Designing an incentive compatible auction that maximizes expected revenue is an intricate task. The single-item case was resolved in a seminal piece of work by Myerson in 1981, but more than 40 years later a full analytical understanding of the optimal design still remains elusive for settings with two or more items. In this work, we initiate the exploration of the use of tools from deep learning for the automated design of optimal auctions. We model an auction as a multi-layer neural network, frame optimal auction design as a constrained learning problem, and show how it can be solved using standard machine learning pipelines. In addition to providing generalization bounds, we present extensive experimental results, recovering essentially all known solutions that come from the theoretical analysis of optimal auction design problems and obtaining novel mechanisms for settings in which the optimal mechanism is unknown.

翻译：设计一个奖励性兼容的拍卖,最大限度地增加预期收入是一项复杂的任务。 单项案件在1981年由Myerson在一项重要工作中得到解决,但40多年之后,对最佳设计的全面分析了解仍然难以在两个或两个以上项目的环境中找到。在这项工作中,我们开始探索如何利用深层学习工具来自动设计最佳拍卖。我们把拍卖模拟成多层神经网络,将最佳拍卖设计视为一个有限的学习问题,并表明如何利用标准的机器学习管道解决这个问题。除了提供一般化的界限外,我们提出了广泛的实验结果,从对最佳拍卖设计问题的理论分析中找到几乎所有已知的解决办法,并为最佳机制尚不为人所知的环境获取新机制。