BUNDLEFLOW: Deep Menus for Combinatorial Auctions by Diffusion-Based Optimization

Differentiable economics--the use of deep learning for auction design--has driven progress in multi-item auction design with additive and unit-demand valuations. However, there has been little progress for combinatorial auctions (CAs), even in the simplest and yet important single bidder case, due to exponential growth of the bundle space with the number of items. We address this challenge by introducing a deep network architecture for a menu-based CA, which supports the first dominantstrategy incentive compatible (DSIC), revenue-optimizing single-bidder CA. Our idea is to generate a bundle distribution through an ordinary differential equation (ODE) applied to a tractable initial distribution. Our method, BUNDLEFLOW, learns suitable ODE-based transforms, one for each menu element, to optimize expected revenue. BUNDLEFLOW achieves up to 2.23 higher revenue than baselines on standard CA testbeds and scales up to 500 items.