Pandora's Box Problem with Order Constraints
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Abstract (arXiv)
The Pandora's Box Problem, originally formalized by Weitzman in 1979, models selection from set of random, alternative options, when evaluation is costly. This includes, for example, the problem of hiring a skilled worker, where only one hire can be made, but the evaluation of each candidate is an expensive procedure. Weitzman showed that the Pandora's Box Problem admits an elegant, simple solution, where the options are considered in decreasing order of reservation value,i.e., the value that reduces to zero the expected marginal gain for opening the box. We study for the first time this problem when order - or precedence - constraints are imposed between the boxes. We show that, despite the difficulty of defining reservation values for the boxes which take into account both in-depth and in-breath exploration of the various options, greedy optimal strategies exist and can be efficiently computed for tree-like order constraints. We also prove that finding approximately optimal adaptive search strategies is NP-hard when certain matroid constraints are used to further restrict the set of boxes which may be opened, or when the order constraints are given as reachability constraints on a DAG. We complement the above result by giving approximate adaptive search strategies based on a connection between optimal adaptive strategies and non-adaptive strategies with bounded adaptivity gap for a carefully relaxed version of the problem.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Feb 2020.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 2; 2024: 0; 2025: 5; 2026: 2.
12 papers.
- 2022Online Learning for Min Sum Set Cover and Pandora's Boxcited by 11
- 2025AI Agents as Universal Task Solverscited by 9
- 2023Semi-Bandit Learning for Monotone Stochastic Optimizationcited by 8
- 2025The Gittins Index: A Design Principle for Decision-Making Under Uncertaintycited by 6
- 2022Contextual Pandora's Boxcited by 5
- 2025Improved Regret and Contextual Linear Extension for Pandora's Box and Prophet Inequalitycited by 5
- 2021Approximating Pandora's Box with Correlationscited by 4
- 2026Scores Are Not Decisions: Cost-Aware Stopping for Tool Acquisition in LLM Agentscited by 4
- 2025Teaching Transformers to Solve Combinatorial Problems through Efficient Trial & Errorcited by 3
- 2023Optimal Pricing for Data-Augmented AutoML Marketplacescited by 0
- 2025T-TAMER: Provably Taming Trade-offs in ML Servingcited by 0
- 2026Capability-Gated Planning: Cost-to-Goal Discovery and the Limits of Myopic Experiment Selectioncited by 0
- 2026Capability-Gated Planning: Cost-to-Goal Discovery and the Limits of Myopic Experiment Selection5 Aug 2026
- 2026Scores Are Not Decisions: Cost-Aware Stopping for Tool Acquisition in LLM Agents29 Jul 2026
- 2025AI Agents as Universal Task Solvers14 Oct 2025
- 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
- 2023Semi-Bandit Learning for Monotone Stochastic Optimization24 Dec 2023
- 2023Optimal Pricing for Data-Augmented AutoML Marketplaces27 Oct 2023
- 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