Reserve Pricing in Repeated Second-Price Auctions with Strategic Bidders

Alexey Drutsa
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2678-2689, 2020.

Abstract

We study revenue optimization learning algorithms for repeated second-price auctions with reserve where a seller interacts with multiple strategic bidders each of which holds a fixed private valuation for a good and seeks to maximize his expected future cumulative discounted surplus. We propose a novel algorithm that has strategic regret upper bound of $O(\log\log T)$ for worst-case valuations. This pricing is based on our novel transformation that upgrades an algorithm designed for the setup with a single buyer to the multi-buyer case. We provide theoretical guarantees on the ability of a transformed algorithm to learn the valuation of a strategic buyer, which has uncertainty about the future due to the presence of rivals.

Cite this Paper

BibTeX
@InProceedings{pmlr-v119-drutsa20b, title = {Reserve Pricing in Repeated Second-Price Auctions with Strategic Bidders}, author = {Drutsa, Alexey}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {2678--2689}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/drutsa20b/drutsa20b.pdf}, url = {https://proceedings.mlr.press/v119/drutsa20b.html}, abstract = {We study revenue optimization learning algorithms for repeated second-price auctions with reserve where a seller interacts with multiple strategic bidders each of which holds a fixed private valuation for a good and seeks to maximize his expected future cumulative discounted surplus. We propose a novel algorithm that has strategic regret upper bound of $O(\log\log T)$ for worst-case valuations. This pricing is based on our novel transformation that upgrades an algorithm designed for the setup with a single buyer to the multi-buyer case. We provide theoretical guarantees on the ability of a transformed algorithm to learn the valuation of a strategic buyer, which has uncertainty about the future due to the presence of rivals.} }
Endnote
%0 Conference Paper %T Reserve Pricing in Repeated Second-Price Auctions with Strategic Bidders %A Alexey Drutsa %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-drutsa20b %I PMLR %P 2678--2689 %U https://proceedings.mlr.press/v119/drutsa20b.html %V 119 %X We study revenue optimization learning algorithms for repeated second-price auctions with reserve where a seller interacts with multiple strategic bidders each of which holds a fixed private valuation for a good and seeks to maximize his expected future cumulative discounted surplus. We propose a novel algorithm that has strategic regret upper bound of $O(\log\log T)$ for worst-case valuations. This pricing is based on our novel transformation that upgrades an algorithm designed for the setup with a single buyer to the multi-buyer case. We provide theoretical guarantees on the ability of a transformed algorithm to learn the valuation of a strategic buyer, which has uncertainty about the future due to the presence of rivals.
APA
Drutsa, A.. (2020). Reserve Pricing in Repeated Second-Price Auctions with Strategic Bidders. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:2678-2689 Available from https://proceedings.mlr.press/v119/drutsa20b.html.

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