Video URL

https://old.simons.berkeley.edu/talks/too-much-data-externalities-and-inefficiencies-data-markets

Too Much Data: Externalities and Inefficiencies in Data Markets

(2022). Too Much Data: Externalities and Inefficiencies in Data Markets. The Simons Institute for the Theory of Computing. https://old.simons.berkeley.edu/talks/too-much-data-externalities-and-inefficiencies-data-markets

Too Much Data: Externalities and Inefficiencies in Data Markets. The Simons Institute for the Theory of Computing, Dec. 01, 2022, https://old.simons.berkeley.edu/talks/too-much-data-externalities-and-inefficiencies-data-markets 

          @misc{ scivideos_23042,
            doi = {},
            url = {https://old.simons.berkeley.edu/talks/too-much-data-externalities-and-inefficiencies-data-markets},
            author = {},
            keywords = {},
            language = {en},
            title = {Too Much Data: Externalities and Inefficiencies in Data Markets},
            publisher = {The Simons Institute for the Theory of Computing},
            year = {2022},
            month = {dec},
            note = {23042 see, \url{https://scivideos.org/index.php/simons-institute/23042}}
          }
Azarakhsh Malekian (U. Toronto)
Source Repository Simons Institute
Subject

Abstract

When a user shares her data with online platforms, she reveals information about others in her social network. In such a setting, network externalities depress the price of data because once a user's information is leaked by others, she has less reason to protect her data and privacy. These depressed prices lead to excessive data sharing. We characterize conditions under which shutting down data markets improves welfare. Platform competition does not redress the problem of excessively low data prices and too much data sharing and may further reduce welfare. We propose a scheme based on mediated data sharing that improves efficiency.