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.
Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems , pages=
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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.