ICML 2020poster21 citations

Reserve Pricing in Repeated Second-Price Auctions with Strategic Bidders

Alexey Drutsa

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.

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.}
}