Consider a seller seeking a selling mechanism to maximize the worst-case rev- enue obtained from a buyer whose valuation distribution lies in a certain ambiguity set. Such a mechanism design problem with one product and one buyer is known as the screening problem. For a generic convex ambiguity set, we show via the minimax theo- rem that strong duality holds between the problem of finding the optimal robust mecha- nism and a minimax pricing problem where the adversary first chooses a worst-case distribution, and then the seller decides the best posted price mechanism. This implies that the extra value of optimizing over more sophisticated mechanisms amounts exactly to the value of eliminating distributional ambiguity under a posted price mechanism. The duality result also connects prior literature that separately studies the primal (robust screening) and problems related to the dual (e.g., robust pricing, buyer-optimal pricing, and personalized pricing). We further analytically solve the minimax pricing problem (as well as the robust pricing problem) for several important ambiguity sets, such as the ones with mean and various dispersion measures, and with the Wasserstein metric, and we provide a unified geometric intuition behind our approach. The solutions are then used to construct the optimal robust mechanism and to compare with the solutions to the robust pricing problem. We also establish the uniqueness of the worst-case distribu- tion for some cases.
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