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

Author

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

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.

Suggested Citation

  • Decarolis, Francesco & Pellegrinetti, Tommaso & Rovigatti, Gabriele & Rovigatti, Michele & Shakhgildyan, Ksenia, 2025. "Algorithmic Competition and Informational Advantage in Digital Markets: Evidence from Search Auctions," CEPR Discussion Papers 19983, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:19983
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    Keywords

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    JEL classification:

    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D18 - Microeconomics - - Household Behavior - - - Consumer Protection
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions

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