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Selecting numerical scales for pairwise comparisons

机译:选择数值刻度以进行成对比较

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It is often desirable in decision analysis problems to elicit from an individual the rankings of a population of attributes according to the individual's preference and to understand the degree to which each attribute is preferred to the others. A common method for obtaining this information involves the use of pairwise comparisons, which allows an analyst to convert subjective expressions of preference between two attributes into numerical values indicating preferences across the entire population of attributes. Key to the use of pairwise comparisons is the underlying numerical scale that is used to convert subjective linguistic expressions of preference into numerical values. This scale represents the psychological manner in which individuals perceive increments of preference among abstract attributes and it has important implications about the distribution and consistency of an individual's preferences. Three popular scale types, the traditional integer scales, balanced scales and power scales are examined. Results of a study of 64 individuals responding to a hypothetical decision problem show that none of these scales can accurately capture the preferences of all individuals. A study of three individuals working on an actual engineering decision problem involving the design of a decay heat removal system for a nuclear fission reactor show that the choice of scale can affect the preferred decision. It is concluded that applications of pairwise comparisons would benefit from permitting participants to choose the scale that best models their own particular way of thinking about the relative preference of attributes.
机译:在决策分析问题中,通常需要根据个人的喜好从个人中得出一组属性的排名,并了解每种属性相对于其他属性的偏好程度。获取此信息的常用方法涉及使用成对比较,这使分析人员可以将两个属性之间的主观偏好表达转换为表示整个属性总体中偏好的数值。使用成对比较的关键是用于将偏好的主观语言表达转换为数值的基础数字量表。该量表代表了个人感知抽象属性之间偏好增加的心理方式,并且对个人偏好的分布和一致性具有重要意义。检验了三种流行的秤类型:传统的整数秤,平衡秤和功率秤。对一个假设决策问题做出反应的64个人的研究结果表明,这些量表都无法准确地反映所有个人的偏好。对涉及解决核裂变反应堆衰变排热系统设计的实际工程决策问题的三个人的研究表明,规模的选择会影响首选的决策。结论是,成对比较的应用将受益于允许参与者选择最能模拟他们自己关于属性相对偏好的特定思维方式的量表。

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