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 auctions, there has been much empirical progress in automated designing such mechanisms using machine learning. However, existing research primarily focuses on randomized auctions, with less attention given to the more practical deterministic auctions. Therefore, this paper investigates the automated design of deterministic auctions and introduces OD-VVCA, an objective decomposition approach for automated designing Virtual Valuations Combinatorial Auctions (VVCAs). Firstly, we restrict our mechanism to deterministic VVCAs, which are inherently DSIC and IR. Afterward, we utilize 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.

翻译：识别兼具占优策略激励相容（DSIC）与个体理性（IR）的高收益机制是拍卖设计中的一个基本挑战。尽管理论方法在多物品拍卖中遇到了瓶颈，但利用机器学习自动化设计此类机制已取得诸多实证进展。然而，现有研究主要集中于随机化拍卖，对更具实用性的确定性拍卖关注较少。因此，本文研究了确定性拍卖的自动化设计，并提出了OD-VVCA，一种用于自动化设计虚拟估值组合拍卖（VVCAs）的目标分解方法。首先，我们将机制限定为确定性VVCA，其本身即具有DSIC和IR特性。随后，我们采用一种可并行化的动态规划算法来高效计算VVCA的分配结果与收益。接着，我们将收益目标函数分解为连续部分和分段常数不连续部分，并使用不同的方法分别进行优化。大量实验表明，OD-VVCA在多物品拍卖中实现了高收益，尤其是在大规模场景下，其表现超越了随机化与确定性基线方法，证明了该方法的有效性和可扩展性。