Improving Deep Learning Powered Auction Design

Designing incentive-compatible and revenue-maximizing auctions is pivotal in mechanism design. Often referred to as optimal auction design, the area has seen little theoretical breakthrough since Myerson’s 1981 seminal work. Not to mention general combinatorial auctions, we don’t even know the optimal auction for selling as few as two distinct items to more than one bidder. In recent years, the stagnation of theoretical progress has promoted many in using deep learning models to find near-optimal auction mechanisms. In this paper, we provide two general methods to improve such deep learning models. Firstly, we propose a new data sampling method that achieves better coverage and utilisation of the possible data. Secondly, we propose a more fine-grained neural network architecture. Unlike existing models which output a single payment percentage for each bidder, the refined network outputs a separate payment percentage for each item. Such an item-wise approach captures the interaction among bidders at a granular level beyond previous models. We conducted comprehensive and in-depth experiments to test our methods and observed improvement in all tested models over their original design. Noticeably, we achieved state-of-the-art performance by applying our methods to an existing model.