Procurement Auctions with Predictions: Improved Frugality for Facility Location
Eric Balkanski, Nicholas DeFilippis, Vasilis Gkatzelis, Xizhi Tan

TL;DR
This paper designs and analyzes procurement auctions for facility location, leveraging predictions to improve cost efficiency while maintaining robustness against prediction errors.
Contribution
It introduces learning-augmented auctions that reduce payments using cost predictions and proves their robustness and improved frugality ratios compared to classic methods.
Findings
VCG auction has a frugality ratio of exactly 3.
Predictions significantly reduce payments when accurate.
Auctions remain robust with inaccurate or adversarial predictions.
Abstract
We study the problem of designing procurement auctions for the strategic uncapacitated facility location problem: a company needs to procure a set of facility locations in order to serve its customers and each facility location is owned by a strategic agent. Each owner has a private cost for providing access to their facility (e.g., renting it or selling it to the company) and needs to be compensated accordingly. The goal is to design truthful auctions that decide which facilities the company should procure and how much to pay the corresponding owners, aiming to minimize the total cost, i.e., the monetary cost paid to the owners and the connection cost suffered by the customers (their distance to the nearest facility). We evaluate the performance of these auctions using the \emph{frugality ratio}. We first analyze the performance of the classic VCG auction in this context and prove…
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Taxonomy
TopicsAuction Theory and Applications · Game Theory and Voting Systems · Game Theory and Applications
