论文标题
拍卖设计的置换量表神经网络体系结构
A Permutation-Equivariant Neural Network Architecture For Auction Design
论文作者
论文摘要
设计一个兼容拍卖，使预期收入最大化是拍卖设计中的核心问题。在过去的几十年中，解决问题的理论方法已经达到了一些限制，而分析解决方案仅以一些简单的设置而闻名。通过使用LPS来解决问题的计算方法具有自己的一套局限性。基于深度学习的成功，Duetting等人最近提出了一种新的方法。 （2019）拍卖是由馈送前馈神经网络建模的，设计问题被构成学习问题。该工作中使用的神经体系结构是通用的，并且不利用问题可能存在的任何对称性，例如置换量比。在这项工作中，我们考虑了具有置换式对称性的拍卖设计问题，并构建了能够完美恢复置换量等式的最佳机制的神经体系结构，我们表明，在先前的体系结构中我们表现不可能。我们证明，置换等值架构不仅能够恢复以前的结果，而且具有更好的概括属性。
Designing an incentive compatible auction that maximizes expected revenue is a central problem in Auction Design. Theoretical approaches to the problem have hit some limits in the past decades and analytical solutions are known for only a few simple settings. Computational approaches to the problem through the use of LPs have their own set of limitations. Building on the success of deep learning, a new approach was recently proposed by Duetting et al. (2019) in which the auction is modeled by a feed-forward neural network and the design problem is framed as a learning problem. The neural architectures used in that work are general purpose and do not take advantage of any of the symmetries the problem could present, such as permutation equivariance. In this work, we consider auction design problems that have permutation-equivariant symmetry and construct a neural architecture that is capable of perfectly recovering the permutation-equivariant optimal mechanism, which we show is not possible with the previous architecture. We demonstrate that permutation-equivariant architectures are not only capable of recovering previous results, they also have better generalization properties.
