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.
Cite
Text
Drutsa. "Weakly Consistent Optimal Pricing Algorithms in Repeated Posted-Price Auctions with Strategic Buyer." International Conference on Machine Learning, 2018.Markdown
[Drutsa. "Weakly Consistent Optimal Pricing Algorithms in Repeated Posted-Price Auctions with Strategic Buyer." International Conference on Machine Learning, 2018.](https://mlanthology.org/icml/2018/drutsa2018icml-weakly/)BibTeX
@inproceedings{drutsa2018icml-weakly,
title = {{Weakly Consistent Optimal Pricing Algorithms in Repeated Posted-Price Auctions with Strategic Buyer}},
author = {Drutsa, Alexey},
booktitle = {International Conference on Machine Learning},
year = {2018},
pages = {1319-1328},
volume = {80},
url = {https://mlanthology.org/icml/2018/drutsa2018icml-weakly/}
}