Prophet Inequalities: Competing with the Top ℓ Items is Easy
Mathieu Molina, Nicolas Gast, Patrick Loiseau, Vianney Perchet
Abstract
We explore a prophet inequality problem, where the values of a sequence of items are drawn i.i.d. from some distribution, and an online decision maker must select one item irrevocably. We establish that CR ℓ the worst-case competitive ratio between the expected optimal performance of an online decision maker compared to that of a prophet who uses the average of the top ℓ items is exactly the solution to an integral equation. This quantity CR ℓ is larger than 1 -e -ℓ . This implies that the bound converges exponentially fast to 1 as ℓ grows. In particular for ℓ = 2, CR 2 ≈ 0.966 which is much closer to 1 than the classical bound of 0.745 for ℓ = 1. Additionally, we prove asymptotic lower bounds for the competitive ratio of a more general scenario, where the decision maker is permitted to select k items. This subsumes the k multi-unit i.i.d. prophet problem and provides the current best asymptotic guarantees, as well as enables broader understanding in the more general framework. Finally, we prove a tight asymptotic competitive ratio when only static threshold policies are allowed.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 87ba2b40-db50-403f-91cb-c47f7e1588c1Cited by top-tier papers1
Ask how each one uses itBuilds on2
Related papers
- Minimization is Harder in the Prophet WorldVasilis Livanos, Ruta MehtaSODA 2024 · 2 citations
- Prophet Inequality from Samples: Is the More the Merrier?Tomer EzraSODA 2026 · 1 citation
- Lookback Prophet InequalitiesZiyad Benomar, Dorian Baudry, Vianney PerchetNeurIPS 2024 · 2 citations
- Order Selection Prophet Inequality: From Threshold Optimization to Arrival Time DesignBo Peng, Zhihao Gavin TangFOCS 2022 · 12 citations
- Learning in Prophet Inequalities with Noisy ObservationsJung-hun Kim, Vianney PerchetICLR 2026
