  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.
