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

Tonghan Wang, Yanchen Jiang, David C. Parkes

Keywords: Combinatorial Auctions, Continuous Flow Matching, Optimization, Dominant-Strategy Incentive Compatible, Revenue-Maximizing

TL;DR: We propose the first deep menu-based method for single-bidder combinatorial auctions that is dominant-strategy incentive compatible and revenue-optimizing, by adapting continuous flow matching for optimization problems.

Abstract: 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 dominant-strategy 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. The BundleFlow method learns suitable ODE-based transforms, one for each menu element, to optimize expected revenue. BundleFlow achieves up to 2.23$\times$ higher revenue than baselines on standard CA testbeds and scales up to 500 items. Compared with other menu-learning baselines, BundleFlow also reduces training iterations by 3.6-9.5$\times$ and cuts training time by about 80% in settings with 50 and 100 items.

Primary Area: Deep learning (e.g., architectures, generative models, optimization for deep networks, foundation models, LLMs)

Submission Number: 12959