根据需求评估设计选项:统计数据有多大帮助?

I. Alexander
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引用次数: 0

摘要

根据需求权衡候选设计是许多项目的关键活动。当许多涉众的目标发生冲突,因此不可能全部得到满足时,情况尤其如此。传统上,加权被用来结合不同标准的得分,从而确定一个获胜的设计。然而,这有一个薄弱的数学基础:标准应该是独立的维度,可以用不同的单位来测量。主成分分析(Principal Components Analysis)的统计技术提供了一种强有力的方法:给出明确的数据,它给出了明确的指导,形式为:“如果你喜欢这些标准,你应该支持这些候选人”。否则,表示无法给出指导。无论哪种方式,这都正确地将决策责任放在了人类的肩上。其结果是改进了项目的权衡过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluating Design Options against Requirements: How Far Can Statistics Help?
Trading-off candidate designs against requirements is a critical activity for many projects. This is especially so where the goals of many stakeholders conflict, and therefore cannot all be satisfied. Traditionally, weighting has been used to try to combine scores on different criteria, so as to identify a winning design. However, this has a weak mathematical basis: criteria should be independent dimensions, and may be measured in different units. The statistical technique of Principal Components Analysis offers a robust approach: given clear data, it gives clear guidance, of the form: "if you prefer these criteria, you should favour these candidates". Otherwise, it indicates that no guidance can be given. Either way, this rightly places responsibility for decision-making on human shoulders. The outcome is an improved trade-off process for projects.
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