. Anton Pichler & Franc{c}ois Lafond & J. Doyne Farmer, 2020. "Technological interdependencies predict innovation dynamics," Papers 2003.00580, arXiv.org.

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