论文标题
比例网络：平衡拍卖设计的公平和收入与深度学习
ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning
论文作者
论文摘要
通过强大的激励保证，收入最大化拍卖的设计是经济理论的核心关注点。计算拍卖可以在线广告，采购，频谱分配和众多金融市场。众所周知，在这个领域的分析进展是困难的。自从迈尔森（Myerson）1981年以单项最佳拍卖为特征的工作以来，限制设置以外的进展有限。 Dütting等人最近的一篇论文。通过将深度学习技术应用于近似最佳拍卖来规避分析困难。同时，Ilvento等人的新研究。在拍卖设计的背景下，其他群体也开发了公平的概念。受这些进步的启发，在本文中，我们扩展了用于使用深度学习近似拍卖的技术，以解决公平性的关注，同时保持高收入和强大的激励保证。
The design of revenue-maximizing auctions with strong incentive guarantees is a core concern of economic theory. Computational auctions enable online advertising, sourcing, spectrum allocation, and myriad financial markets. Analytic progress in this space is notoriously difficult; since Myerson's 1981 work characterizing single-item optimal auctions, there has been limited progress outside of restricted settings. A recent paper by Dütting et al. circumvents analytic difficulties by applying deep learning techniques to, instead, approximate optimal auctions. In parallel, new research from Ilvento et al. and other groups has developed notions of fairness in the context of auction design. Inspired by these advances, in this paper, we extend techniques for approximating auctions using deep learning to address concerns of fairness while maintaining high revenue and strong incentive guarantees.
