# Artificial Intelligence for Multi-Unit Auction design

arXiv: https://arxiv.org/abs/2404.15633 · HTML: https://mlsanity.com/paper/2404.15633/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 24 Apr 2024.

## Builds on (most cited first, 6 of 6)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Learning Simple Auctions](https://mlsanity.com/paper/1604.03171/) (2016)
- [Artificial Intelligence and Auction Design](https://mlsanity.com/paper/2202.05947/) (2022)
- [A2C is a special case of PPO](https://mlsanity.com/paper/2205.09123/) (2022)
- [The Price of Anarchy in Auctions](https://mlsanity.com/paper/1607.07684/) (2016)
- [Strategic Bidding in Knapsack Auctions](https://mlsanity.com/paper/2403.07928/) (2024)

## Abstract (arXiv)

Understanding bidding behavior in multi-unit auctions remains an ongoing challenge for researchers. Despite their widespread use, theoretical insights into the bidding behavior, revenue ranking, and efficiency of commonly used multi-unit auctions are limited. This paper utilizes artificial intelligence, specifically reinforcement learning, as a model free learning approach to simulate bidding in three prominent multi-unit auctions employed in practice. We introduce six algorithms that are suitable for learning and bidding in multi-unit auctions and compare them using an illustrative example. This paper underscores the significance of using artificial intelligence in auction design, particularly in enhancing the design of multi-unit auctions.