The ordered weighted average in the theory of expertons

Linares-Mustarós, S; Ferrer-Comalat, JC; Corominas-Coll, D; Merigó, JM

The ordered weighted average in the theory of expertons

Linares-Mustarós, S Ferrer-Comalat, JC Corominas-Coll, D Merigó, JM

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Publication Type:: Journal Article
Citation:: International Journal of Intelligent Systems, 2019, 34 (3), pp. 345 - 365
Issue Date:: 2019-03-01

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Field	Value	Language
dc.contributor.author	Linares-Mustarós, S	en_US
dc.contributor.author	Ferrer-Comalat, JC	en_US
dc.contributor.author	Corominas-Coll, D	en_US
dc.contributor.author	Merigó, JM https://orcid.org/0000-0002-4672-6961	en_US
dc.date.issued	2019-03-01	en_US
dc.identifier.citation	International Journal of Intelligent Systems, 2019, 34 (3), pp. 345 - 365	en_US
dc.identifier.issn	0884-8173	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132269
dc.description.abstract	© 2018 Wiley Periodicals, Inc. This work presents a data-fusion mathematical object that incorporates the optimism level of a decision-making agent. The new fusion object is constructed by extending the ordered weighted averaging (OWA) operator in the process of creating an experton. The main advantage of this approach is that it can represent the attitudinal character of the decision maker in the construction of the experton. Therefore, this approach represents a new method for addressing multiperson problems by using optimistic and pessimistic perspectives. The work presents different practical examples based on the absolute hierarchical relationships of the “minimum of the bottom end of the intervals,” “minimum of the top end of the intervals,” and “minimum size of the interval.” The work also considers a wide range of particular cases of the OWA-experton, including the minimum experton, the maximum experton, the average experton, and the olympic experton. In addition, the study presents software for the calculation of OWA-expertons. Finally, the paper ends with an application in business decision-making regarding the calculation of expected benefits.	en_US
dc.relation.ispartof	International Journal of Intelligent Systems	en_US
dc.relation.isbasedon	10.1002/int.22055	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	The ordered weighted average in the theory of expertons	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	34	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1702 Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
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 Information, Systems and Modelling
utslib.copyright.status	closed_access	*
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	34	en_US

Abstract:

© 2018 Wiley Periodicals, Inc. This work presents a data-fusion mathematical object that incorporates the optimism level of a decision-making agent. The new fusion object is constructed by extending the ordered weighted averaging (OWA) operator in the process of creating an experton. The main advantage of this approach is that it can represent the attitudinal character of the decision maker in the construction of the experton. Therefore, this approach represents a new method for addressing multiperson problems by using optimistic and pessimistic perspectives. The work presents different practical examples based on the absolute hierarchical relationships of the “minimum of the bottom end of the intervals,” “minimum of the top end of the intervals,” and “minimum size of the interval.” The work also considers a wide range of particular cases of the OWA-experton, including the minimum experton, the maximum experton, the average experton, and the olympic experton. In addition, the study presents software for the calculation of OWA-expertons. Finally, the paper ends with an application in business decision-making regarding the calculation of expected benefits.

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http://hdl.handle.net/10453/132269