Learning Truthful, Efficient, and Welfare Maximizing Auction Rules

TL;DR

This paper introduces a novel deep learning-based approach to designing auction rules that prioritize social welfare over monetary gains, outperforming existing methods in welfare, applicability, and robustness.

Contribution

It presents a new deep learning architecture for auction rule design that balances generality with welfare maximization, addressing limitations of traditional protocols.

Findings

Outperforms state-of-the-art auction rules in welfare metrics

Demonstrates robustness across various settings

Enhances applicability in complex, strategic environments

Abstract

From social networks to supply chains, more and more aspects of how humans, firms and organizations interact is mediated by artificial learning agents. As the influence of machine learning systems grows, it is paramount that we study how to imbue our modern institutions with our own values and principles. Here we consider the problem of allocating goods to buyers who have preferences over them in settings where the seller's aim is not to maximize their monetary gains, but rather to advance some notion of social welfare (e.g. the government trying to award construction licenses for hospitals or schools). This problem has a long history in economics, and solutions take the form of auction rules. Researchers have proposed reliable auction rules that work in extremely general settings, and in the presence of information asymmetry and strategic buyers. However, these protocols require significant payments from participants resulting in low aggregate welfare. Here we address this shortcoming by casting auction rule design as a statistical learning problem, and trade generality for participant welfare effectively and automatically with a novel deep learning network architecture and auction representation. Our analysis shows that our auction rules outperform state-of-the art approaches in terms of participants welfare, applicability, robustness.