Designing an auction that maximizes expected revenue is a major open problem in economics. Despite significant effort, only the single-item case is fully understood. We explore the use of tools from deep learning on this topic. Multi-layer neural networks can learn essentially optimal auction designs for the few problems that have been solved analytically, and can be used to design auctions for poorly understood problems, including settings with multiple items and budget constraints. I will also overview applications to other problems of optimal economic design and discuss the broader implications of this work.
Joint work with Paul Duetting (LSE), Zhe Feng (Harvard University), Noah Golowich (Harvard University), and Harikrishna Narasimhan (Harvard University).
The lecture is given as part of the Israel Pollak Distinguished Lecture Series 2018