在线拍卖设计使用无分布式不确定性量化与电子商务应用
Jiale Han1, Xiaowu Dai1,2
1Department of Statistics and Data Science, University of California, Los Angeles, CA.
Journal of the American Statistical Association
|January 19, 2026
概括
本研究介绍了合规在线拍卖设计 (COAD),这是一种最大限度地提高在线拍卖收入的新方法. 在不需要已知的分布的情况下,COAD量化了投标者价值的不确定性,改进了传统方法.
科学领域:
- 电子商务和计算经济学
- 机器学习和人工智能机器学习和人工智能
- 机制设计和游戏理论.
背景情况:
- 在线拍卖对电子商务至关重要,但由于未知的投标者价值和不确定的参与者数量,收入最大化面临挑战.
- 现有的机制往往依赖于对已知的价值分布和固定参与者/项目集的不切实际假设.
- 现实世界的在线拍卖需要灵活,强大的机制,有效地处理不确定性和未知分布.
研究的目的:
- 引入合规在线拍卖设计 (COAD),这是一个用于最大限度地提高在线拍卖收入的新机制.
- 解决传统拍卖设计的局限性,包括无发行不确定性量化.
- 开发一种与激励相容的机制,利用投标者和项目特征来改善收入的产生.
主要方法:
- 开发了合规在线拍卖设计 (COAD),一种新的收入最大化机制.
- 使用无分配不确定性量化技术来估计投标者价值.
- 集成机器学习模型 (随机森林,内核方法,深度神经网络) 具有投标者和项目特征.
- 基于估值的较低置信限值实施的投标人特定储备价格.
主要成果:
- COAD有效地量化了投标人价值的不确定性,而不假定已知的分布.
- 该机制在现实世界的eBay拍卖数据上表现出了实际的有效性.
- 理论结果和模拟验证了该方法的收入最大化特性和激励兼容性.
- 在单一储备价格策略下,投标者特定的储备价格得到了改善.
结论:
- 合规在线拍卖设计 (COAD) 为在线拍卖中的收入最大化提供了强大而有效的解决方案.
- 该方法成功地整合了机器学习和不确定性量化,用于实际的电子商务应用.
- 通过处理未知的分布和动态环境,COAD在传统的拍卖设计方法上取得了显著的进步.
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