多属性选择决策的前瞻和混合样本分配方法
Lookahead and Hybrid Sample Allocation Procedures for Multiple Attribute Selection Decisions
摘要：
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，因此测量属性可能有助于决策者做出更好的决策。本文考虑的设置中，每个测量产生一个样本的一个属性的一个替代品。当给定一个固定数量的样本收集，决策者必须确定哪些样本来获得，进行测量，更新有关属性大小的先验信念，然后选择一个替代方案。本文提出了多属性选择决策的样本分配问题，并提出了两个连续的，前瞻性的程序的情况下，离散分布模型的不确定属性的大小。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），这激发了不同的决策规则：（1）选择具有最大期望效用的替代方案，（2）选择最有可能成为真正最佳替代方案的替代方案。我们进行了模拟研究，以评估性能的顺序程序和混合程序，首先分配一些样本使用统一的分配程序，然后使用顺序，前瞻性的程序。结果表明，混合程序是有效的，分配许多（但不是所有）的初始样本的统一分配程序不仅减少了整体的计算工作量，但也选择了替代品，具有较低的平均机会成本，更经常是真正最好的。
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
