NeurIPS 2023poster4 citations
Learning and Collusion in Multi-unit Auctions
Simina Branzei, Mahsa Derakhshan, Negin Golrezaei, Yanjun Han
Abstract
In a carbon auction, licenses for CO2 emissions are allocated among multiple interested players. Inspired by this setting, we consider repeated multi-unit auctions with uniform pricing, which are widely used in practice. Our contribution is to analyze these auctions in both the offline and online settings, by designing efficient bidding algorithms with low regret and giving regret lower bounds. We also analyze the quality of the equilibria in two main variants of the auction, finding that one variant is susceptible to collusion among the bidders while the other is not.
multi-unit auctionsrepeated auctionsonline learningcollusiongames and learninglower boundsmultiplicative weight updatesbandit learning
BibTeX
@inproceedings{
branzei2023learning,
title={Learning and Collusion in Multi-unit Auctions},
author={Simina Branzei and Mahsa Derakhshan and Negin Golrezaei and Yanjun Han},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=1HKJ3lPz6m}
}