具有均匀有序高效向量的配对比较矩阵

IF 3.2 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Susana Furtado , Charles R. Johnson
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引用次数: 0

摘要

我们的主要兴趣在于理解所有有效向量顺序相同的互易矩阵,即只有一种有效顺序(我们称这些矩阵为均匀有序矩阵,UO)。这些互易矩阵的有效向量都不会产生严格的顺序颠倒。如果倒数矩阵的每一列顺序相同,则称为列有序矩阵(CO)。列序相同的 CO 矩阵的有效向量总是存在的。例如,倒易矩阵部分或所有列的分项几何平均数是有效的,如果矩阵是 CO 矩阵,则列序相同。UO 的一个必要条件(但不是充分条件)是矩阵必须是 CO 矩阵,然后列应满足唯一有效的顺序(可能是弱的)。对于 UO 而言,CO 是必要且充分条件,但对于 .我们描述了 4 乘 4 UO 矩阵的特征,并确定了当矩阵为 CO 时三种可能的交替阶次(并给出了出现这些阶次的条目条件)。我们还描述了属于 UO 的简单扰动一致矩阵。为此开发的一些技术具有独立的意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pairwise comparison matrices with uniformly ordered efficient vectors

Our primary interest is understanding reciprocal matrices all of whose efficient vectors are ordinally the same, i.e., there is only one efficient order (we call these matrices uniformly ordered, UO). These are reciprocal matrices for which no efficient vector produces strict order reversals. A reciprocal matrix is called column ordered (CO) if each column is ordinally the same. Efficient vectors for a CO matrix with the same order of the columns always exist. For example, the entry-wise geometric mean of some or all columns of a reciprocal matrix is efficient and, if the matrix is CO, has the same order of the columns. A necessary, but not sufficient, condition for UO is that the matrix be CO and then the only efficient order should be satisfied by the columns (possibly weakly). In the case n=3, CO is necessary and sufficient for UO, but not for n>3. We characterize the 4-by-4 UO matrices and identify the three possible alternate orders when the matrix is CO (and give entry-wise conditions for their occurrence). We also describe the simple perturbed consistent matrices that are UO. Some of the technology developed for this purpose is of independent interest.

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来源期刊
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning 工程技术-计算机:人工智能
CiteScore
6.90
自引率
12.80%
发文量
170
审稿时长
67 days
期刊介绍: The International Journal of Approximate Reasoning is intended to serve as a forum for the treatment of imprecision and uncertainty in Artificial and Computational Intelligence, covering both the foundations of uncertainty theories, and the design of intelligent systems for scientific and engineering applications. It publishes high-quality research papers describing theoretical developments or innovative applications, as well as review articles on topics of general interest. Relevant topics include, but are not limited to, probabilistic reasoning and Bayesian networks, imprecise probabilities, random sets, belief functions (Dempster-Shafer theory), possibility theory, fuzzy sets, rough sets, decision theory, non-additive measures and integrals, qualitative reasoning about uncertainty, comparative probability orderings, game-theoretic probability, default reasoning, nonstandard logics, argumentation systems, inconsistency tolerant reasoning, elicitation techniques, philosophical foundations and psychological models of uncertain reasoning. Domains of application for uncertain reasoning systems include risk analysis and assessment, information retrieval and database design, information fusion, machine learning, data and web mining, computer vision, image and signal processing, intelligent data analysis, statistics, multi-agent systems, etc.
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