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

Andrea Borsato and André Lorentz

Working Papers of BETA from Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg

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.

Keywords: Artificial Intelligence; Data Markets; Industrial Dynamics; Agent-based Models. (search for similar items in EconPapers)
JEL-codes: L10 L60 O33 O38 (search for similar items in EconPapers)
Date: 2022
New Economics Papers: this item is included in nep-big, nep-cmp, nep-evo, nep-gro, nep-hme, nep-ind, nep-ino and nep-tid
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Journal Article: Data production and the coevolving AI trajectories: an attempted evolutionary model (2023)
Working Paper: Data production and the coevolving AI trajectories: an attempted evolutionary model (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:ulp:sbbeta:2022-09

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