Robust Stackelberg buyers in repeated auctions

Thomas Nedelec, Clement Calauzenes, Vianney Perchet, Noureddine El Karoui
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:1342-1351, 2020.

Abstract

We consider the practical and classical setting where the seller is using an exploration stage to learn the value distributions of the bidders before running a revenue-maximizing auction in a exploitation phase. In this two-stage process, we exhibit practical, simple and robust strategies with large utility uplifts for the bidders. We quantify precisely the seller revenue against non-discounted buyers, complementing recent studies that had focused on impatient/heavily discounted buyers. We also prove the robustness of these shading strategies to sample approximation error of the seller, to bidder’s approximation error of the competition and to possible change of the mechanisms.

Cite this Paper

BibTeX
@InProceedings{pmlr-v108-nedelec20a, title = {Robust Stackelberg buyers in repeated auctions}, author = {Nedelec, Thomas and Calauzenes, Clement and Perchet, Vianney and Karoui, Noureddine El}, booktitle = {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics}, pages = {1342--1351}, year = {2020}, editor = {Chiappa, Silvia and Calandra, Roberto}, volume = {108}, series = {Proceedings of Machine Learning Research}, month = {26--28 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v108/nedelec20a/nedelec20a.pdf}, url = {https://proceedings.mlr.press/v108/nedelec20a.html}, abstract = {We consider the practical and classical setting where the seller is using an exploration stage to learn the value distributions of the bidders before running a revenue-maximizing auction in a exploitation phase. In this two-stage process, we exhibit practical, simple and robust strategies with large utility uplifts for the bidders. We quantify precisely the seller revenue against non-discounted buyers, complementing recent studies that had focused on impatient/heavily discounted buyers. We also prove the robustness of these shading strategies to sample approximation error of the seller, to bidder’s approximation error of the competition and to possible change of the mechanisms. } }
Endnote
%0 Conference Paper %T Robust Stackelberg buyers in repeated auctions %A Thomas Nedelec %A Clement Calauzenes %A Vianney Perchet %A Noureddine El Karoui %B Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2020 %E Silvia Chiappa %E Roberto Calandra %F pmlr-v108-nedelec20a %I PMLR %P 1342--1351 %U https://proceedings.mlr.press/v108/nedelec20a.html %V 108 %X We consider the practical and classical setting where the seller is using an exploration stage to learn the value distributions of the bidders before running a revenue-maximizing auction in a exploitation phase. In this two-stage process, we exhibit practical, simple and robust strategies with large utility uplifts for the bidders. We quantify precisely the seller revenue against non-discounted buyers, complementing recent studies that had focused on impatient/heavily discounted buyers. We also prove the robustness of these shading strategies to sample approximation error of the seller, to bidder’s approximation error of the competition and to possible change of the mechanisms.
APA
Nedelec, T., Calauzenes, C., Perchet, V. & Karoui, N.E.. (2020). Robust Stackelberg buyers in repeated auctions. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 108:1342-1351 Available from https://proceedings.mlr.press/v108/nedelec20a.html.

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