IDEAS home Printed from
   My bibliography  Save this article

The AI techno-economic complex System: Worldwide landscape, thematic subdomains and technological collaborations


  • Righi, Riccardo
  • Samoili, Sofia
  • López Cobo, Montserrat
  • Vázquez-Prada Baillet, Miguel
  • Cardona, Melisande
  • De Prato, Giuditta


Artificial intelligence (AI) is playing a major role in the new paradigm shift occurring across the technological landscape. After a series of alternate seasons starting in the 60s, AI is now experiencing a new spring. Nevertheless, although it is spreading throughout our economies and societies in multiple ways, the absence of standardised classifications prevents us from obtaining a measure of its pervasiveness. In addition, AI cannot be identified as part of a specific sector, but rather as a transversal technology because the fields in which it is applied do not have precise boundaries. In this work, we address the need for a deeper understanding of this complex phenomenon by investigating economic agents’ involvement in industrial activities aimed to supply AI-related goods and services, and AI-related R&D processes in the form of patents and publications. In order to conduct this extensive analysis, we use a complex systems approach through the agent-artifact space model, which identifies the core dimensions that should be considered. Therefore, by considering the geographic location of the involved agents and their organisation types (i.e., firms, governmental institutions, and research institutes), we (i) provide an overview of the worldwide presence of agents, (ii) investigate the patterns in which AI technological subdomains subsist and scatter in different parts of the system, and (iii) reveal the size, composition, and topology of the AI R&D collaboration network. Based on a unique data collection of multiple micro-based data sources and supported by a methodological framework for the analysis of techno-economic segments (TES), we capture the state of AI in the worldwide landscape in the period 2009–2018. As expected, we find that major roles are played by the US, China, and the EU28. Nevertheless, by measuring the system, we unveil elements that provide new, crucial information to support more conscious discussions in the process of policy design and implementation.

Suggested Citation

  • Righi, Riccardo & Samoili, Sofia & López Cobo, Montserrat & Vázquez-Prada Baillet, Miguel & Cardona, Melisande & De Prato, Giuditta, 2020. "The AI techno-economic complex System: Worldwide landscape, thematic subdomains and technological collaborations," Telecommunications Policy, Elsevier, vol. 44(6).
  • Handle: RePEc:eee:telpol:v:44:y:2020:i:6:s0308596120300355
    DOI: 10.1016/j.telpol.2020.101943

