Optimal Auctions through Deep Learning

Optimal auction design is one of the cornerstones of economic theory. It is of great practical importance, as auctions are used across industries and by the public sector to organize the sale of their products and services. Concrete examples are the US FCC Incentive Auction, the sponsored search auctions conducted by web search engines such as Google, or the auctions run on platforms such as eBay. In the standard independent private valuations model, each bidder has a valuation function over subsets of items, drawn independently from not necessarily identical distributions. It is assumed that the auctioneer knows the distributions and can (and will) use this information in designing the auction. A major difficulty in designing auctions is that valuations are private and bidders need to be incentivized to report their valuations truthfully. The goal is to learn an incentive compatible auction that maximizes revenue. We would like to thank Yang Cai, Vincent Conitzer, Yannai Gonczarowski, Constantinos Daskalakis, Glenn Ellison, Sergiu Hart, Ron Lavi, Kevin Leyton-Brown, Shengwu Li, Noam Nisan, Parag Pathak, Alexander Rush, Karl Schlag, Alex Wolitzky, participants in the Economics and Computation Reunion Workshop at the Simons Institute, the NIPS'17 Workshop on Learning in the Presence of Strategic Behavior, a Dagstuhl Workshop on Computational Learning Theory meets Game Theory, the EC'18 Workshop on Algorithmic Game Theory and Data Science, the Annual Congress of the German Economic Association, participants in seminars at LSE, Technion, Hebrew, Google, HBS, MIT, and the anonymous reviewers on earlier versions of this paper for their helpful feedback. The first version of this paper was posted on arXiv on June 12, 2017.