Introduces a robust satisficing model for screening under Wasserstein ambiguity that meets a revenue target by minimizing worst-case shortfall, yielding tractable randomized pricing mechanisms that enhance buyer surplus over robust optimization under increasing hazard rates.
Available at SSRN 4045001 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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math.OC 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
New algorithms for joint contextual MNL assortment and pricing deliver improved online regret bounds of order W sqrt(d T log N)/L0 and local suboptimality guarantees offline.
citing papers explorer
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From Optimization to Satisficing: Robust Screening under Distributional Ambiguity
Introduces a robust satisficing model for screening under Wasserstein ambiguity that meets a revenue target by minimizing worst-case shortfall, yielding tractable randomized pricing mechanisms that enhance buyer surplus over robust optimization under increasing hazard rates.
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Optimal Online and Offline Algorithms for Contextual MNL with Applications to Assortment and Pricing
New algorithms for joint contextual MNL assortment and pricing deliver improved online regret bounds of order W sqrt(d T log N)/L0 and local suboptimality guarantees offline.