Functional Sequential Treatment Allocation
以分布泛函为目标的序贯处理分配AI 解读 ↓
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分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确以分位数、截尾均值、不平等、福利和贫困等结果分布泛函为决策目标。
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以分布泛函为目标的序贯处理分配
- 研究问题
- 当政策制定者逐个分配处理并即时观察结果，目标是分位数、不平等或福利等分布特征时，如何兼顾学习与决策？
- 方法与思路
- 作者将序贯学习目标扩展为结果分布的一般泛函，并分别研究先探索后固定选择的政策类与不受此限制的全部政策。
- 材料陈述的贡献
- 摘要称，文章为上述两类政策获得极小极大期望遗憾最优性结果，将序贯处理分配研究扩展至均值之外的分布目标。
- 阅读关注点
- 应核对允许哪些分布泛函、遗憾如何定义、最优性对应的分布类别，以及不同政策类的算法和信息要求有何差别。
- 阅读边界
- 摘要未列出泛函的正则条件、遗憾界或具体算法；不能将一般泛函的表述理解为对任意分布指标均无条件成立。
原文摘要
Consider a setting in which a policy maker assigns subjects to treatments, observing each outcome before the next subject arrives. Initially, it is unknown which treatment is best, but the sequential nature of the problem permits learning about the effectiveness of the treatments. While the multi-armed-bandit literature has shed much light on the situation when the policy maker compares the effectiveness of the treatments through their mean, much less is known about other targets. This is restrictive, because a cautious decision maker may prefer to target a robust location measure such as a quantile or a trimmed mean. Furthermore, socio-economic decision making often requires targeting purpose specific characteristics of the outcome distribution, such as its inherent degree of inequality, welfare or poverty. In the present article, we introduce and study sequential learning algorithms when the distributional characteristic of interest is a general functional of the outcome distribution. Minimax expected regret optimality results are obtained within the subclass of explore-then-commit policies, and for the unrestricted class of all policies. Supplementary materials for this article are available online.