Prices, Bids, Values: One ML-Powered Combinatorial Auction to Rule Them All

Understand

We study the design of iterative combinatorial auctions (ICAs).

• The main challenge in this domain is that the bundle space grows exponentially in the number of items.
• To address this, recent work has proposed machine learning (ML)-based preference elicitation algorithms that aim to elicit only the most critical information from bidders to maximize efficiency.
• However, while the SOTA ML-based algorithms elicit bidders' preferences via value queries, ICAs that are used in practice elicit information via \emph{demand queries}.