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摘要翻译：
考虑这样一个环境，政策制定者将受试者分配给治疗对象，在下一个受试者到达之前观察每个结果。最初，不知道哪种治疗是最好的，但问题的顺序性允许了解治疗的有效性。当政策制定者通过其平均值比较治疗的有效性时，多武装强盗文献已经对这种情况有了很大的了解，但对其他目标知之甚少。这是限制性的，因为谨慎的决策者可能更喜欢以稳健的位置度量为目标，如分位数或修剪平均值。此外，社会经济决策往往需要有针对性地确定结果分配的具体特点，例如其固有的不平等、福利或贫穷程度。本文介绍并研究了兴趣分布特征是结果分布的泛函时的序列学习算法。在探索-然后-提交策略的子类和所有策略的无限制类中，得到了极大极小期望后悔最优性结果。
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英文标题：
《Functional Sequential Treatment Allocation》
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作者：
Anders Bredahl Kock and David Preinerstorfer and Bezirgen Veliyev
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最新提交年份：
2020
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
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 paper 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.
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PDF链接：
https://arxiv.org/pdf/1812.09408
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