ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning
This work addresses auction design for applications like online advertising and financial markets, but it is incremental as it builds on existing deep learning methods for auctions.
The paper tackles the problem of designing auctions that balance fairness and revenue, extending deep learning techniques to approximate optimal auctions while maintaining high revenue and strong incentive guarantees.
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.