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dc.contributor.authorGonzález Arteaga, María Teresa 
dc.contributor.authorAndrés Calle, Rocío de
dc.contributor.authorPeral, Marta
dc.date.accessioned2020-02-27T13:09:20Z
dc.date.available2020-02-27T13:09:20Z
dc.date.issued2017
dc.identifier.citationProgress in Artifitial Intelligence, 2017, vol. 6, p. 235–244.es
dc.identifier.issn2192-6352es
dc.identifier.urihttp://uvadoc.uva.es/handle/10324/40549
dc.description.abstractThis work introduces a non-traditional perspective about the problem of measuring the stability of agents’ preferences. Specifically, the cohesiveness of preferences at different moments of time is explored under the assumption of considering dichotomous evaluations. The general concept of time cohesiveness measure is introduced as well as a particular formulation based on the consideration of any two successive moments of time, the sequential time cohesiveness measure. Moreover, some properties of the novel measure are also provided. Finally, and in order to emphasize the adaptability of our proposal to real situations, a factual case of study about clinical decision-making is presented. Concretely, the study of preference stability for life-sustaining treatments of patients with advanced cancer at end of life is analysed. The research considers patients who express their opinions on three life-sustaining treatments at four consecutive periods of time. The novel measure provides information of patients preference stability along time and considers the possibility of cancer metastaseses
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.publisherSpringeres
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.subject.classificationTime cohesiveness measure. Dichotomous opinions. Preference stability. Patients’ preferenceses
dc.titlePreference stability along time: the time cohesiveness measurees
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holderSpringeres
dc.identifier.doi10.1007/s13748-017-0119-3es
dc.relation.publisherversionhttps://doi.org/10.1007/s13748-017-0119-3es
dc.identifier.publicationfirstpage235es
dc.identifier.publicationissue3es
dc.identifier.publicationlastpage244es
dc.identifier.publicationtitleProgress in Artificial Intelligencees
dc.identifier.publicationvolume6es
dc.peerreviewedSIes
dc.description.projectEste trabajo forma parte del proyecto de investigación con financiación nacional: MEC-FEDER Grant ECO2016-77900-Pes
dc.identifier.essn2192-6360es
dc.type.hasVersioninfo:eu-repo/semantics/draftes


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