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Multidimensional Screening with Rich Consumer Data

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  • Mira Frick
  • Ryota Iijima
  • Yuhta Ishii

Abstract

A multi-product monopolist faces a buyer who is privately informed about his valuations for the goods. As is well-known, optimal mechanisms are in general complicated, while simple mechanisms -- such as pure bundling or separate sales -- can be far from optimal and do not admit clear-cut comparisons. We show that this changes if the monopolist observes sufficiently rich data about the buyer's valuations: Now, pure bundling always outperforms separate sales; moreover, there is a sense in which pure bundling performs essentially as well as the optimal mechanism. To formalize this, we characterize how fast the corresponding revenues converge to the first-best revenue as the monopolist's data grows rich: Pure bundling achieves the same convergence rate to the first-best as optimal mechanisms; in contrast, the convergence rate under separate sales is suboptimal.

Suggested Citation

  • Mira Frick & Ryota Iijima & Yuhta Ishii, 2024. "Multidimensional Screening with Rich Consumer Data," Papers 2411.06312, arXiv.org.
  • Handle: RePEc:arx:papers:2411.06312
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    References listed on IDEAS

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    5. Tim Roughgarden & Inbal Talgam-Cohen, 2019. "Approximately Optimal Mechanism Design," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 355-381, August.
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