Using AI approaches to automatically design mechanisms has been a central
research mission at the interface of AI and economics [Conitzer and Sandholm,
2002]. Previous approaches that attempt to design revenue optimal auctions for
the multi-dimensional settings fall short in at least one of the three aspects:
1) representation -- search in a space that probably does not even contain the
optimal mechanism; 2) exactness -- finding a mechanism that is either not
truthful or far from optimal; 3) domain dependence -- need a different design
for different environment settings.
To resolve the three difficulties, in this paper, we put forward -- MenuNet
-- a unified neural network based framework that automatically learns to design
revenue optimal mechanisms. Our framework consists of a mechanism network that
takes an input distribution for training and outputs a mechanism, as well as a
buyer network that takes a mechanism as input and output an action. Such a
separation in design mitigates the difficulty to impose incentive compatibility
constraints on the mechanism, by making it a rational choice of the buyer. As a
result, our framework easily overcomes the previously mentioned difficulty in
incorporating IC constraints and always returns exactly incentive compatible
mechanisms.
We then apply our framework to a number of multi-item revenue optimal design
settings, for a few of which the theoretically optimal mechanisms are unknown.
We then go on to theoretically prove that the mechanisms found by our framework
are indeed optimal.
To the best of our knowledge, we are the first to apply neural networks to
discover optimal auction mechanisms with provable optimality.

本文介绍的是一个名为 MenuNet 的神经网络框架，它可以自动化地设计出最优拍卖机制，且在实验中得到的机制都是 incentive-compatible 的，能够在多项收入最优设计中应用，并经过理论证明的机制是最优的。