    Download full text from publisher

    File URL:
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    1. Giorgio Fagiolo & Javier Reyes & Stefano Schiavo, 2010. "The evolution of the world trade web: a weighted-network analysis," Journal of Evolutionary Economics, Springer, vol. 20(4), pages 479-514, August.
    2. Etzkowitz, Henry & Leydesdorff, Loet, 2000. "The dynamics of innovation: from National Systems and "Mode 2" to a Triple Helix of university-industry-government relations," Research Policy, Elsevier, vol. 29(2), pages 109-123, February.
    3. Suominen, Arho & Toivanen, Hannes & Seppänen, Marko, 2017. "Firms' knowledge profiles: Mapping patent data with unsupervised learning," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 131-142.
    4. Abbasi, Alireza & Hossain, Liaquat & Leydesdorff, Loet, 2012. "Betweenness centrality as a driver of preferential attachment in the evolution of research collaboration networks," Journal of Informetrics, Elsevier, vol. 6(3), pages 403-412.
    5. Lee, Changyong & Kang, Bokyoung & Shin, Juneseuk, 2015. "Novelty-focused patent mapping for technology opportunity analysis," Technological Forecasting and Social Change, Elsevier, vol. 90(PB), pages 355-365.
    6. Venugopalan, Subhashini & Rai, Varun, 2015. "Topic based classification and pattern identification in patents," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 236-250.
    7. Kevin W. Boyack & Richard Klavans & Katy Börner, 2005. "Mapping the backbone of science," Scientometrics, Springer;Akadémiai Kiadó, vol. 64(3), pages 351-374, August.
    8. Verhoeven, Dennis & Bakker, Jurriën & Veugelers, Reinhilde, 2016. "Measuring technological novelty with patent-based indicators," Research Policy, Elsevier, vol. 45(3), pages 707-723.
    9. Gilsing, Victor & Nooteboom, Bart & Vanhaverbeke, Wim & Duysters, Geert & van den Oord, Ad, 2008. "Network embeddedness and the exploration of novel technologies: Technological distance, betweenness centrality and density," Research Policy, Elsevier, vol. 37(10), pages 1717-1731, December.
    10. Pierre-Alexandre Balland, 2012. "Proximity and the Evolution of Collaboration Networks: Evidence from Research and Development Projects within the Global Navigation Satellite System (GNSS) Industry," Regional Studies, Taylor & Francis Journals, vol. 46(6), pages 741-756, September.
    11. Freeman, C., 1991. "Networks of innovators: A synthesis of research issues," Research Policy, Elsevier, vol. 20(5), pages 499-514, October.
    12. Alessandro Annoni & Peter Benczur & Paolo Bertoldi & Blagoj Delipetrev & Giuditta De Prato & Claudio Feijoo & Enrique Fernandez Macias & Emilia Gomez Gutierrez & Maria Iglesias Portela & Henrik Junkle, 2018. "Artificial Intelligence: A European Perspective," JRC Working Papers JRC113826, Joint Research Centre (Seville site).
    13. Arho Suominen & Hannes Toivanen, 2016. "Map of science with topic modeling: Comparison of unsupervised learning and human-assigned subject classification," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(10), pages 2464-2476, October.
    14. Lee, Changyong & Kwon, Ohjin & Kim, Myeongjung & Kwon, Daeil, 2018. "Early identification of emerging technologies: A machine learning approach using multiple patent indicators," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 291-303.
    15. Mario Amendola & Jean-Luc Gaffard, 1988. "The innovative choice. An economic analysis of the dynamics of technology," Post-Print halshs-00420361, HAL.
    16. Dosi, Giovanni, 1993. "Technological paradigms and technological trajectories : A suggested interpretation of the determinants and directions of technical change," Research Policy, Elsevier, vol. 22(2), pages 102-103, April.
    17. Ding, Ying, 2011. "Community detection: Topological vs. topical," Journal of Informetrics, Elsevier, vol. 5(4), pages 498-514.
    18. Arthur, W. Brian, 2007. "The structure of invention," Research Policy, Elsevier, vol. 36(2), pages 274-287, March.
    19. David A. Lane, 2011. "Complexity and Innovation Dynamics," Chapters, in: Cristiano Antonelli (ed.), Handbook on the Economic Complexity of Technological Change, chapter 2, Edward Elgar Publishing.
    20. Lee, Won Sang & Han, Eun Jin & Sohn, So Young, 2015. "Predicting the pattern of technology convergence using big-data technology on large-scale triadic patents," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 317-329.
    21. Fagiolo, Giorgio & Reyes, Javier & Schiavo, Stefano, 2008. "On the topological properties of the world trade web: A weighted network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(15), pages 3868-3873.
    22. Jan M. Gerken & Martin G. Moehrle, 2012. "A new instrument for technology monitoring: novelty in patents measured by semantic patent analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 645-670, June.
    23. Zhengyin Hu & Shu Fang & Tian Liang, 2014. "Empirical study of constructing a knowledge organization system of patent documents using topic modeling," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 787-799, September.
    24. Ta-Shun Cho & Hsin-Yu Shih, 2011. "Patent citation network analysis of core and emerging technologies in Taiwan: 1997–2008," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(3), pages 795-811, December.
    25. Garlaschelli, Diego & Loffredo, Maria I., 2005. "Structure and evolution of the world trade network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 355(1), pages 138-144.
    26. Jason Owen-Smith & Walter W. Powell, 2004. "Knowledge Networks as Channels and Conduits: The Effects of Spillovers in the Boston Biotechnology Community," Organization Science, INFORMS, vol. 15(1), pages 5-21, February.
    27. Giuditta De Prato & Montserrat Lopez Cobo & Sofia Samoili & Riccardo Righi & Miguel Vazquez Prada Baillet & Melisande Cardona, 2019. "The AI Techno-Economic Segment Analysis," JRC Working Papers JRC118071, Joint Research Centre (Seville site).
    28. Breitzman, Anthony & Thomas, Patrick, 2015. "The Emerging Clusters Model: A tool for identifying emerging technologies across multiple patent systems," Research Policy, Elsevier, vol. 44(1), pages 195-205.
    29. D. Garlaschelli & M. I. Loffredo, 2005. "Structure and Evolution of the World Trade Network," Papers physics/0502066,, revised May 2005.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Riccardo Righi & Montserrat Lopez-Cobo & Georgios Alaveras & Sofia Samoili & Melisande Cardona & Miguel Vazquez-Prada Baillet & Lukasz Ziemba & Giuditta De-Prato, 2020. "Academic Offer of Advanced Digital Skills in 2019-20. International Comparison. Focus on Artificial Intelligence, High Performance Computing, Cybersecurity and Data Science," JRC Working Papers JRC121680, Joint Research Centre (Seville site).
    2. Matheus E. Leusin & Bjoern Jindra & Daniel S. Hain, 2021. "An evolutionary view on the emergence of Artificial Intelligence," Papers 2102.00233,

