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Prophet Inequalities via the Expected Competitive Ratio

  • Tomer Ezra
  • , Stefano Leonardi
  • , Rebecca Reiffenhäuser
  • , Matteo Russo*
  • , Alexandros Tsigonias-Dimitriadis
  • *Corresponding author for this work
  • Simons Laufer Mathematical Sciences Institute
  • Department of Computer Science
  • University of Rome La Sapienza
  • University of Amsterdam
  • European Central Bank

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

We consider prophet inequalities under downward-closed constraints. In this problem, a decision-maker makes immediate and irrevocable choices on arriving elements, subject to constraints. Traditionally, performance is compared to the expected offline optimum, called the Ratio of Expectations (RoE ). However, RoE has limitations as it only guarantees the average performance compared to the optimum, and might perform poorly against the realized ex-post optimal value. We study an alternative performance measure, the Expected Ratio (EoR ), namely the expectation of the ratio between algorithm’s and prophet’s value. EoR offers robust guarantees, e.g., a constant EoR implies achieving a constant fraction of the offline optimum with constant probability. For the special case of single-choice problems the EoR coincides with the well-studied notion of probability of selecting the maximum. However, the EoR naturally generalizes the probability of selecting the maximum for combinatorial constraints, which are the main focus of this paper. Specifically, we establish two reductions: for every constraint, RoE and the EoR are at most a constant factor apart. Additionally, we show that the EoR is a stronger benchmark than the RoE in that, for every instance (constraint and distribution), the RoE is at least a constant fraction of the EoR, but not vice versa. Both these reductions imply a wealth of EoR results in multiple settings where RoE results are known.

Original languageEnglish
Title of host publicationWeb and Internet Economics - 19th International Conference, WINE 2023, Proceedings
EditorsJugal Garg, Max Klimm, Yuqing Kong
PublisherSpringer Science and Business Media Deutschland GmbH
Pages272-289
Number of pages18
ISBN (Print)9783031489730
DOIs
StatePublished - 2024
Externally publishedYes
Event19th InternationalConference on Web and Internet Economics, WINE 2023 - Shanghai, China
Duration: 4 Dec 2023 → 8 Dec 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14413 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th InternationalConference on Web and Internet Economics, WINE 2023
Country/TerritoryChina
CityShanghai
Period4/12/23 → 8/12/23

Funding

FundersFunder number
Future Artificial Intelligence Research
Bundesministerium für Bildung und Forschung
TU Munich and Universidad de Chile
European Central Bank
Simons Laufer Mathematical Sciences Institute, University of California Berkeley
Ministero dell’Istruzione, dell’Università e della Ricerca
European Research Council
Alexander von Humboldt-Stiftung
National Science Foundation1928930, DMS-1928930
Agencia Nacional de Investigación y DesarrolloACT210005
Horizon 2020 Framework Programme788893, 1928930
Deutsche Forschungsgemeinschaft277991500
European Cooperation in Science and TechnologyCA16228
Alfred P. Sloan FoundationG-2021-16778

    Keywords

    • Downward-closed Feasibility Constraints
    • Online Decision-Making
    • Prophet Inequalities

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