Decision Making in Reinsurance with Induced OWA Operators and Minkowski Distances
Author
dc.contributor.author
Casanovas, Montserrat
Author
dc.contributor.author
Torres Martínez, Agustín
Author
dc.contributor.author
Merigó Lindahl, José
Admission date
dc.date.accessioned
2017-01-16T18:33:23Z
Available date
dc.date.available
2017-01-16T18:33:23Z
Publication date
dc.date.issued
2016
Cita de ítem
dc.identifier.citation
Cybernetics and Systems Volumen: 47 Número: 6 Páginas: 460-477 (2016)
es_ES
Identifier
dc.identifier.issn
0196-9722
Identifier
dc.identifier.uri
https://repositorio.uchile.cl/handle/2250/142463
Abstract
dc.description.abstract
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.