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arXiv 2512.09367cs.GT
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带有预测的采购拍卖:改进的设施选址frugality

Procurement Auctions with Predictions: Improved Frugality for Facility Location

Eric Balkanski, Nicholas DeFilippis, Vasilis Gkatzelis, Xizhi Tan

AI总结:

本文提出了一种改进的设施选址拍卖机制,通过引入预测增强框架,提高了frugality比率并增强了鲁棒性。

摘要:

我们研究了设计采购拍卖以解决战略无容量设施选址问题:一家公司需要采购一组设施位置以服务于其客户,而每个设施位置由一个战略代理人拥有。每个拥有者有提供访问其设施的私人成本(例如出租或出售给公司),并需要相应得到补偿。目标是设计诚实的拍卖,决定公司应采购哪些设施以及向相应拥有者支付多少费用,旨在最小化总成本,即支付给拥有者的货币成本和客户所遭受的连接成本(他们到最近设施的距离)。我们使用frugality比率来评估这些拍卖的性能。我们首先分析经典VCG拍卖在此情境下的性能,并证明其frugality比率恰好为3。然后利用学习增强框架,设计带有对拥有者私人成本预测的拍卖。具体来说,我们提出了一组学习增强拍卖,当预测准确时,这些拍卖能实现显著的付款减少,从而带来更好的frugality比率。同时,我们证明即使预测任意不准确,这些拍卖仍保持稳健,并且在对抗性选择的预测下仍能保持合理的frugality比率。最后,我们提供一组“容错”拍卖,即使预测仅近似准确,它们仍能保持改进的frugality比率,并提供frugality比率的上界作为预测误差函数的函数。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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