We propose a novel statistical learning method for multi-item auctions that incorporates credible intervals. Our approach employs nonparametric density estimation to estimate credible intervals for bidder types based on historical data. We introduce two new strategies that leverage these credible intervals to reduce the time cost of implementing auctions. The first strategy screens potential winners' value regions within the credible intervals, while the second strategy simplifies the type distribution when the length of the interval is below a threshold value. These strategies are easy to implement and ensure fairness, dominant-strategy incentive compatibility, and dominant-strategy individual rationality with a high probability, while simultaneously reducing implementation costs. We demonstrate the effectiveness of our strategies using the Vickrey-Clarke-Groves mechanism and evaluate their performance through simulation experiments. Our results show that the proposed strategies consistently outperform alternative methods, achieving both revenue maximization and cost reduction objectives.

翻译：我们提出了一种新颖的统计学习方法，用于多物品拍卖，该方法融入了可信区间。我们的方法采用非参数密度估计，基于历史数据为投标者类型估计可信区间。我们引入了两种新策略，利用这些可信区间来降低实施拍卖的时间成本。第一种策略在可信区间内筛选潜在获胜者的价值区域，而第二种策略在区间长度低于阈值时简化类型分布。这些策略易于实施，并以高概率确保公平性、占优策略激励相容性以及占优策略个体理性，同时降低实施成本。我们使用Vickrey-Clarke-Groves机制证明了我们策略的有效性，并通过仿真实验评估了其性能。我们的结果表明，所提出的策略始终优于替代方法，同时实现了收益最大化和成本降低的目标。