Revenue-Incentive Tradeoffs in Dynamic Reserve Pricing

Yuan Deng, Sebastien Lahaie, Vahab Mirrokni, Song Zuo
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:2601-2610, 2021.

Abstract

Online advertisements are primarily sold via repeated auctions with reserve prices. In this paper, we study how to set reserves to boost revenue based on the historical bids of strategic buyers, while controlling the impact of such a policy on the incentive compatibility of the repeated auctions. Adopting an incentive compatibility metric which quantifies the incentives to shade bids, we propose a novel class of reserve pricing policies and provide analytical tradeoffs between their revenue performance and bid-shading incentives. The policies are inspired by the exponential mechanism from the literature on differential privacy, but our study uncovers mechanisms with significantly better revenue-incentive tradeoffs than the exponential mechanism in practice. We further empirically evaluate the tradeoffs on synthetic data as well as real ad auction data from a major ad exchange to verify and support our theoretical findings.

Cite this Paper

BibTeX
@InProceedings{pmlr-v139-deng21c, title = {Revenue-Incentive Tradeoffs in Dynamic Reserve Pricing}, author = {Deng, Yuan and Lahaie, Sebastien and Mirrokni, Vahab and Zuo, Song}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {2601--2610}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/deng21c/deng21c.pdf}, url = {https://proceedings.mlr.press/v139/deng21c.html}, abstract = {Online advertisements are primarily sold via repeated auctions with reserve prices. In this paper, we study how to set reserves to boost revenue based on the historical bids of strategic buyers, while controlling the impact of such a policy on the incentive compatibility of the repeated auctions. Adopting an incentive compatibility metric which quantifies the incentives to shade bids, we propose a novel class of reserve pricing policies and provide analytical tradeoffs between their revenue performance and bid-shading incentives. The policies are inspired by the exponential mechanism from the literature on differential privacy, but our study uncovers mechanisms with significantly better revenue-incentive tradeoffs than the exponential mechanism in practice. We further empirically evaluate the tradeoffs on synthetic data as well as real ad auction data from a major ad exchange to verify and support our theoretical findings.} }
Endnote
%0 Conference Paper %T Revenue-Incentive Tradeoffs in Dynamic Reserve Pricing %A Yuan Deng %A Sebastien Lahaie %A Vahab Mirrokni %A Song Zuo %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-deng21c %I PMLR %P 2601--2610 %U https://proceedings.mlr.press/v139/deng21c.html %V 139 %X Online advertisements are primarily sold via repeated auctions with reserve prices. In this paper, we study how to set reserves to boost revenue based on the historical bids of strategic buyers, while controlling the impact of such a policy on the incentive compatibility of the repeated auctions. Adopting an incentive compatibility metric which quantifies the incentives to shade bids, we propose a novel class of reserve pricing policies and provide analytical tradeoffs between their revenue performance and bid-shading incentives. The policies are inspired by the exponential mechanism from the literature on differential privacy, but our study uncovers mechanisms with significantly better revenue-incentive tradeoffs than the exponential mechanism in practice. We further empirically evaluate the tradeoffs on synthetic data as well as real ad auction data from a major ad exchange to verify and support our theoretical findings.
APA
Deng, Y., Lahaie, S., Mirrokni, V. & Zuo, S.. (2021). Revenue-Incentive Tradeoffs in Dynamic Reserve Pricing. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:2601-2610 Available from https://proceedings.mlr.press/v139/deng21c.html.

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