Designing an auction that maximizes expected revenue is an intricate task. Despite major efforts, only the single-item case is fully understood. We explore the use of tools from deep learning on this topic. The design objective is revenue optimal, dominant-strategy incentive compatible auctions. For a baseline, we show that multi-layer neural networks can learn almost-optimal auctions for a variety of settings for which there are analytical solutions, and even without encoding characterization results into the design of the network. Looking ahead, deep learning has promise for deriving auctions with high revenue for poorly understood problems.

Paul Duetting, Zhe Feng, Harikrishna Narasimhan, and David Parkes