TY - GEN
T1 - Prophet Inequalities via the Expected Competitive Ratio
AU - Ezra, Tomer
AU - Leonardi, Stefano
AU - Reiffenhäuser, Rebecca
AU - Russo, Matteo
AU - Tsigonias-Dimitriadis, Alexandros
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Downward-closed Feasibility Constraints
KW - Online Decision-Making
KW - Prophet Inequalities
UR - https://www.scopus.com/pages/publications/85181981288
U2 - 10.1007/978-3-031-48974-7_16
DO - 10.1007/978-3-031-48974-7_16
M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???
AN - SCOPUS:85181981288
SN - 9783031489730
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 272
EP - 289
BT - Web and Internet Economics - 19th International Conference, WINE 2023, Proceedings
A2 - Garg, Jugal
A2 - Klimm, Max
A2 - Kong, Yuqing
PB - Springer Science and Business Media Deutschland GmbH
T2 - 19th InternationalConference on Web and Internet Economics, WINE 2023
Y2 - 4 December 2023 through 8 December 2023
ER -