A Context-Integrated Transformer-Based Neural Network for Auction Design

One of the central problems in auction design is to develop an incentive compatible mechanism that maximizes the expected revenue. While theoretical approaches have encountered bottlenecks for multi-item auctions, recently there are many progresses of finding optimal auction through deep learning. However, these works either focus on fixed bidders and items, or restrict the auction to be symmetric. In this work, we overcome such limitations by factoring \emph{public} contextual information of bidders and items into the auction learning framework. We propose $\mathtt{CITransNet}$, a context-integrated transformer-based neural network for optimal auction design, which maintains permutation-equivariance over bids while being able to find asymmetric solutions. We show by extensive experiments that $\mathtt{CITransNet}$ can recover the known optimal solutions, outperform strong baselines in multi-item auctions, and generalize well to settings other than those in training.

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