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Bidding with Budgets: Algorithmic and Data-Driven Bids in Digital Advertising

Author

Listed:
  • Dirk Bergemann

    (Yale University)

  • Alessandro Bonatti

    (Massachusetts Institute of Technology)

  • Nicholas Wu

    (Yale University)

Abstract

In digital advertising, the allocation of sponsored search, sponsored product, or display advertisements is mediated by auctions. The generation of bids in these auctions for attention is increasingly supported by auto-bidding algorithms and platform-provided data. We analyze the equilibrium properties of a sequence of increasingly sophisticated auto-bidding algorithms. First, we consider the equilibrium bidding behavior of an individual advertiser who controls the auto bidding algorithm through the choice of their budget. Second, we examine the interaction when all bidders use budget-controlled bidding algorithms. Finally, we derive the bidding algorithm that maximizes the platformÕs revenue while ensuring all advertisers continue to participate.

Suggested Citation

  • Dirk Bergemann & Alessandro Bonatti & Nicholas Wu, 2025. "Bidding with Budgets: Algorithmic and Data-Driven Bids in Digital Advertising," Cowles Foundation Discussion Papers 2429, Cowles Foundation for Research in Economics, Yale University.
    • Handle: RePEc:cwl:cwldpp:2429
    as

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    File URL: https://cowles.yale.edu/sites/default/files/2025-03/d2429.pdf
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    References listed on IDEAS

    as
    1. Emilio Calvano & Giacomo Calzolari & Vincenzo Denicolò & Sergio Pastorello, 2020. "Artificial Intelligence, Algorithmic Pricing, and Collusion," American Economic Review, American Economic Association, vol. 110(10), pages 3267-3297, October.
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