比较三种排序依赖的两两比较方法，找出最稳定且省时的决策方式。
Stability of Ranking-dependent Pair-wise Comparison Patterns in the Analytic Hierarchy Process
- 对比三种两两比较模式：最佳-最差、最佳-次佳、最大差异法。
- 仿真实验表明最佳-最差法在专家误差下最稳定，且减少比较次数。
- 适合需要高效可信决策支持的场景，如多准则评估与认知研究。
本文研究几种依赖排序的决策支持方法。利用被比较对象的序信息可提升专家数据质量，并减少所需比较次数。文中比较了三种适用于层次分析法的不完整排序依赖两两比较模式：最佳-最差法、最佳-次佳法（顶2法）以及原始最大差异法。前两种为不完整模式，第三种可为完整模式。本文确定了三者在专家误差下的稳定性比较条件，并通过仿真实验进行对比。研究结果有助于识别最稳定的不完整两两比较模式，在不损失专家判断可信度的前提下减少比较数量。该研究在算法、认知及应用层面均对不确定环境中的决策支持有所贡献。
原文摘要 · Abstract (English)
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.
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