Decision Making in Reinsurance with Induced OWA Operators and Minkowski Distances

Casanovas, M; Torres-Martínez, A; Merigó, JM

Decision Making in Reinsurance with Induced OWA Operators and Minkowski Distances

Casanovas, M Torres-Martínez, A Merigó, JM

Permalink

Publisher:: TAYLOR & FRANCIS INC
Publication Type:: Journal Article
Citation:: Cybernetics and Systems, 2016, 47, (6), pp. 460-477
Issue Date:: 2016-08-17

Closed Access

	Filename	Description	Size
	CS 2017 - Montse-Agustin.pdf	Published version	984.82 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Casanovas, M
dc.contributor.author	Torres-Martínez, A
dc.contributor.author	Merigó, JM
dc.date.accessioned	2021-02-23T12:22:14Z
dc.date.available	2021-02-23T12:22:14Z
dc.date.issued	2016-08-17
dc.identifier.citation	Cybernetics and Systems, 2016, 47, (6), pp. 460-477
dc.identifier.issn	0196-9722
dc.identifier.issn	1087-6553
dc.identifier.uri	http://hdl.handle.net/10453/146359
dc.description.abstract	© 2016, Copyright © Taylor & Francis Group, LLC. The decision to choose a reinsurance program has many complexities because it is difficult to simultaneously achieve high levels in different optimal criteria including maximum gain, minimum variance, and probability of ruin. This article suggests a new method by which, through membership functions, we can measure the distance of each alternative to an optimal result and aggregate it by using different types of aggregations. In this article, particular attention is given to the induced Minkowski ordered weighted averaging distance operator and the induced Minkowski probabilistic ordered weighted averaging distance operator. The main advantage of these operators is that they include a wide range of special cases. Thus, they can adapt efficiently to the specific needs of the calculation processes. By doing so, the reinsurance system can make better decisions by using different scenarios in the uncertain environment considered.
dc.language	English
dc.publisher	TAYLOR & FRANCIS INC
dc.relation.ispartof	Cybernetics and Systems
dc.relation.isbasedon	10.1080/01969722.2016.1206767
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	Decision Making in Reinsurance with Induced OWA Operators and Minkowski Distances
dc.type	Journal Article
utslib.citation.volume	47
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	1702 Cognitive Sciences
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
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2021-02-23T12:22:12Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	47
utslib.citation.issue	6

Abstract:

© 2016, Copyright © Taylor & Francis Group, LLC. The decision to choose a reinsurance program has many complexities because it is difficult to simultaneously achieve high levels in different optimal criteria including maximum gain, minimum variance, and probability of ruin. This article suggests a new method by which, through membership functions, we can measure the distance of each alternative to an optimal result and aggregate it by using different types of aggregations. In this article, particular attention is given to the induced Minkowski ordered weighted averaging distance operator and the induced Minkowski probabilistic ordered weighted averaging distance operator. The main advantage of these operators is that they include a wide range of special cases. Thus, they can adapt efficiently to the specific needs of the calculation processes. By doing so, the reinsurance system can make better decisions by using different scenarios in the uncertain environment considered.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/146359