Artificial Intelligence & Data Obfuscation: Algorithmic Competition in Digital Ad Auctions

Francesco Decarolis, Gabriele Rovigatti, Michele Rovigatti and Ksenia Shakhgildyan

No 18009, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: Artificial Intelligence Algorithms differ in their capabilities depending on the type of available data. We explore how this dimension informs two key design features: memory and updating (or learning) rule. We apply this insight to the case of online search auctions, where platforms control the type of data given to advertisers about their rivals’ bids. Simulated experiments with asymmetric bidders reveal that, when less detailed information is available to train the algorithms, the auctioneer revenues improve substantially. This might explain why hosting platforms have recently reduced the information disclosed, an industry trend known as data obfuscation. Finally, we explain how our findings are linked to dynamic strategies and to the possibility of calculating counterfactuals, as well as to the responsiveness of the algorithms to the actions of other players.

Keywords: Asymmetric information; Auctions; Artificial intelligence; Data governance; Digital advertising; Digital platforms; Competition (search for similar items in EconPapers)
JEL-codes: D21 D44 L22 L40 L50 (search for similar items in EconPapers)
Date: 2023-03
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