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Data Production and the coevolving AI trajectories: An attempted evolutionary model

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  • Andrea Borsato
  • Andre Lorentz

Abstract

This paper contributes to the understanding of the relationship between the nature of data and the Artificial Intelligence (AI) technological trajectories. We develop an agentbased model in which firms are data producers that compete on the markets for data and AI. The model is enriched by a public sector that fuels the purchase of data and trains the scientists that will populate firms as workforce. Through several simulation experiments we analyze the determinants of each market structure, the corresponding relationships with innovation attainments, the pattern followed by labour and data productivity, and the quality of data traded in the economy. More precisely, we question the established view in the literature on industrial organization according to which technological imperatives are enough to experience divergent industrial dynamics on both the markets for data and AI blueprints. Although technical change behooves if any industry pattern is to emerge, the actual unfolding is not the outcome of a specific technological trajectory, but the result of the interplay between technology-related factors and the availability of data-complementary inputs such as labour and AI capital, the market size, preferences and public policies.

Suggested Citation

  • Andrea Borsato & Andre Lorentz, 2022. "Data Production and the coevolving AI trajectories: An attempted evolutionary model," Working Papers of BETA 2022-09, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
  • Handle: RePEc:ulp:sbbeta:2022-09
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    1. Domini, Giacomo & Grazzi, Marco & Moschella, Daniele & Treibich, Tania, 2021. "Threats and opportunities in the digital era: Automation spikes and employment dynamics," Research Policy, Elsevier, vol. 50(7).
    2. Daron Acemoglu & Pascual Restrepo, 2019. "Automation and New Tasks: How Technology Displaces and Reinstates Labor," Journal of Economic Perspectives, American Economic Association, vol. 33(2), pages 3-30, Spring.
    3. Silverberg, Gerald & Verspagen, Bart, 1995. "An Evolutionary Model of Long Term Cyclical Variations of Catching Up and Falling Behind," Journal of Evolutionary Economics, Springer, vol. 5(3), pages 209-227, September.
    4. Dosi, Giovanni & Fagiolo, Giorgio & Roventini, Andrea, 2010. "Schumpeter meeting Keynes: A policy-friendly model of endogenous growth and business cycles," Journal of Economic Dynamics and Control, Elsevier, vol. 34(9), pages 1748-1767, September.
    5. Daron Acemoglu & Pascual Restrepo, 2017. "Secular Stagnation? The Effect of Aging on Economic Growth in the Age of Automation," American Economic Review, American Economic Association, vol. 107(5), pages 174-179, May.
    6. Daron Acemoglu & David Autor & Jonathon Hazell & Pascual Restrepo, 2020. "AI and Jobs: Evidence from Online Vacancies," NBER Working Papers 28257, National Bureau of Economic Research, Inc.
    7. Franco Malerba & Richard Nelson & Luigi Orsenigo & Sidney Winter, 2007. "Demand, innovation, and the dynamics of market structure: The role of experimental users and diverse preferences," Journal of Evolutionary Economics, Springer, vol. 17(4), pages 371-399, August.
    8. Philippe Aghion & Benjamin F. Jones & Charles I. Jones, 2018. "Artificial Intelligence and Economic Growth," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 237-282, National Bureau of Economic Research, Inc.
    9. J. Klinger & J. Mateos-Garcia & K. Stathoulopoulos, 2018. "Deep learning, deep change? Mapping the development of the Artificial Intelligence General Purpose Technology," Papers 1808.06355, arXiv.org.
    10. 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 Research Reports JRC113826, Joint Research Centre.
    11. Dosi, Giovanni & Nelson, Richard R., 2010. "Technical Change and Industrial Dynamics as Evolutionary Processes," Handbook of the Economics of Innovation, in: Bronwyn H. Hall & Nathan Rosenberg (ed.), Handbook of the Economics of Innovation, edition 1, volume 1, chapter 0, pages 51-127, Elsevier.
    12. Jason Furman & Robert Seamans, 2019. "AI and the Economy," Innovation Policy and the Economy, University of Chicago Press, vol. 19(1), pages 161-191.
    13. 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.
