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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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Listed:
  • 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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    References listed on IDEAS

    as
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    3. Katz, Michael L & Shapiro, Carl, 1985. "Network Externalities, Competition, and Compatibility," American Economic Review, American Economic Association, vol. 75(3), pages 424-440, June.
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    Keywords

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