Pandora's Problem with Nonobligatory Inspection
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Abstract (arXiv)
Martin Weitzman's "Pandora's problem" furnishes the mathematical basis for optimal search theory in economics. Nearly 40 years later, Laura Doval introduced a version of the problem in which the searcher is not obligated to pay the cost of inspecting an alternative's value before selecting it. Unlike the original Pandora's problem, the version with nonobligatory inspection cannot be solved optimally by any simple ranking-based policy, and it is unknown whether there exists any polynomial-time algorithm to compute the optimal policy. This motivates the study of approximately optimal policies that are simple and computationally efficient. In this work we provide the first non-trivial approximation guarantees for this problem. We introduce a family of "committing policies" such that it is computationally easy to find and implement the optimal committing policy. We prove that the optimal committing policy is guaranteed to approximate the fully optimal policy within a 1-frac1e = 0.63ldots factor, and for the special case of two boxes we improve this factor to 4/5 and show that this approximation is tight for the class of committing policies.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 May 2019.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 0; 2024: 0; 2025: 3; 2026: 2.
9 papers.
- 2022Online Learning for Min Sum Set Cover and Pandora's Boxcited by 11
- 2022Online Learning and Bandits with Queried Hintscited by 6
- 2025The Gittins Index: A Design Principle for Decision-Making Under Uncertaintycited by 6
- 2022Contextual Pandora's Boxcited by 5
- 2021Approximating Pandora's Box with Correlationscited 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
- 2022Online Learning and Bandits with Queried Hints4 Nov 2022
- 2022Contextual Pandora's Box26 May 2022
- 2022Online Learning for Min Sum Set Cover and Pandora's Box10 Feb 2022
- 2021Approximating Pandora's Box with Correlations30 Aug 2021