We present a machine learning-powered iterative combinatorial auction (MLCA). The main goal of integrating machine learning (ML) into the auction is to improve preference elicitation, which is a major challenge in large combinatorial auctions (CAs). In contrast to prior work, our auction design uses value queries instead of prices to drive the auction. The ML algorithm is used to help the auction decide which value queries to ask in every iteration. While using ML inside a CA introduces new challenges, we demonstrate how we obtain a design that is individually rational, satisfies no-deficit, has good incentives, and is computationally practical. We benchmark our new auction against the well-known combinatorial clock auction (CCA). Our results indicate that, especially in large domains, MLCA can achieve significantly higher allocative efficiency than the CCA, even with only a small number of value queries.
翻译:我们展示了机器学习动力迭代组合拍卖(MLCA)。 将机器学习(MLA)纳入拍卖的主要目标是改进优惠引导,这是大型组合拍卖(CA)中的一大挑战。 与以往的工作不同,我们的拍卖设计使用价值查询而不是价格来驱动拍卖。 ML算法用来帮助拍卖决定每个迭代中哪些价值查询。 在在CA中使用ML时引入新的挑战的同时,我们展示了我们如何获得一个个人理性、无赤字、有良好激励和计算实用的设计。 我们用著名的组合时钟拍卖(CCA)作为我们新的拍卖基准。 我们的结果表明,特别是在大领域,LOLA可以实现比CCA高得多的分配效率,即使只有少量的价值查询。