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The Slicing Method: Determining Insensitivity Regions of Probability Weighting Functions
dc.contributor.author | Egozcue, Martin | |
dc.contributor.author | Fuentes García, Luis | |
dc.contributor.author | Zitikis, Ricardas | |
dc.date.accessioned | 2024-10-22T19:02:41Z | |
dc.date.available | 2024-10-22T19:02:41Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Egozcue, 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/S10614-022-10252-8 | es_ES |
dc.identifier.uri | http://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.sponsorship | The 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.sponsorship | Xunta de Galicia; ED431C 2018/41 | es_ES |
dc.description.sponsorship | Universidade da Coruña; GMNI-G000177 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Springer Nature | es_ES |
dc.relation | info: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 RENOVABLE | es_ES |
dc.relation.uri | https://doi.org/10.1007/S10614-022-10252-8 | es_ES |
dc.rights | This 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/S10614-022-10252-8 | es_ES |
dc.subject | Behavioural economics | es_ES |
dc.subject | Probability weighting function | es_ES |
dc.subject | Subadditivity | es_ES |
dc.subject | Likelihood insensitivity | es_ES |
dc.subject | Probabilistic insensitivity | es_ES |
dc.title | The Slicing Method: Determining Insensitivity Regions of Probability Weighting Functions | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Computational Economics | es_ES |
UDC.volume | 61 | es_ES |
UDC.issue | 4 | es_ES |
UDC.startPage | 1369 | es_ES |
UDC.endPage | 1402 | es_ES |
dc.identifier.doi | 10.1007/S10614-022-10252-8 | |
UDC.coleccion | Investigación | es_ES |
UDC.departamento | Matemáticas | es_ES |
UDC.grupoInv | Grupo de Métodos Numéricos en Enxeñaría (GMNI) | es_ES |
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