Artificial Intelligence for Multi-Unit Auction design
This work addresses the problem of limited theoretical insights into multi-unit auctions for researchers, though it appears incremental as it applies existing AI methods to this domain.
The paper tackled the challenge of understanding bidding behavior in multi-unit auctions by using reinforcement learning to simulate bidding in three prominent auction types, introducing six algorithms and comparing them with an illustrative example.
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.