Towards Prior-Free Approximately Truthful One-Shot Auction Learning via Differential Privacy

TL;DR

This paper proposes a differentially private deep learning approach to design approximately truthful, prior-free auctions, addressing the challenge of auction manipulation without relying on known bidder preference distributions.

Contribution

It adapts the RegretNet framework with differential privacy techniques to enable prior-free auction learning with approximate truthfulness.

Findings

Preliminary empirical results demonstrate feasibility.

Qualitative analysis shows potential for manipulation resistance.

Trade-offs include reduced performance compared to prior-dependent methods.

Abstract

Designing truthful, revenue maximizing auctions is a core problem of auction design. Multi-item settings have long been elusive. Recent work (arXiv:1706.03459) introduces effective deep learning techniques to find such auctions for the prior-dependent setting, in which distributions about bidder preferences are known. One remaining problem is to obtain priors in a way that excludes the possibility of manipulating the resulting auctions. Using techniques from differential privacy for the construction of approximately truthful mechanisms, we modify the RegretNet approach to be applicable to the prior-free setting. In this more general setting, no distributional information is assumed, but we trade this property for worse performance. We present preliminary empirical results and qualitative analysis for this work in progress.