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Regulating Algorithmic Learning in Digital Platform Ecosystems through Data Sharing and Data Siloing: Consequences for Innovation and Welfare

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  • Krämer, Jan
  • Shekhar, Shiva
  • Hofmann, Janina
Abstract
Algorithmic learning gives rise to a data-driven network effects, which allow a dominant platform to reinforce its dominant market position. Data-driven network effects can also spill over to related markets and thereby allow to leverage a dominant position. This has led policymakers to propose data siloing and mandated data sharing remedies for dominant data-driven platforms in order to keep digital markets open and contestable. While data siloing seeks to prevent the spillover of data-driven network effects generated by algorithmic learning to other markets, data sharing seeks to share this externality with rival firms. Using a game-theoretic model, we investigate the impacts of both types of regulation. Our results bear important policy implications, as we demonstrate that data siloing and data sharing are potentially harmful remedies, which can reduce the innovation incentives of the regulated platform, and can lead overall lower consumer surplus and total welfare.

Suggested Citation

  • Krämer, Jan & Shekhar, Shiva & Hofmann, Janina, 2022. "Regulating Algorithmic Learning in Digital Platform Ecosystems through Data Sharing and Data Siloing: Consequences for Innovation and Welfare," 31st European Regional ITS Conference, Gothenburg 2022: Reining in Digital Platforms? Challenging monopolies, promoting competition and developing regulatory regimes 265645, International Telecommunications Society (ITS).
    • Handle: RePEc:zbw:itse22:265645
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    File URL: https://www.econstor.eu/bitstream/10419/265645/1/Kraemer-et-al.pdf
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    References listed on IDEAS

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    1. Schaefer, Maximilian & Sapi, Geza & Lorincz, Szabolcs, 2018. "The effect of big data on recommendation quality: The example of internet search," DICE Discussion Papers 284, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    2. Katz, Michael L & Shapiro, Carl, 1985. "Network Externalities, Competition, and Compatibility," American Economic Review, American Economic Association, vol. 75(3), pages 424-440, June.
    3. Haftor, Darek M. & Costa Climent, Ricardo & Lundström, Jenny Eriksson, 2021. "How machine learning activates data network effects in business models: Theory advancement through an industrial case of promoting ecological sustainability," Journal of Business Research, Elsevier, vol. 131(C), pages 196-205.
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    Cited by:

    1. Hemant Bhargava & Antoine Dubus & David Ronayne & Shiva Shekhar, 2024. "The Strategic Value of Data Sharing in Interdependent Markets," CESifo Working Paper Series 10963, CESifo.
    2. Navarra, Federico & Pino, Flavio & Sandrini, Luca, 2024. "Mandated data-sharing in hybrid marketplaces," ZEW Discussion Papers 24-051, ZEW - Leibniz Centre for European Economic Research.

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    More about this item

    Keywords

    Data-driven network effects; algorithmic learning; regulation; data sharing; data siloing;
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