Optimal Pricing in Repeated Posted-Price Auctions with Different Patience of the Seller and the Buyer
Abstract
We study revenue optimization pricing algorithms for repeated posted-price auctions where a seller interacts with a single strategic buyer that holds a fixed private valuation. When the participants non-equally discount their cumulative utilities, we show that the optimal constant pricing (which offers the Myerson price) is no longer optimal. In the case of more patient seller, we propose a novel multidimensional optimization functional --- a generalization of the one used to determine Myerson's price. This functional allows to find the optimal algorithm and to boost revenue of the optimal static pricing by an efficient low-dimensional approximation. Numerical experiments are provided to support our results.
Cite
Text
Vanunts and Drutsa. "Optimal Pricing in Repeated Posted-Price Auctions with Different Patience of the Seller and the Buyer." Neural Information Processing Systems, 2019.Markdown
[Vanunts and Drutsa. "Optimal Pricing in Repeated Posted-Price Auctions with Different Patience of the Seller and the Buyer." Neural Information Processing Systems, 2019.](https://mlanthology.org/neurips/2019/vanunts2019neurips-optimal/)BibTeX
@inproceedings{vanunts2019neurips-optimal,
title = {{Optimal Pricing in Repeated Posted-Price Auctions with Different Patience of the Seller and the Buyer}},
author = {Vanunts, Arsenii and Drutsa, Alexey},
booktitle = {Neural Information Processing Systems},
year = {2019},
pages = {941-953},
url = {https://mlanthology.org/neurips/2019/vanunts2019neurips-optimal/}
}