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dc.contributor.authorEgozcue, Martin
dc.contributor.authorFuentes García, Luis
dc.contributor.authorZitikis, Ricardas
dc.date.accessioned2024-10-22T19:02:41Z
dc.date.available2024-10-22T19:02:41Z
dc.date.issued2022
dc.identifier.citationEgozcue, M., Fuentes García, L., & Zitikis, R. (2023). The Slicing Method: Determining Insensitivity Regions of Probability Weighting Functions. Computational Economics, 61(4), 1369-1402. https://doi.org/10.1007/S10614-022-10252-8es_ES
dc.identifier.urihttp://hdl.handle.net/2183/39739
dc.description.abstract[Abstract:] A popular rule of thumb, usually called “heuristic technique” in Behavioral Economics, for determining the likelihood insensitivity regions of probability weighting functions (pwf’s) is based on searching for points at which the pwf’s are twice their values at half the points. Although this technique works remarkably well for many commonly used pwf’s, it sometimes fails to provide the correct answer. In order to cover the class of pwf’s for which the heuristic technique does not work, in this paper we propose, discuss, and illustrate an extension of the technique into what we call the “slicing method,” which is capable of finding the subadditivity and insensitivity regions of any continuous pwf.es_ES
dc.description.sponsorshipThe research has been supported by the National Agency for Research and Innovation (ANII) of Uruguay, the Natural Sciences and Engineering Research Council (NSERC) of Canada, and the national research organization Mathematics of Information Technology and Complex Systems (MITACS) of Canada. This work has also been partially supported by the Ministerio de Ciencia, Innovación y Universidades (grant #RTI2018-093366-B-I00) of the Spanish Government and by the Consellería de Educación e Ordenación Universitaria of the Xunta de Galicia (grant #ED431C 2018/41), cofinanced by the Universidade da Coruña (#GMNI-G000177).es_ES
dc.description.sponsorshipXunta de Galicia; ED431C 2018/41es_ES
dc.description.sponsorshipUniversidade da Coruña; GMNI-G000177es_ES
dc.language.isoenges_ES
dc.publisherSpringer Naturees_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-093366-B-I00/ES/NUEVOS METODOS SIN MALLA PARA LA SIMULACION NUMERICA DE FLUJOS TURBULENTOS Y PROBLEMAS DE MULTIFISICA. APLICACION AL DESARROLLO DE SISTEMAS DE GENERACION DE ENERGIA RENOVABLEes_ES
dc.relation.urihttps://doi.org/10.1007/S10614-022-10252-8es_ES
dc.rightsThis version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/S10614-022-10252-8es_ES
dc.subjectBehavioural economicses_ES
dc.subjectProbability weighting functiones_ES
dc.subjectSubadditivityes_ES
dc.subjectLikelihood insensitivityes_ES
dc.subjectProbabilistic insensitivityes_ES
dc.titleThe Slicing Method: Determining Insensitivity Regions of Probability Weighting Functionses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleComputational Economicses_ES
UDC.volume61es_ES
UDC.issue4es_ES
UDC.startPage1369es_ES
UDC.endPage1402es_ES
dc.identifier.doi10.1007/S10614-022-10252-8
UDC.coleccionInvestigaciónes_ES
UDC.departamentoMatemáticases_ES
UDC.grupoInvGrupo de Métodos Numéricos en Enxeñaría (GMNI)es_ES


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