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Optimal auctions through deep learning

Dütting, Paul, Feng, Zhe, Narasimham, Harikrishna, Parkes, David C. and Ravindranath, Sal S (2019) Optimal auctions through deep learning. In: Proceedings of ICML 2019. Proceedings of Machine Learning Research,97. UNSPECIFIED, USA.

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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 multibidder, 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.

Item Type: Book Section
Official URL:
Additional Information: © 2019 The Authors
Divisions: Mathematics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
JEL classification: Z - Other Special Topics > Z0 - General > Z00 - General
Date Deposited: 29 May 2019 16:09
Last Modified: 30 Mar 2020 23:38

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