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Technology networks: the autocatalytic origins of innovation

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  • Lorenzo Napolitano
  • Evangelos Evangelou
  • Emanuele Pugliese
  • Paolo Zeppini
  • 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.

Suggested Citation

  • 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.
  • Handle: RePEc:arx:papers:1708.03511
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    References listed on IDEAS

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    1. 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.
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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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