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sun, Bixuan & Kolesnikov, Sergey & Goldstein, Anna & Chan, Gabriel, 2021. "A dynamic approach for identifying technological breakthroughs with an application in solar photovoltaics," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    2. Samira Ranaei & Arho Suominen & Alan Porter & Stephen Carley, 2020. "Evaluating technological emergence using text analytics: two case technologies and three approaches," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 215-247, January.
    3. Lee, Changyong, 2021. "A review of data analytics in technological forecasting," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    4. Marcos Duenas & Rossana Mastrandrea & Matteo Barigozzi & Giorgio Fagiolo, 2017. "Spatio-Temporal Patterns of the International Merger and Acquisition Network," LEM Papers Series 2017/13, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    5. Rakas, Marija & Hain, Daniel S., 2019. "The state of innovation system research: What happens beneath the surface?," Research Policy, Elsevier, vol. 48(9), pages 1-1.
    6. Nobi, Ashadun & Lee, Tae Ho & Lee, Jae Woo, 2020. "Structure of trade flow networks for world commodities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    7. Subramanian, Annapoornima M. & Bo, Wang & Kah-Hin, Chai, 2018. "The role of knowledge base homogeneity in learning from strategic alliances," Research Policy, Elsevier, vol. 47(1), pages 158-168.
    8. Assaf Almog & Rhys Bird & Diego Garlaschelli, 2015. "Enhanced Gravity Model of trade: reconciling macroeconomic and network models," Papers 1506.00348,, revised Feb 2019.
    9. Yang, Yu & Poon, Jessie P.H. & Liu, Yi & Bagchi-Sen, Sharmistha, 2015. "Small and flat worlds: A complex network analysis of international trade in crude oil," Energy, Elsevier, vol. 93(P1), pages 534-543.
    10. Uijun Kwon & Youngjung Geum, 2020. "Identification of promising inventions considering the quality of knowledge accumulation: a machine learning approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1877-1897, December.
    11. Lee, Changyong & Kwon, Ohjin & Kim, Myeongjung & Kwon, Daeil, 2018. "Early identification of emerging technologies: A machine learning approach using multiple patent indicators," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 291-303.
    12. Joyez, Charlie, 2017. "On the topological structure of multinationals network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 578-588.
    13. Fan, Ying & Ren, Suting & Cai, Hongbo & Cui, Xuefeng, 2014. "The state's role and position in international trade: A complex network perspective," Economic Modelling, Elsevier, vol. 39(C), pages 71-81.
    14. Gautier M Krings & Jean-François Carpantier & Jean-Charles Delvenne, 2014. "Trade Integration and Trade Imbalances in the European Union: A Network Perspective," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-14, January.
    15. Marco Dueñas & Giorgio Fagiolo, 2013. "Modeling the International-Trade Network: a gravity approach," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(1), pages 155-178, April.
    16. Maxim Kotsemir & Alexander Abroskin & Dirk Meissner, 2013. "Innovation concepts and typology – an evolutionary discussion," HSE Working papers WP BRP 05/STI/2013, National Research University Higher School of Economics.
    17. Kaihuang Zhang & Qinglan Qian & Yijing Zhao, 2020. "Evolution of Guangzhou Biomedical Industry Innovation Network Structure and Its Proximity Mechanism," Sustainability, MDPI, Open Access Journal, vol. 12(6), pages 1-20, March.
    18. Massimo Riccaboni & Alessandro Rossi & Stefano Schiavo, 2013. "Global networks of trade and bits," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(1), pages 33-56, April.
    19. Massimo Riccaboni & Stefano Schiavo, 2009. "The Structure and Growth of International Trade," Documents de Travail de l'OFCE 2009-24, Observatoire Francais des Conjonctures Economiques (OFCE).
    20. João Amador & Sónia Cabral & Rossana Mastrandrea & Franco Ruzzenenti, 2018. "Who’s Who in Global Value Chains? A Weighted Network Approach," Open Economies Review, Springer, vol. 29(5), pages 1039-1059, November.


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:telpol:v:44:y:2020:i:6:s0308596120300355. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Nithya Sathishkumar). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.