AI 中文总结
该研究对比了层次分析法的三种依赖排序的成对比较模式，确定其稳定性对比条件，通过模拟实验得出最稳定的不完整模式，可在不降低结果可信度的前提下减少比较次数。
AI 中文摘要
本文研究了几种依赖排序的决策支持方法：被比较对象的序数信息可用于提升估计过程中专家数据的质量，并减少专家需执行的比较次数。文中对比了三种可用于层次分析法（Analytic Hierarchy Process, AHP）的不完整依赖排序的成对比较模式：最佳-最差法（Best-worst method）、最佳-次佳（Top 2）法，以及原始最大差值法。前两种比较模式（及对应方法）为不完整模式，第三种则为完整模式。本文确定了三种方法在针对专家误差的稳定性方面可进行对比的条件，还呈现了模拟型实验的结果，在该实验中对三种方法展开了对比。此项研究可帮助我们确定最稳定的不完整依赖排序的成对比较模式，在不降低专家 session 结果可信度的前提下减少比较次数，为不确定环境下决策支持的算法、认知及应用层面作出贡献。
英文摘要
The paper addresses several ranking-dependent decision support methods. Ordinal information on compared objects can be used to improve the quality of expert data during estimation and help reduce the number of comparisons that the experts need to perform. In the paper we compare three incomplete ranking-dependent pair-wise comparison patterns which can be used in the Analytic Hierarchy Process - Best-worst method, Best-Second Best (Top 2) method, and the original maximum difference method. The first two comparison patterns (and respective methods) are incomplete, while the third can be a complete one. We determine conditions under which these three methods can be compared in terms of stability to expert errors. We also present the results of a simulation-type experiment, in which the three methods are compared. The research allows us to define the most stable incomplete ranking-dependent pair-wise comparison pattern and reduce the number of comparisons without loss of credibility of expert session results. The research contributes to algorithmic, cognitive, and applied aspects of decision support in uncertain environments.
Comments14 pages, 14 figures
