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Automated auction design aims to find empirically high-revenue mechanismsthrough machine learning. Existing works on multi item auction scenarios can beroughly divided into RegretNet-like and affine maximizer auctions (AMAs)approaches. However, the former cannot strictly ensure dominant strategyincentive compatibility (DSIC), while the latter faces scalability issue due tothe large number of allocation candidates. To address these limitations, wepropose AMenuNet, a scalable neural network that constructs the AMA parameters(even including the allocation menu) from bidder and item representations.AMenuNet is always DSIC and individually rational (IR) due to the properties ofAMAs, and it enhances scalability by generating candidate allocations through aneural network. Additionally, AMenuNet is permutation equivariant, and itsnumber of parameters is independent of auction scale. We conduct extensiveexperiments to demonstrate that AMenuNet outperforms strong baselines in bothcontextual and non-contextual multi-item auctions, scales well to largerauctions, generalizes well to different settings, and identifies usefuldeterministic allocations. Overall, our proposed approach offers an effectivesolution to automated DSIC auction design, with improved scalability and strongrevenue performance in various settings.

A Scalable Neural Network for DSIC Affine Maximizer Auction Design