iMLCA: Machine Learning-powered Iterative Combinatorial Auctions with Interval Bidding

TL;DR

The paper introduces iMLCA, an innovative iterative combinatorial auction leveraging machine learning and interval bidding, which reduces bidding costs while maintaining high efficiency and outperforming traditional methods.

Contribution

It presents a novel ML-powered auction with interval bidding and a price-based activity rule, improving efficiency and reducing bidder effort in large combinatorial auctions.

Findings

iMLCA achieves similar efficiency to exact-value ML auctions.

It outperforms the combinatorial clock auction in realistic scenarios.

Interval bidding reduces bidding costs without sacrificing allocation quality.

Abstract

Preference elicitation is a major challenge in large combinatorial auctions because the bundle space grows exponentially in the number of items. Recent work has used machine learning (ML) algorithms to identify a small set of bundles to query from each bidder. However, a shortcoming of this prior work is that bidders must submit exact values for the queried bundles, which can be quite costly. To address this, we propose iMLCA, a new ML-powered iterative combinatorial auction with interval bidding (i.e., where bidders submit upper and lower bounds instead of exact values). To steer the auction towards an efficient allocation, we introduce a price-based activity rule, asking bidders to tighten bounds on relevant bundles only. In our experiments, iMLCA achieves the same allocative efficiency as the prior ML-based auction that uses exact bidding. Moreover, it outperforms the well-known combinatorial clock auction in a realistically-sized domain.