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Multi-Dimensional Screening: Buyer-Optimal Learning and Informational Robustness

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  • Roesler, Anne-Katrin
  • Deb, Rahul

Abstract

A monopolist seller of multiple goods screens a buyer whose type is initially unknown to both but drawn from a commonly known distribution. The buyer privately learns about his type via a signal. We derive the seller’s optimal mechanism in two different information environments. We begin by deriving the buyer-optimal outcome. Here, an information designer first selects a signal, and then the seller chooses an optimal mechanism in response; the designer’s objective is to maximize consumer surplus. Then, we derive the optimal informationally robust mechanism. In this case, the seller first chooses the mechanism, and then nature picks the signal that minimizes the seller’s profits. We derive the relation between both problems and show that the optimal mechanism in both cases takes the form of pure bundling.

Suggested Citation

  • Roesler, Anne-Katrin & Deb, Rahul, 2021. "Multi-Dimensional Screening: Buyer-Optimal Learning and Informational Robustness," CEPR Discussion Papers 16206, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:16206
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    Cited by:

    1. is not listed on IDEAS
    2. Frank Yang, 2021. "Costly Multidimensional Screening," Papers 2109.00487, arXiv.org, revised Sep 2025.
    3. Shota Ichihashi & Alex Smolin, 2023. "Buyer-Optimal Algorithmic Recommendations," Papers 2309.12122, arXiv.org, revised Jun 2025.
    4. Bikhchandani, Sushil & Mishra, Debasis, 2024. "Rank-preserving multidimensional mechanisms: An equivalence between identical-object and heterogeneous-object models," Journal of Economic Theory, Elsevier, vol. 222(C).

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