    14. Nelson, Richard R. & Sampat, Bhaven N., 2001. "Making sense of institutions as a factor shaping economic performance," Journal of Economic Behavior & Organization, Elsevier, vol. 44(1), pages 31-54, January.
    15. Franco Malerba & Richard Nelson & Luigi Orsenigo & Sidney G. Winter, 2001. "History-Friendly Models: an Overview of the Case of the Computer Industry," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 4(3), pages 1-6.
    16. Abhishek Nagaraj & Esther Shears & Mathijs de Vaan, 2020. "Improving data access democratizes and diversifies science," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(38), pages 23490-23498, September.
    17. Simone Vannuccini & Ekaterina Prytkova, 2021. "Artificial Intelligence’s New Clothes? From General Purpose Technology to Large Technical System," SPRU Working Paper Series 2021-02, SPRU - Science Policy Research Unit, University of Sussex Business School.
    18. Iain M. Cockburn & Rebecca Henderson & Scott Stern, 2018. "The Impact of Artificial Intelligence on Innovation: An Exploratory Analysis," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 115-146, National Bureau of Economic Research, Inc.
    19. Tommaso Ciarli & André Lorentz & Maria Savona & Marco Valente, 2010. "The Effect Of Consumption And Production Structure On Growth And Distribution. A Micro To Macro Model," Metroeconomica, Wiley Blackwell, vol. 61(1), pages 180-218, February.
    20. Florent Bordot, 2022. "Artificial Intelligence, Robots and Unemployment: Evidence from OECD Countries," Journal of Innovation Economics, De Boeck Université, vol. 0(1), pages 117-138.
    21. Maria Savona, 2019. "The Value of Data:Towards a Framework to Redistribute It," SPRU Working Paper Series 2019-21, SPRU - Science Policy Research Unit, University of Sussex Business School.
    22. Markus Spiekermann, 2019. "Data Marketplaces: Trends and Monetisation of Data Goods," Intereconomics: Review of European Economic Policy, Springer;ZBW - Leibniz Information Centre for Economics;Centre for European Policy Studies (CEPS), vol. 54(4), pages 208-216, July.
    23. Franco Malerba & Luigi Orsenigo, 2002. "Innovation and market structure in the dynamics of the pharmaceutical industry and biotechnology: towards a history-friendly model," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 11(4), pages 667-703, August.
    24. Malerba, Franco & Orsenigo, Luigi, 1995. "Schumpeterian Patterns of Innovation," Cambridge Journal of Economics, Cambridge Political Economy Society, vol. 19(1), pages 47-65, February.
    25. Zhen Yu & Zheng Liang & Peiyi Wu, 2021. "How data shape actor relations in artificial intelligence innovation systems: an empirical observation from China [Linking vertically related industries: entry by employee spinouts across industry ," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 30(1), pages 251-267.
    26. Dosi, Giovanni, 1988. "Sources, Procedures, and Microeconomic Effects of Innovation," Journal of Economic Literature, American Economic Association, vol. 26(3), pages 1120-1171, September.
    27. Giovanni Dosi, 2000. "Sources, Procedures, and Microeconomic Effects of Innovation," Chapters, in: Innovation, Organization and Economic Dynamics, chapter 2, pages 63-114, Edward Elgar Publishing.
    28. Malerba, Franco & Orsenigo, Luigi, 1996. "Schumpeterian patterns of innovation are technology-specific," Research Policy, Elsevier, vol. 25(3), pages 451-478, May.
    29. Dosi, Giovanni & Llerena, Patrick & Labini, Mauro Sylos, 2006. "The relationships between science, technologies and their industrial exploitation: An illustration through the myths and realities of the so-called `European Paradox'," Research Policy, Elsevier, vol. 35(10), pages 1450-1464, December.
    30. Iain M. Cockburn & Rebecca Henderson & Scott Stern, 2018. "The Impact of Artificial Intelligence on Innovation," NBER Working Papers 24449, National Bureau of Economic Research, Inc.
    31. Isabel Almudi & Francisco Fatas-Villafranca & Luis R. Izquierdo, 2012. "Innovation, catch-up, and leadership in science-based industries," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 21(2), pages 345-375, April.
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    More about this item

    Keywords

    Artificial Intelligence; Data Markets; Industrial Dynamics; Agent-based Models.;
    All these keywords.

    JEL classification:

    • L10 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - General
    • L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy

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