License: 
Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ITCS.2023.11
URN: urn:nbn:de:0030-drops-175143
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/17514/
Balkanski, Eric ;
Gkatzelis, Vasilis ;
Tan, Xizhi
Strategyproof Scheduling with Predictions
Abstract
In their seminal paper that initiated the field of algorithmic mechanism design, Nisan and Ronen [Noam Nisan and Amir Ronen, 1999] studied the problem of designing strategyproof mechanisms for scheduling jobs on unrelated machines aiming to minimize the makespan. They provided a strategyproof mechanism that achieves an n-approximation and they made the bold conjecture that this is the best approximation achievable by any deterministic strategyproof scheduling mechanism. After more than two decades and several efforts, n remains the best known approximation and very recent work by Christodoulou et al. [George Christodoulou et al., 2021] has been able to prove an Ω(√n) approximation lower bound for all deterministic strategyproof mechanisms. This strong negative result, however, heavily depends on the fact that the performance of these mechanisms is evaluated using worst-case analysis. To overcome such overly pessimistic, and often uninformative, worst-case bounds, a surge of recent work has focused on the "learning-augmented framework", whose goal is to leverage machine-learned predictions to obtain improved approximations when these predictions are accurate (consistency), while also achieving near-optimal worst-case approximations even when the predictions are arbitrarily wrong (robustness).
In this work, we study the classic strategic scheduling problem of Nisan and Ronen [Noam Nisan and Amir Ronen, 1999] using the learning-augmented framework and give a deterministic polynomial-time strategyproof mechanism that is 6-consistent and 2n-robust. We thus achieve the "best of both worlds": an O(1) consistency and an O(n) robustness that asymptotically matches the best-known approximation. We then extend this result to provide more general worst-case approximation guarantees as a function of the prediction error. Finally, we complement our positive results by showing that any 1-consistent deterministic strategyproof mechanism has unbounded robustness.
BibTeX - Entry
@InProceedings{balkanski_et_al:LIPIcs.ITCS.2023.11,
author = {Balkanski, Eric and Gkatzelis, Vasilis and Tan, Xizhi},
title = {{Strategyproof Scheduling with Predictions}},
booktitle = {14th Innovations in Theoretical Computer Science Conference (ITCS 2023)},
pages = {11:1--11:22},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-263-1},
ISSN = {1868-8969},
year = {2023},
volume = {251},
editor = {Tauman Kalai, Yael},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2023/17514},
URN = {urn:nbn:de:0030-drops-175143},
doi = {10.4230/LIPIcs.ITCS.2023.11},
annote = {Keywords: Mechanism Design with Predictions, Strategyproof Scheduling}
}
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Keywords: |
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Mechanism Design with Predictions, Strategyproof Scheduling |
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Collection: |
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14th Innovations in Theoretical Computer Science Conference (ITCS 2023) |
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Issue Date: |
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2023 |
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Date of publication: |
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01.02.2023 |
