Prices, Bids, Values: One ML-Powered Combinatorial Auction to Rule Them All
This work addresses the problem of improving efficiency and reducing cognitive load for bidders in combinatorial auctions, representing a strong specific gain rather than a foundational advancement.
The paper tackles the challenge of exponential bundle space in iterative combinatorial auctions by introducing MLHCA, a novel ML-powered auction that combines value and demand queries, reducing efficiency loss by up to a factor of 10 and using up to 58% fewer queries.
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 demand queries. In this paper, we introduce a novel ML algorithm that provably makes use of the full information from both value and demand queries, and we show via experiments that combining both query types results in significantly better learning performance in practice. Building on these insights, we present MLHCA, a new ML-powered auction that uses value and demand queries. MLHCA substantially outperforms the previous SOTA, reducing efficiency loss by up to a factor 10, with up to 58% fewer queries. Thus, MLHCA achieves large efficiency improvements while also reducing bidders' cognitive load, establishing a new benchmark for both practicability and efficiency.