Ordering vs. AHP. Does the intensity used in the decision support techniques compensate?

Sáenz-Royo, Carlos (Universidad de Zaragoza) ; Chiclana, Francisco ; Herrera-Viedma, Enrique

Resumen: 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.

Idioma: Inglés

DOI: 10.1016/j.eswa.2023.121922

Año: 2024

Publicado en: Expert Systems with Applications 238, Part D (2024), 121922
ISSN: 0957-4174

Factor impacto JCR: 7.5 (2024)

Categ. JCR: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE rank: 28 / 204 = 0.137 (2024) - Q1 - T1
Categ. JCR: OPERATIONS RESEARCH & MANAGEMENT SCIENCE rank: 7 / 106 = 0.066 (2024) - Q1 - T1
Categ. JCR: ENGINEERING, ELECTRICAL & ELECTRONIC rank: 36 / 366 = 0.098 (2024) - Q1 - T1

Factor impacto CITESCORE: 13.8 - Computer Science Applications (Q1) - Artificial Intelligence (Q1) - Engineering (all) (Q1)

Factor impacto SCIMAGO: 1.854 - Artificial Intelligence (Q1) - Computer Science Applications (Q1) - Engineering (miscellaneous) (Q1)

Financiación: info:eu-repo/grantAgreement/ES/AEI/PID2019-103880RB-I00
Financiación: info:eu-repo/grantAgreement/ES/AEI/PID2020-113338RB-I00
Financiación: info:eu-repo/grantAgreement/ES/MINECO/ECO2017-86305-C4-3-R
Financiación: info:eu-repo/grantAgreement/ES/UZ/UZCUD2017-SOC-04

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Organización de Empresas (Dpto. Direcc.Organiza.Empresas)