MAGNOT, Jean-Pierre, Jiří MAZUREK and Viera ČERŇANOVÁ. A gradient method for inconsistency reduction of pairwise comparisons matrices. International Journal of Approximate Reasoning. USA, 2022, Neuveden, No 152, p. 46-58. ISSN 0888-613X.
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Basic information
Original name A gradient method for inconsistency reduction of pairwise comparisons matrices
Authors MAGNOT, Jean-Pierre (250 France), Jiří MAZUREK (203 Czech Republic, guarantor, belonging to the institution) and Viera ČERŇANOVÁ (703 Slovakia).
Edition International Journal of Approximate Reasoning, USA, 2022, 0888-613X.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher United States of America
Confidentiality degree is not subject to a state or trade secret
WWW URL
RIV identification code RIV/47813059:19520/22:A0000289
Organization unit School of Business Administration in Karvina
UT WoS 000877684300003
Keywords in English Gradient method; Inconsistency indicator; Pairwise comparisons; Priority vector
Tags International impact, Reviewed
Links GA21-03085S, research and development project.
Changed by Changed by: Miroslava Snopková, učo 43819. Changed: 11/4/2023 11:15.
Abstract
We investigate an application of a mathematically robust minimization method - the gradient method - to the consistencization problem of pairwise comparisons (PC) matrix. Our approach sheds new light on the notion of a priority vector and leads naturally to the definition of instant priority vectors. We describe a sample family of inconsistency indicators based on various ways of taking an average value, which extends the inconsistency indicator based on the “sup”- norm. We apply this family of inconsistency indicators both for additive and multiplicative PC matrices to show that the choice of various inconsistency indicators leads to non-equivalent consistencization procedures.
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