ICML 2018oral23 citations
Weakly Consistent Optimal Pricing Algorithms in Repeated Posted-Price Auctions with Strategic Buyer
Abstract
We study revenue optimization learning algorithms for repeated posted-price auctions where a seller interacts with a single strategic buyer that holds a fixed private valuation for a good and seeks to maximize his cumulative discounted surplus. We propose a novel algorithm that never decreases offered prices and has a tight strategic regret bound of $\Theta(\log\log T)$. This result closes the open research question on the existence of a no-regret horizon-independent weakly consistent pricing. We also show that the property of non-decreasing prices is nearly necessary for a weakly consistent algorithm to be a no-regret one.
BibTeX
@InProceedings{pmlr-v80-drutsa18a,
title = {Weakly Consistent Optimal Pricing Algorithms in Repeated Posted-Price Auctions with Strategic Buyer},
author = {Drutsa, Alexey},
booktitle = {Proceedings of the 35th International Conference on Machine Learning},
pages = {1319--1328},
year = {2018},
editor = {Dy, Jennifer and Krause, Andreas},
volume = {80},
series = {Proceedings of Machine Learning Research},
month = {10--15 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v80/drutsa18a/drutsa18a.pdf},
url = {https://proceedings.mlr.press/v80/drutsa18a.html},
abstract = {We study revenue optimization learning algorithms for repeated posted-price auctions where a seller interacts with a single strategic buyer that holds a fixed private valuation for a good and seeks to maximize his cumulative discounted surplus. We propose a novel algorithm that never decreases offered prices and has a tight strategic regret bound of $\Theta(\log\log T)$. This result closes the open research question on the existence of a no-regret horizon-independent weakly consistent pricing. We also show that the property of non-decreasing prices is nearly necessary for a weakly consistent algorithm to be a no-regret one.}
}