通过最佳干预来设计单阶段的因果激励机制
Sebastián Bejos1, Eduardo F Morales1, Luis Enrique Sucar1
1Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Puebla 72840, Mexico.
Entropy (Basel, Switzerland)
|January 28, 2026
概括
我们介绍了因果激励设计 (CID),这是一个使用因果推理用于私人信息的主要代理问题的框架. 这使得在复杂的系统中选择最佳的激励措施,以获得更好的决策.
科学领域:
- 经济学 经济学 经济学
- 计算机科学 计算机科学
- 因果推理因果推理
- 机制设计 机制设计
背景情况:
- 主要代理问题 (PAP) 涉及在双边私人信息下优化激励措施.
- 现有的框架经常与私人信息和因果关系的复杂性作斗争.
- 因果图形模型 (CGM) 提供了一种正式的方式来表示这些关系.
研究的目的:
- 引入因果激励设计 (CID),这是一个新的框架,将因果推理与PAP整合在一起.
- 通过使用CGM和模式激励作为干预措施来正式制定PAP.
- 从观察数据中开发估计和选择最佳激励政策的方法.
主要方法:
- 使用添加噪音CGM的正式PAP.
- 模拟激励作为对函数空间变量 (Γ) 的干预措施.
- 开发了一个功能因果贝叶斯优化 (FCBO) 算法用于政策选择,利用功能高斯过程和UCB获取函数.
主要成果:
- 定义了一个因果估计和V ((Γ) 代表主体在干预下预期的效用.
- 开发了使用高斯-赫米特方程和内核重权的高效估计技术.
- 为FCBO算法建立了高概率的累积后悔边界.
结论:
- 在单击游戏中,CID提供了一个因果决策管道,用于选择高性能激励措施.
- 该框架允许离线政策选择,适用于适应性部署不切实际的场景.
- 这项工作开创了CGM和因果推理用于激励设计和PAP的应用.
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