129421 20260217205516.0 doi 10.1016/j.eswa.2023.121922 sideral 135651 ART-2024-135651 eng Sáenz-Royo, Carlos Universidad de Zaragoza (orcid)0000-0001-7914-2623 Ordering vs. AHP. Does the intensity used in the decision support techniques compensate? 2024 Access copy available to the general public Unrestricted The manifestation of the intensity in the judgment of one alternative versus another in the peer comparison processes is a central element in some decision support techniques, such as the Analytical Hierarchy Process (AHP). However, its contribution regarding quality (expected performance) with respect to the priority vector has not been evaluated so far. Using the Intentional Bounded Rationality Methodology (IBRM), this work analyzes the gains obtained from requiring the decision-maker to report an intensity judgment in pairs (AHP) with respect to a technique that only requires expressing a preference (Ordering). The results show that when decision-makers have low levels of expertise, it is possible that a less informative and computational cheap technique (Ordering) performs better than a more informative and computational expensive one (AHP). When decision-makers have medium and high levels of expertise, AHP technique obtains modest gains with respect to the Ordering technique. This study proposes a cost-benefit analysis of decision support techniques contrasting the gains of a technique that requires more resources (AHP) against other that require less resources (Ordering). Our results can change the managing approach of the information obtained from experts’ judgments. info:eu-repo/grantAgreement/ES/AEI/PID2019-103880RB-I00 info:eu-repo/grantAgreement/ES/AEI/PID2020-113338RB-I00 info:eu-repo/grantAgreement/ES/MINECO/ECO2017-86305-C4-3-R info:eu-repo/grantAgreement/ES/UZ/UZCUD2017-SOC-04 info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es 7.5 2024 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 28 / 204 = 0.137 2024 Q1 T1 OPERATIONS RESEARCH & MANAGEMENT SCIENCE 7 / 106 = 0.066 2024 Q1 T1 ENGINEERING, ELECTRICAL & ELECTRONIC 37 / 368 = 0.101 2024 Q1 T1 1.854 2024 Artificial Intelligence 2024 Q1 Engineering (miscellaneous) 2024 Q1 Computer Science Applications 2024 Q1 15.0 2024 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Chiclana, Francisco Herrera-Viedma, Enrique 4012 650 Universidad de Zaragoza Dpto. Direcc.Organiza.Empresas Área Organización de Empresas 238, Part D (2024), 121922 Expert syst. appl. Expert Systems with Applications 0957-4174 804579 http://zaguan.unizar.es/record/129421/files/texto_completo.pdf Versión publicada 2671850 http://zaguan.unizar.es/record/129421/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:129421 articulos driver 2026-02-17-20:26:51 ARTICLE