Data sharing or algorithm sharing?
Bruno Carballa-Smichowski (bruno.carballa-smichowski@ec.europa.eu),
Yassine Lefouili,
Andrea Mantovani and
Carlo Reggiani
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Bruno Carballa-Smichowski: European Commission’s Joint Research Centre, Seville, Spain
Authors registered in the RePEc Author Service: Bruno Carballa Smichowski
No 23-08, Working Papers from NET Institute
Abstract:
Data combination and analytics can generate valuable insights for firms and society as a whole. Multiple firms can do so by means of new technologies that bring the algorithm to the data (“algorithm sharing”) or, more conventionally, by sharing the data (“data sharing”). Algorithm-sharing technologies are gaining traction because of their advantages in terms of privacy, security, and environmental impact. We present a model that allows us to study the economic incentives generated by these technologies for both firms and a platform facilitating data combination. We find that, first, the platform chooses data sharing unless algorithm sharing’s analytics are sufficiently superior to those associated to data sharing. Second, we identify the properties of the analytics benefit function that ensure that algorithm sharing results in a higher total data contribution. Third, we highlight scenarios in which, in presence of data externalities, there can be a mismatch between the choice of the platform and the preference of a social planner
Keywords: data sharing; algorithm sharing; data platforms; federated learning; data externalities. (search for similar items in EconPapers)
JEL-codes: D43 K21 L11 L13 L41 L86 M21 M31 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2023-09
New Economics Papers: this item is included in nep-big and nep-reg
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Persistent link: https://EconPapers.repec.org/RePEc:net:wpaper:2308
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