Robust Pricing in Dynamic Mechanism Design

Yuan Deng, Sebastien Lahaie, Vahab Mirrokni
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2494-2503, 2020.

Abstract

Motivated by the repeated sale of online ads via auctions, optimal pricing in repeated auctions has attracted a large body of research. While dynamic mechanisms offer powerful techniques to improve on both revenue and efficiency by optimizing auctions across different items, their reliance on exact distributional information of buyers’ valuations (present and future) limits their use in practice. In this paper, we propose robust dynamic mechanism design. We develop a new framework to design dynamic mechanisms that are robust to both estimation errors in value distributions and strategic behavior. We apply the framework in learning environments, leading to the first policy that achieves provably low regret against the optimal dynamic mechanism in contextual auctions, where the dynamic benchmark has full and accurate distributional information.

Cite this Paper


BibTeX
@InProceedings{pmlr-v119-deng20d, title = {Robust Pricing in Dynamic Mechanism Design}, author = {Deng, Yuan and Lahaie, Sebastien and Mirrokni, Vahab}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {2494--2503}, year = {2020}, editor = {Hal Daumé III and Aarti Singh}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/deng20d/deng20d.pdf}, url = { http://proceedings.mlr.press/v119/deng20d.html }, abstract = {Motivated by the repeated sale of online ads via auctions, optimal pricing in repeated auctions has attracted a large body of research. While dynamic mechanisms offer powerful techniques to improve on both revenue and efficiency by optimizing auctions across different items, their reliance on exact distributional information of buyers’ valuations (present and future) limits their use in practice. In this paper, we propose robust dynamic mechanism design. We develop a new framework to design dynamic mechanisms that are robust to both estimation errors in value distributions and strategic behavior. We apply the framework in learning environments, leading to the first policy that achieves provably low regret against the optimal dynamic mechanism in contextual auctions, where the dynamic benchmark has full and accurate distributional information.} }
Endnote
%0 Conference Paper %T Robust Pricing in Dynamic Mechanism Design %A Yuan Deng %A Sebastien Lahaie %A Vahab Mirrokni %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-deng20d %I PMLR %P 2494--2503 %U http://proceedings.mlr.press/v119/deng20d.html %V 119 %X Motivated by the repeated sale of online ads via auctions, optimal pricing in repeated auctions has attracted a large body of research. While dynamic mechanisms offer powerful techniques to improve on both revenue and efficiency by optimizing auctions across different items, their reliance on exact distributional information of buyers’ valuations (present and future) limits their use in practice. In this paper, we propose robust dynamic mechanism design. We develop a new framework to design dynamic mechanisms that are robust to both estimation errors in value distributions and strategic behavior. We apply the framework in learning environments, leading to the first policy that achieves provably low regret against the optimal dynamic mechanism in contextual auctions, where the dynamic benchmark has full and accurate distributional information.
APA
Deng, Y., Lahaie, S. & Mirrokni, V.. (2020). Robust Pricing in Dynamic Mechanism Design. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:2494-2503 Available from http://proceedings.mlr.press/v119/deng20d.html .

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