论文标题
一种用于生成个性化数字量表(Bagins)的基本算法
A Basic Algorithm for Generating Individualized Numerical Scale (BAGINS)
论文作者
论文摘要
语言标签是表达定性评估的有效手段,因为它们解释了人类偏好的不确定性。但是,要使用语言标签执行计算,必须首先使用比例功能将它们转换为数字。在分析层次结构过程(AHP)的背景下,用于数值表示语言标签的最流行的量表是线性1-9量表,这是由Saaty提出的。但是,该量表受到了几位研究人员的批评,文献中提出了各种替代方案。由于决策者对这些语言标签的看法是高度主观的,因此对规模个性化的兴趣越来越不仅依赖于通用的固定规模。文献中提出的有关规模个性化的方法集中于最大程度地减少传递性误差,即一致性。在这项研究中,我们提出了一种基于兼容性的新颖,易于学习的,易于实现的,易于实现的尺度个性化方法。我们还开发了一个实验设置,并介绍了两个新的指标,这些指标可以由研究人员使用,这些指标有助于AHP理论。为了评估总体个性化的价值,并特别是提出的新方法的表现,进行了数值和两项经验研究。分析结果表明,量表个性化的表现优于常规固定尺度方法,并验证了提出的新型启发式措施的好处。
Linguistic labels are effective means of expressing qualitative assessments because they account for the uncertain nature of human preferences. However, to perform computations with linguistic labels, they must first be converted to numbers using a scale function. Within the context of the Analytic Hierarchy Process (AHP), the most popular scale used to represent linguistic labels numerically is the linear 1-9 scale, which was proposed by Saaty. However, this scale has been criticized by several researchers, and various alternatives are proposed in the literature. There is a growing interest in scale individualization rather than relying on a generic fixed scale since the perceptions of the decision maker regarding these linguistic labels are highly subjective. The methods proposed in the literature for scale individualization focus on minimizing the transitivity errors, i.e., consistency. In this research, we proposed a novel, easy-to-learn, easy-to-implement, and computationally less demanding scale individualization approach based on compatibility. We also developed an experimental setup and introduced two new metrics that can be used by researchers that contribute to the theory of AHP. To assess the value of scale individualization in general, and the performance of the proposed novel approach in particular, numerical and two empirical studies are conducted. The results of the analyses demonstrate that the scale individualization outperforms the conventional fixed scale approach and validates the benefit of the proposed novel heuristic.