Automated Deterministic Auction Design with Objective Decomposition

摘要

Identifying high-revenue mechanisms that are both dominant strategy incentive compatible (DSIC) and individually rational (IR) is a fundamental challenge in auction design. While theoretical approaches have encountered bottlenecks in multi-item combinatorial auctions, there has been much empirical progress in the automated design of such mechanisms using machine learning. However, existing research primarily focuses on randomized auctions, with less attention given to more practical deterministic auctions. Therefore, in this paper, we introduce OD-VVCA, an objective decomposition approach for automated designing revenue-maximizing deterministic Virtual Valuations Combinatorial Auctions (VVCAs), which are inherently DSIC and IR. We use a parallelizable dynamic programming algorithm to compute the allocation and revenue outcomes of a VVCA efficiently. We then decompose the revenue objective function into continuous and piecewise-constant discontinuous components, optimizing each using distinct methods. Extensive experiments show that OD-VVCA achieves high revenue in multi-item auctions, especially in large-scale settings where it outperforms both randomized and deterministic baselines, indicating its efficacy and scalability.