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

TL;DR

This paper introduces MLHCA, a machine learning-powered combinatorial auction that combines value and demand queries, significantly improving efficiency and reducing query complexity compared to previous methods.

Contribution

The paper develops a novel ML algorithm utilizing both value and demand queries, and presents MLHCA, a new auction that outperforms state-of-the-art methods in efficiency and practicality.

Findings

MLHCA reduces efficiency loss by up to a factor of 10.

MLHCA requires up to 58% fewer queries than previous algorithms.

Combining value and demand queries improves learning performance.

Abstract

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}. 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 significantly 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. Our code is available at https://github.com/marketdesignresearch/MLHCA.