Third-Party Data Providers Ruin Simple Mechanisms

Yang Cai, Federico Echenique, Hu Fu, Katrina Ligett, Adam Wierman, Juba Ziani

Research output: Contribution to journalArticlepeer-review


Motivated by the growing prominence of third-party data providers in online marketplaces, this paper studies the impact of the presence of third-party data providers on mechanism design. When no data provider is present, it has been shown that simple mechanisms are "good enough'' -- they can achieve a constant fraction of the revenue of optimal mechanisms. The results in this paper demonstrate that this is no longer true in the presence of a third-party data provider who can provide the bidder with a signal that is correlated with the item type. Specifically, even with a single seller, a single bidder, and a single item of uncertain type for sale, the strategies of pricing each item-type separately (the analog of item pricing for multi-item auctions) and bundling all item-types under a single price (the analog of grand bundling) can both simultaneously be a logarithmic factor worse than the optimal revenue. Further, in the presence of a data provider, item-type partitioning mechanisms---a more general class of mechanisms which divide item-types into disjoint groups and offer prices for each group---still cannot achieve within a $łog łog$ factor of the optimal revenue. Thus, our results highlight that the presence of a data-provider forces the use of more complicated mechanisms in order to achieve a constant fraction of the optimal revenue.
Original languageEnglish
Article number1
Pages (from-to)12:1-12:31
Number of pages31
JournalProceedings of the ACM on Measurement and Analysis of Computing Systems
Issue number1
StatePublished - 5 Jun 2020


  • Algorithmic mechanism design


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