Prioritized Induced Heavy Operators Applied to Political Modelling

León-Castro, E; Perez-Arellano, LA; Olazabal-Lugo, M; Merigó, JM

Prioritized Induced Heavy Operators Applied to Political Modelling

León-Castro, E Perez-Arellano, LA Olazabal-Lugo, M Merigó, JM

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Publisher:: WORLD SCIENTIFIC PUBL CO PTE LTD
Publication Type:: Journal Article
Citation:: International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems, 2021, 29, (4), pp. 603-620
Issue Date:: 2021-08-01

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Field	Value	Language
dc.contributor.author	León-Castro, E
dc.contributor.author	Perez-Arellano, LA
dc.contributor.author	Olazabal-Lugo, M
dc.contributor.author	Merigó, JM
dc.date.accessioned	2022-06-06T06:07:48Z
dc.date.available	2022-06-06T06:07:48Z
dc.date.issued	2021-08-01
dc.identifier.citation	International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems, 2021, 29, (4), pp. 603-620
dc.identifier.issn	0218-4885
dc.identifier.issn	1793-6411
dc.identifier.uri	http://hdl.handle.net/10453/157973
dc.description.abstract	This paper presents the prioritized induced heavy ordered weighted average (PIHOWA) operator. This operator combines an unbounded weighting vector, an induced vector and a prioritized vector and can be applied to the group decision-making process where the information provided by each decision maker does not have the same importance. An application of this operator is done in governmental transparency in Mexico based on the Open Government Metric (OGM). Among the main results it is possible to visualize how the relative importance of each component can generate important change in the top 10 ranking.
dc.language	English
dc.publisher	WORLD SCIENTIFIC PUBL CO PTE LTD
dc.relation.ispartof	International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems
dc.relation.isbasedon	10.1142/S0218488521500264
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0802 Computation Theory and Mathematics
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Prioritized Induced Heavy Operators Applied to Political Modelling
dc.type	Journal Article
utslib.citation.volume	29
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0802 Computation Theory and Mathematics
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	2022-06-06T06:07:47Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	29
utslib.citation.issue	4

Abstract:

This paper presents the prioritized induced heavy ordered weighted average (PIHOWA) operator. This operator combines an unbounded weighting vector, an induced vector and a prioritized vector and can be applied to the group decision-making process where the information provided by each decision maker does not have the same importance. An application of this operator is done in governmental transparency in Mexico based on the Open Government Metric (OGM). Among the main results it is possible to visualize how the relative importance of each component can generate important change in the top 10 ranking.

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http://hdl.handle.net/10453/157973