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Unfolding the innovation system for the development of countries: co-evolution of Science, Technology and Production

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  • Emanuele Pugliese
  • Giulio Cimini
  • Aurelio Patelli
  • Andrea Zaccaria
  • Luciano Pietronero
  • Andrea Gabrielli

Abstract

We show that the space in which scientific, technological and economic developments interplay with each other can be mathematically shaped using pioneering multilayer network and complexity techniques. We build the tri-layered network of human activities (scientific production, patenting, and industrial production) and study the interactions among them, also taking into account the possible time delays. Within this construction we can identify which capabilities and prerequisites are needed to be competitive in a given activity, and even measure how much time is needed to transform, for instance, the technological know-how into economic wealth and scientific innovation, being able to make predictions with a very long time horizon. Quite unexpectedly, we find empirical evidence that the naive knowledge flow from science, to patents, to products is not supported by data, being instead technology the best predictor for industrial and scientific production for the next decades.

Suggested Citation

  • Emanuele Pugliese & Giulio Cimini & Aurelio Patelli & Andrea Zaccaria & Luciano Pietronero & Andrea Gabrielli, 2017. "Unfolding the innovation system for the development of countries: co-evolution of Science, Technology and Production," Papers 1707.05146, arXiv.org, revised Dec 2017.
    • Handle: RePEc:arx:papers:1707.05146
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    Cited by:

    1. 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.
    2. Dario Mazzilli & Manuel Sebastian Mariani & Flaviano Morone & Aurelio Patelli, 2022. "Equivalence between the Fitness-Complexity and the Sinkhorn-Knopp algorithms," Papers 2212.12356, arXiv.org, revised Mar 2024.
    3. Aurelio Patelli & Andrea Zaccaria & Luciano Pietronero, 2021. "Universal Database for Economic Complexity," Papers 2110.00302, arXiv.org.
    4. Tiziano Squartini & Guido Caldarelli & Giulio Cimini & Andrea Gabrielli & Diego Garlaschelli, 2018. "Reconstruction methods for networks: the case of economic and financial systems," Papers 1806.06941, arXiv.org.
    5. 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.
    6. Luciano Pietronero & Matthieu Cristelli & Andrea Gabrielli & Dario Mazzilli & Emanuele Pugliese & Andrea Tacchella & Andrea Zaccaria, 2017. "Economic Complexity: "Buttarla in caciara" vs a constructive approach," Papers 1709.05272, arXiv.org.
    7. 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.
    8. Patelli, Aurelio & Napolitano, Lorenzo & Cimini, Giulio & Gabrielli, Andrea, 2023. "Geography of science: Competitiveness and inequality," Journal of Informetrics, Elsevier, vol. 17(1).
    9. Aurelio Patelli & Lorenzo Napolitano & Giulio Cimini & Andrea Gabrielli, 2021. "Geography of Science: Competitiveness and Inequality," Papers 2110.01615, arXiv.org.
    10. Andrea Palmucci & Hao Liao & Andrea Napoletano & Andrea Zaccaria, 2020. "Where is your field going? A machine learning approach to study the relative motion of the domains of physics," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-16, June.
    11. Donis, Silvia & Gómez, Jaime & Salazar, Idana, 2023. "Economic complexity, property rights and the judicial system as drivers of eco-innovations: An analysis of OECD countries," Technovation, Elsevier, vol. 128(C).
    12. Mika J. Straka & Guido Caldarelli & Tiziano Squartini & Fabio Saracco, 2017. "From Ecology to Finance (and Back?): Recent Advancements in the Analysis of Bipartite Networks," Papers 1710.10143, arXiv.org.

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