IJCAI 2022poster37 citations
Mechanism Design with Predictions
Abstract
Improving algorithms via predictions is a very active research topic in recent years. This paper initiates the systematic study of mechanism design in this model. In a number of well-studied mechanism design settings, we make use of imperfect predictions to design mechanisms that perform much better than traditional mechanisms if the predictions are accurate (consistency), while always retaining worst-case guarantees even with very imprecise predictions (robustness). Furthermore, we refer to the largest prediction error sufficient to give a good performance as the error tolerance of a mechanism, and observe that an intrinsic tradeoff among consistency, robustness and error tolerance is common for mechanism design with predictions.
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BibTeX
@inproceedings{ijcai2022p81,
title = {Mechanism Design with Predictions},
author = {Xu, Chenyang and Lu, Pinyan},
booktitle = {Proceedings of the Thirty-First International Joint Conference on
Artificial Intelligence, {IJCAI-22}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Lud De Raedt},
pages = {571--577},
year = {2022},
month = {7},
note = {Main Track},
doi = {10.24963/ijcai.2022/81},
url = {https://doi.org/10.24963/ijcai.2022/81},
}