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Dynamic Mechanism Design with Budget-Constrained Buyers Under Limited Commitment

Author

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  • Santiago R. Balseiro

    (Graduate School of Business, Columbia University, New York, New York 10027)

  • Omar Besbes

    (Graduate School of Business, Columbia University, New York, New York 10027)

  • Gabriel Y. Weintraub

    (Graduate School of Business, Stanford University, Stanford, California 94305)

Abstract

We study the dynamic mechanism design problem of a seller who repeatedly auctions independent items over a discrete time horizon to buyers who face a cumulative budget constraint. A driving motivation behind our model is the emergence of real-time bidding markets for online display advertising in which such budgets are prevalent. We assume the seller has a strong form of limited commitment: she commits to the rules of the current auction but cannot commit to those of future auctions. We show that the celebrated Myersonian approach that leverages the envelope theorem fails in this setting, and therefore, characterizing the dynamic optimal mechanism seems intractable. Despite these challenges, we derive and characterize a near-optimal dynamic mechanism. To do so, we show that the Myersonian approach is recovered in a corresponding fluid continuous time model in which the time interval between consecutive items becomes negligible. Then we leverage this approach to characterize the optimal dynamic direct-revelation mechanism, highlighting novel incentives at play in settings with buyers’ budget constraints and seller’s limited commitment. We show through a combination of theoretical and numerical results that the optimal mechanism arising from the fluid continuous time model approximately satisfies incentive compatibility for the buyers and is approximately sequentially rational for the seller in the original discrete time model.

Suggested Citation

  • Santiago R. Balseiro & Omar Besbes & Gabriel Y. Weintraub, 2019. "Dynamic Mechanism Design with Budget-Constrained Buyers Under Limited Commitment," Operations Research, INFORMS, vol. 67(3), pages 711-730, May.
  • Handle: RePEc:inm:oropre:v:67:y:2019:i:3:p:711-730
    DOI: 10.1287/opre.2018.1830
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    References listed on IDEAS

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    Cited by:

    1. Ilan Lobel, 2021. "Revenue Management and the Rise of the Algorithmic Economy," Management Science, INFORMS, vol. 67(9), pages 5389-5398, September.
    2. Santiago Balseiro & Omar Besbes & Francisco Castro, 2021. "Mechanism Design under Approximate Incentive Compatibility," Papers 2103.03403, arXiv.org, revised Mar 2022.
    3. Santiago Balseiro & Anthony Kim & Mohammad Mahdian & Vahab Mirrokni, 2021. "Budget-Management Strategies in Repeated Auctions," Operations Research, INFORMS, vol. 69(3), pages 859-876, May.

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