Incentive-aware Contextual Pricing with Non-parametric Market Noise
Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang
Abstract
We consider a dynamic pricing problem for repeated contextual second-price auctions with multiple strategic buyers who aim to maximize their long-term time discounted utility. The seller has limited information on buyers’ overall demand curves which depends on a non-parametric market-noise distribution, and buyers may potentially submit corrupted bids (relative to true valuations) to manipulate the seller’s pricing policy for more favorable reserve prices in the future. We focus on designing the seller’s learning policy to set contextual reserve prices where the seller’s goal is to minimize regret compared to the revenue of a benchmark clairvoyant policy that has full information of buyers’ demand. We propose a policy with a phased-structure that incorporates randomized “isolation” periods, during which a buyer is randomly chosen to solely participate in the auction. We show that this design allows the seller to control the number of periods in which buyers significantly corrupt their bids. We then prove that our policy enjoys a T-period regret of $O(\sqrt{T})$ facing strategic buyers. Finally, we conduct numerical simulations to compare our proposed algorithm to standard pricing policies. Our numerical results show that our algorithm outperforms these policies under various buyer bidding behavior.
BibTeX
@InProceedings{pmlr-v206-golrezaei23b,
title = {Incentive-aware Contextual Pricing with Non-parametric Market Noise},
author = {Golrezaei, Negin and Jaillet, Patrick and Cheuk Nam Liang, Jason},
booktitle = {Proceedings of The 26th International Conference on Artificial Intelligence and Statistics},
pages = {9331--9361},
year = {2023},
editor = {Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem},
volume = {206},
series = {Proceedings of Machine Learning Research},
month = {25--27 Apr},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v206/golrezaei23b/golrezaei23b.pdf},
url = {https://proceedings.mlr.press/v206/golrezaei23b.html},
abstract = {We consider a dynamic pricing problem for repeated contextual second-price auctions with multiple strategic buyers who aim to maximize their long-term time discounted utility. The seller has limited information on buyers’ overall demand curves which depends on a non-parametric market-noise distribution, and buyers may potentially submit corrupted bids (relative to true valuations) to manipulate the seller’s pricing policy for more favorable reserve prices in the future. We focus on designing the seller’s learning policy to set contextual reserve prices where the seller’s goal is to minimize regret compared to the revenue of a benchmark clairvoyant policy that has full information of buyers’ demand. We propose a policy with a phased-structure that incorporates randomized “isolation” periods, during which a buyer is randomly chosen to solely participate in the auction. We show that this design allows the seller to control the number of periods in which buyers significantly corrupt their bids. We then prove that our policy enjoys a T-period regret of $O(\sqrt{T})$ facing strategic buyers. Finally, we conduct numerical simulations to compare our proposed algorithm to standard pricing policies. Our numerical results show that our algorithm outperforms these policies under various buyer bidding behavior.}
}