Learning and Collusion in Multi-Unit Auctions
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.
Cite
Text
Branzei et al. "Learning and Collusion in Multi-Unit Auctions." Neural Information Processing Systems, 2023.Markdown
[Branzei et al. "Learning and Collusion in Multi-Unit Auctions." Neural Information Processing Systems, 2023.](https://mlanthology.org/neurips/2023/branzei2023neurips-learning/)BibTeX
@inproceedings{branzei2023neurips-learning,
title = {{Learning and Collusion in Multi-Unit Auctions}},
author = {Branzei, Simina and Derakhshan, Mahsa and Golrezaei, Negin and Han, Yanjun},
booktitle = {Neural Information Processing Systems},
year = {2023},
url = {https://mlanthology.org/neurips/2023/branzei2023neurips-learning/}
}