Pandora's Problem with Nonobligatory Inspection: Optimal Structure and a PTAS
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Abstract (arXiv)
Weitzman introduced Pandora's box problem as a mathematical model of sequential search with inspection costs, in which a searcher is allowed to select a prize from one of n alternatives. Several decades later, Doval introduced a close version of the problem, where the searcher does not need to incur the inspection cost of an alternative, and can select it uninspected. Unlike the original problem, the optimal solution to the nonobligatory inspection variant is proved to need adaptivity, and by recent work of [FLL22], finding the optimal solution is NP-hard. Our first main result is a structural characterization of the optimal policy: We show there exists an optimal policy that follows only two different pre-determined orders of inspection, and transitions from one to the other at most once. Our second main result is a polynomial time approximation scheme (PTAS). Our proof involves a novel reduction to a framework developed by [FLX18], utilizing our optimal two-phase structure. Furthermore, we show Pandora's problem with nonobligatory inspection belongs to class NP, which by using the hardness result of [FLL22], settles the computational complexity class of the problem. Finally, we provide a tight 0.8 approximation and a novel proof for committing policies [BK19] (informally, the set of nonadaptive policies) for general classes of distributions, which was previously shown only for discrete and finite distributions [GMS08].
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Dec 2022.
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Citing papers per year: 2025: 4; 2026: 2.
6 papers.
- 2025The Gittins Index: A Design Principle for Decision-Making Under Uncertaintycited by 6
- 2025Improved Regret and Contextual Linear Extension for Pandora's Box and Prophet Inequalitycited by 4
- 2025Teaching Transformers to Solve Combinatorial Problems through Efficient Trial & Errorcited by 2
- 2026Pandora's AI Model Routing Box: Efficient Allocation with Costly Value Estimationcited by 1
- 2025T-TAMER: Provably Taming Trade-offs in ML Servingcited by 0
- 2026Online Convex Optimization with Sublinear Noisy Probescited by 0
- 2026Pandora's AI Model Routing Box: Efficient Allocation with Costly Value Estimation20 Aug 2026
- 2026Online Convex Optimization with Sublinear Noisy Probes12 Jun 2026
- 2025T-TAMER: Provably Taming Trade-offs in ML Serving26 Sep 2025
- 2025Teaching Transformers to Solve Combinatorial Problems through Efficient Trial & Error26 Sep 2025
- 2025The Gittins Index: A Design Principle for Decision-Making Under Uncertainty12 Jun 2025
- 2025Improved Regret and Contextual Linear Extension for Pandora's Box and Prophet Inequality24 May 2025