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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 (1965 - 2019) - CNRS - Centre National de la Recherche Scientifique - UniCA - 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
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    2. Giambattista Albora & Matteo Straccamore & Andrea Zaccaria, 2024. "Machine learning-based similarity measure to forecast M&A from patent data," Papers 2404.07179, arXiv.org.
    3. Emanuele Pugliese & Lorenzo Napolitano & Andrea Zaccaria & Luciano Pietronero, 2019. "Coherent diversification in corporate technological portfolios," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-22, October.
    4. Hafele, Jakob & Le Lannou, Laure-Alizée & Rochowicz, Nils & Kuhls, Sonia & Gräbner-Radkowitsch, Claudius, 2023. "Securing future-fit jobs in the green transformation: A policy framework for industrial policy," ZOE Discussion Papers 10, ZOE. institute for future-fit economies, Bonn.
    5. Francesco de Cunzo & Alberto Petri & Andrea Zaccaria & Angelica Sbardella, 2022. "The trickle down from environmental innovation to productive complexity," Papers 2206.07537, arXiv.org.
    6. Napolitano, Lorenzo & Sbardella, Angelica & Consoli, Davide & Barbieri, Nicolò & Perruchas, François, 2022. "Green innovation and income inequality: A complex system analysis," Structural Change and Economic Dynamics, Elsevier, vol. 63(C), pages 224-240.
    7. Anton Pichler & Franc{c}ois Lafond & J. Doyne Farmer, 2020. "Technological interdependencies predict innovation dynamics," Papers 2003.00580, arXiv.org.
    8. 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.
    9. Arnaud Persenda & Alexandre Ruiz, 2023. "Autocatalytic Networks and the Green Economy," GREDEG Working Papers 2023-16, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    10. Sabrina Aufiero & Giordano De Marzo & Angelica Sbardella & Andrea Zaccaria, 2023. "Mapping job complexity and skills into wages," Papers 2304.05251, arXiv.org.
    11. Angelica Sbardella & Andrea Zaccaria & Luciano Pietronero & Pasquale Scaramozzino, 2021. "Behind the Italian Regional Divide: An Economic Fitness and Complexity Perspective," LEM Papers Series 2021/30, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.

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