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arXiv 2302.00941cs.GTstat.ML
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具有统计学习的稳健多物品拍卖设计

A Robust Multi-Item Auction Design with Statistical Learning

UCLA(加州大学洛杉矶分校)

Jiale Han, Xiaowu Dai

AI总结:

本文提出了一种结合统计学习和可信区间的多物品拍卖设计方法,通过两种新策略减少实施成本并提高效率。

摘要:

我们提出了一种新的统计学习方法,用于多物品拍卖,该方法结合了可信区间。我们的方法采用非参数密度估计来根据历史数据估计投标人类型的可信区间。我们引入了两种新的策略,利用这些可信区间来减少拍卖实施的时间成本。第一种策略在可信区间内筛选潜在赢家的价值区域,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,并且能够以高概率确保公平性、主导策略激励相容性和主导策略个体理性,同时减少实施成本。我们使用维克里-克劳斯-格罗夫斯机制展示了我们策略的有效性,并通过模拟实验评估了其性能。我们的结果表明,所提出的策略在持续性上优于其他方法,实现了收益最大化和成本降低的目标。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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