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

Konferenzbeitrag

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: 31st European Conference of the International Telecommunications Society (ITS): "Reining in Digital Platforms? Challenging monopolies, promoting competition and developing regulatory regimes", Gothenburg, Sweden, 20th - 21st June 2022

Classification and Topics

• Classification: Wirtschaft
• Subject: Data-driven network effects ; algorithmic learning ; regulation ; data sharing ; data siloing

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Krämer, Jan ; Shekhar, Shiva ; Hofmann, Janina
• Event: Veröffentlichung
• (who): International Telecommunications Society (ITS)
• (where): Calgary
• (when): 2022

Further information

• Handle: http://hdl.handle.net/10419/265645
• Last update: 10.03.2025, 11:44 AM CET