Credible, Sealed-bid, Optimal Repeated Auctions With Differentiable EconomicsDownload PDF

Published: 01 Feb 2023, 19:30, Last Modified: 13 Feb 2023, 23:26Submitted to ICLR 2023Readers: Everyone
Keywords: Mechanism Design, Differentiable Economics, Deep Learning, Zero-Knowledge Proofs
TL;DR: We propose an approach to run computationally efficient, credible, revenue-maximizing repeated auctions with cryptographic tools.
Abstract: Online advertisement auctions happen billions of times per day. Bidders in auctions strategize to improve their own utility, subject to published auctions' rules. Yet, bidders may not know that an auction has been run as promised. A credible auction is one in which bidders can trust the auctioneer to run its allocation and pricing mechanisms as promised. It is known that, assuming no communication between bidders, no credible, sealed-bid, and incentive compatible (aka ``truth-telling'' or otherwise truthful-participation-incentivizing) mechanism can exist. In reality, bidders can certainly communicate, so what happens if we relax this (typically unrealistic) constraint? In this work, we propose a framework incorporating cryptography to allow computationally-efficient, credible, revenue-maximizing (aka ``optimal'') auctions in a repeated auction setting. Our contribution is two-fold: first, we introduce a protocol for running repeated auctions with a verification scheme, and we show such a protocol can eliminate the auctioneer's incentive to deviate while costing negligible additional computation. Secondly, we provide a method for training optimal auctions under uncertain bidder participation profiles, which generalizes our protocol to a much wider class of auctions in the online ad market. Our empirical results show strong support for both the theory and competency of the proposed method.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Supplementary Material: zip
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics
Submission Guidelines: Yes
Please Choose The Closest Area That Your Submission Falls Into: Social Aspects of Machine Learning (eg, AI safety, fairness, privacy, interpretability, human-AI interaction, ethics)
11 Replies