Optimal Auctions through Deep Learning: Advances in Differentiable Economics-Reference-Cited by-同舟云学术

Optimal Auctions through Deep Learning: Advances in Differentiable Economics

Published:2023-11-11 Issue: Volume: Page:
ISSN:0004-5411
Container-title:Journal of the ACM
language:en
Short-container-title:J. ACM

Author:

Dütting Paul¹,Feng Zhe²,Narasimhan Harikrishna²,Parkes David C.³,Ravindranath Sai Srivatsa³

Affiliation:

1. Google Research, Switzerland

2. Google Research, USA

3. John A. Paulson School of Engineering and Applied Sciences, Harvard University, USA

Abstract

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, but more than 40 years later, a full analytical understanding of the optimal design still remains elusive for settings with two or more items. 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 machine learning pipelines. In addition to providing generalization bounds, we present extensive experimental results, recovering essentially all known solutions that come from the theoretical analysis of optimal auction design problems and obtaining novel mechanisms for settings in which the optimal mechanism is unknown.

Publisher

Association for Computing Machinery (ACM)

Subject

Artificial Intelligence,Hardware and Architecture,Information Systems,Control and Systems Engineering,Software

Link

https://dl.acm.org/doi/pdf/10.1145/3630749

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