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The Power of Simple Menus in Robust Selling Mechanisms

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  • Shixin Wang

Abstract

We study a robust selling problem where a seller attempts to sell one item to a buyer but is uncertain about the buyer's valuation distribution. Existing literature indicates that robust mechanism design provides a stronger theoretical guarantee than robust deterministic pricing. Meanwhile, the superior performance of robust mechanism design comes at the expense of implementation complexity given that the seller offers a menu with an infinite number of options, each coupled with a lottery and a payment for the buyer's selection. In view of this, the primary focus of our research is to find simple selling mechanisms that can effectively hedge against market ambiguity. We show that a selling mechanism with a small menu size (or limited randomization across a finite number of prices) is already capable of deriving significant benefits achieved by the optimal robust mechanism with infinite options. In particular, we develop a general framework to study the robust selling mechanism problem where the seller only offers a finite number of options in the menu. Then we propose a tractable reformulation that addresses a variety of ambiguity sets of the buyer's valuation distribution. Our formulation further enables us to characterize the optimal selling mechanisms and the corresponding competitive ratio for different menu sizes and various ambiguity sets, including support, mean, and quantile information. In light of the closed-form competitive ratios associated with different menu sizes, we provide managerial implications that incorporating a modest menu size already yields a competitive ratio comparable to the optimal robust mechanism with infinite options, which establishes a favorable trade-off between theoretical performance and implementation simplicity. Remarkably, a menu size of merely two can significantly enhance the competitive ratio, compared to the deterministic pricing scheme.

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  • Shixin Wang, 2023. "The Power of Simple Menus in Robust Selling Mechanisms," Papers 2310.17392, arXiv.org.
  • Handle: RePEc:arx:papers:2310.17392
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    Cited by:

    1. Jerry Anunrojwong & Santiago R. Balseiro & Omar Besbes, 2024. "The Best of Many Robustness Criteria in Decision Making: Formulation and Application to Robust Pricing," Papers 2403.12260, arXiv.org.

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