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Third-Party Data Providers Ruin Simple Mechanisms.

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

This paper studies the revenue of simple mechanisms in settings where athird-party data provider is present. When no data provider is present, it isknown that simple mechanisms achieve a constant fraction of the revenue ofoptimal mechanisms. The results in this paper demonstrate that this is nolonger true in the presence of a third party data provider who can provide thebidder with a signal that is correlated with the item type. Specifically, weshow that even with a single seller, a single bidder, and a single item ofuncertain type for sale, pricing each item-type separately (the analog of itempricing for multi-item auctions) and bundling all item-types under a singleprice (the analog of grand bundling) can both simultaneously be a logarithmicfactor worse than the optimal revenue. Further, in the presence of a dataprovider, item-type partitioning mechanisms---a more general class ofmechanisms which divide item-types into disjoint groups and offer prices foreach group---still cannot achieve within a $\log \log$ factor of the optimalrevenue.

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