Covariance Logarithmic Aggregation Operators in Decision-Making Processes

Pérez-Romero, ME; Alfaro-García, VG; Merigó, JM; Flores-Romero, MB

Covariance Logarithmic Aggregation Operators in Decision-Making Processes

Pérez-Romero, ME Alfaro-García, VG Merigó, JM Flores-Romero, MB

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Publisher:: TAYLOR & FRANCIS INC
Publication Type:: Journal Article
Citation:: Cybernetics and Systems, 2023, 54, (2), pp. 220-238
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Pérez-Romero, ME
dc.contributor.author	Alfaro-García, VG
dc.contributor.author	Merigó, JM
dc.contributor.author	Flores-Romero, MB
dc.date.accessioned	2023-03-21T23:26:12Z
dc.date.available	2023-03-21T23:26:12Z
dc.date.issued	2023-01-01
dc.identifier.citation	Cybernetics and Systems, 2023, 54, (2), pp. 220-238
dc.identifier.issn	0196-9722
dc.identifier.issn	1087-6553
dc.identifier.uri	http://hdl.handle.net/10453/168002
dc.description.abstract	The objective of this study is to introduce some covariance ordered weighted logarithmic averaging (Cov-OWLA) operators. Covariance measures the conduct of one variable based on the behavior of another; this characteristic makes it key for decision-making processes, especially when applied in uncertain environments. We present some families and particular cases of the introduced operators, including their generalized, induced, and generalized induced formulations. An illustrative example using real-world data on tourism gross domestic product and homeland security indicators is proposed. The results show that there was a positive linear relation between the introduced variables. In this scenario, crime did not affect tourist activity, but tourist activity incited crime. The resulting aggregation with Cov-OWLA operators shows a generally better fit when compared to traditional methods.
dc.language	English
dc.publisher	TAYLOR & FRANCIS INC
dc.relation.ispartof	Cybernetics and Systems
dc.relation.isbasedon	10.1080/01969722.2022.2058692
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Covariance Logarithmic Aggregation Operators in Decision-Making Processes
dc.type	Journal Article
utslib.citation.volume	54
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-21T23:26:09Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	54
utslib.citation.issue	2

Abstract:

The objective of this study is to introduce some covariance ordered weighted logarithmic averaging (Cov-OWLA) operators. Covariance measures the conduct of one variable based on the behavior of another; this characteristic makes it key for decision-making processes, especially when applied in uncertain environments. We present some families and particular cases of the introduced operators, including their generalized, induced, and generalized induced formulations. An illustrative example using real-world data on tourism gross domestic product and homeland security indicators is proposed. The results show that there was a positive linear relation between the introduced variables. In this scenario, crime did not affect tourist activity, but tourist activity incited crime. The resulting aggregation with Cov-OWLA operators shows a generally better fit when compared to traditional methods.

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http://hdl.handle.net/10453/168002