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摘要翻译：
属性提供决策者正在考虑的备选方案的关键信息。当它们的大小不确定时，决策者可能不确定哪种选择是真正最好的，因此度量属性可以帮助决策者做出更好的决策。本文考虑的设置，其中每个测量产生一个样本的一个属性为一个备选方案。当给定要收集的样本数量固定时，决策者必须确定要获得哪些样本，进行测量，更新关于属性大小的先验信念，然后选择替代方案。本文给出了多属性选择决策的样本分配问题，并针对用离散分布建模不确定属性大小的情况，提出了两种顺序的、前瞻性的方法。这两个过程是相似的，但反映了不同的质量度量（和损失函数），这激励了不同的决策规则：（1）选择期望效用最大的方案；（2）选择最有可能是真正最佳的方案。我们进行了一个模拟研究，以评估序列过程和混合过程的性能，首先使用一个统一的分配过程分配一些样本，然后使用序列，向前看过程。结果表明，混合程序是有效的；用统一分配过程分配许多（但不是全部）初始样本不仅减少了总体计算工作量，而且选择了具有较低的平均机会成本和更经常是真正最佳的方案。
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英文标题：
《Lookahead and Hybrid Sample Allocation Procedures for Multiple Attribute
Selection Decisions》
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作者：
Jeffrey W. Herrmann and Kunal Mehta
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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：Theoretical Economics 理论经济学
分类描述：Includes theoretical contributions to Contract Theory, Decision Theory, Game Theory, General Equilibrium, Growth, Learning and Evolution, Macroeconomics, Market and Mechanism Design, and Social Choice.
包括对契约理论、决策理论、博弈论、一般均衡、增长、学习与进化、宏观经济学、市场与机制设计、社会选择的理论贡献。
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一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
Attributes provide critical information about the alternatives that a decision-maker is considering. When their magnitudes are uncertain, the decision-maker may be unsure about which alternative is truly the best, so measuring the attributes may help the decision-maker make a better decision. This paper considers settings in which each measurement yields one sample of one attribute for one alternative. When given a fixed number of samples to collect, the decision-maker must determine which samples to obtain, make the measurements, update prior beliefs about the attribute magnitudes, and then select an alternative. This paper presents the sample allocation problem for multiple attribute selection decisions and proposes two sequential, lookahead procedures for the case in which discrete distributions are used to model the uncertain attribute magnitudes. The two procedures are similar but reflect different quality measures (and loss functions), which motivate different decision rules: (1) select the alternative with the greatest expected utility and (2) select the alternative that is most likely to be the truly best alternative. We conducted a simulation study to evaluate the performance of the sequential procedures and hybrid procedures that first allocate some samples using a uniform allocation procedure and then use the sequential, lookahead procedure. The results indicate that the hybrid procedures are effective; allocating many (but not all) of the initial samples with the uniform allocation procedure not only reduces overall computational effort but also selects alternatives that have lower average opportunity cost and are more often truly best.
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PDF链接：
https://arxiv.org/pdf/2007.16119
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