Optimal Auctions through Deep Learning


Paul Duetting, Zhe Feng, Harikrishna Narasimhan, David Parkes, Sai Srivatsa Ravindranath ;
Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1706-1715, 2019.


Designing an incentive compatible auction that maximizes expected revenue is an intricate task. The single-item case was resolved in a seminal piece of work by Myerson in 1981. Even after 30-40 years of intense research the problem remains unsolved for seemingly simple multi-bidder, multi-item settings. In this work, we initiate the exploration of the use of tools from deep learning for the automated design of optimal auctions. We model an auction as a multi-layer neural network, frame optimal auction design as a constrained learning problem, and show how it can be solved using standard pipelines. We prove generalization bounds and present extensive experiments, recovering essentially all known analytical solutions for multi-item settings, and obtaining novel mechanisms for settings in which the optimal mechanism is unknown.

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