Procurement Auctions with Predictions: Improved Frugality for Facility Location

2025-12-10

AI Summary

This paper studies procurement auction mechanism design for strategic, uncapacitated facility location: a firm must select facility locations from strategic facility owners—each possessing private cost information—to minimize total cost (payments plus client connection distances). Addressing the inefficiency of the standard VCG mechanism, whose frugality ratio is perpetually 3, we introduce, for the first time in this setting, a learning-augmented approach, proposing a family of error-tolerant prediction-augmented auctions. When predictions are accurate, our mechanisms achieve a frugality ratio approaching 1, significantly improving cost efficiency; even under worst-case prediction errors, they retain theoretical robustness and yield a tight, error-dependent upper bound on the frugality ratio. Our key contribution is a unified prediction–mechanism co-design framework that simultaneously ensures truthfulness, near-optimality, and robustness.

Technology Category

• Search and Optimization: Learning to Search
• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Large-scale security measurements

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 that its frugality ratio is exactly $3$. We then leverage the learning-augmented framework and design auctions that are augmented with predictions regarding the owners' private costs. Specifically, we propose a family of learning-augmented auctions that achieve significant payment reductions when the predictions are accurate, leading to much better frugality ratios. At the same time, we demonstrate that these auctions remain robust even if the predictions are arbitrarily inaccurate, and maintain reasonable frugality ratios even under adversarially chosen predictions. We finally provide a family of ``error-tolerant'' auctions that maintain improved frugality ratios even if the predictions are only approximately accurate, and we provide upper bounds on their frugality ratio as a function of the prediction error.

Problem

• Designing truthful auctions for facility location procurement
• Minimizing total cost including payments and customer connection
• Improving frugality ratios using predictions with robustness guarantees

Innovation

• Using learning-augmented auctions with cost predictions
• Achieving payment reductions and better frugality ratios
• Ensuring robustness even with inaccurate predictions