Algorithmic Competition and Informational Advantage in Digital Markets: Evidence from Search Auctions

Francesco Decarolis, Tommaso Pellegrinetti, Gabriele Rovigatti, Michele Rovigatti and Ksenia Shakhgildyan

No 19983, CEPR Discussion Papers from Centre for Economic Policy Research

Abstract: This paper examines how proprietary algorithms used by dominant digital platforms create informational advantages in search auctions, reshaping market competition. Using experimental evidence and counterfactual simulations, we quantify the impact of algorithmic bidding on auction outcomes and competitive dynamics. Our findings reveal how platforms can leverage superior information to significantly improve their revenues, distorting competition and creating welfare losses for independent advertisers. We also show why platforms prefer selling a bidding algorithm service over directly selling data. These results highlight the need for greater scrutiny of algorithmic decision-making in platform markets, offering new insights for competition policy in digital economies.

Keywords: Collusion (search for similar items in EconPapers)
JEL-codes: C73 D18 D44 D82 D83 (search for similar items in EconPapers)
Date: 2025-02
References: Add references at CitEc
Citations:

Downloads: (external link)
https://cepr.org/publications/DP19983 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cpr:ceprdp:19983

Ordering information: This working paper can be ordered from
https://cepr.org/publications/DP19983

Access Statistics for this paper

More papers in CEPR Discussion Papers from Centre for Economic Policy Research 33 Great Sutton Street, London EC1V 0DX, UK.
Bibliographic data for series maintained by CEPR ().