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The Slicing Method: Determining Insensitivity Regions of Probability Weighting Functions

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 techniqu...

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Detalles Bibliográficos
Autores principales: Egozcue, Martín, García, Luis Fuentes, Zitikis, Ričardas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8972790/
https://www.ncbi.nlm.nih.gov/pubmed/35382141
http://dx.doi.org/10.1007/s10614-022-10252-8
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author Egozcue, Martín
García, Luis Fuentes
Zitikis, Ričardas
author_facet Egozcue, Martín
García, Luis Fuentes
Zitikis, Ričardas
author_sort Egozcue, Martín
collection PubMed
description 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.
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spelling pubmed-89727902022-04-01 The Slicing Method: Determining Insensitivity Regions of Probability Weighting Functions Egozcue, Martín García, Luis Fuentes Zitikis, Ričardas Comput Econ Article 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. Springer US 2022-04-01 2023 /pmc/articles/PMC8972790/ /pubmed/35382141 http://dx.doi.org/10.1007/s10614-022-10252-8 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Egozcue, Martín
García, Luis Fuentes
Zitikis, Ričardas
The Slicing Method: Determining Insensitivity Regions of Probability Weighting Functions
title The Slicing Method: Determining Insensitivity Regions of Probability Weighting Functions
title_full The Slicing Method: Determining Insensitivity Regions of Probability Weighting Functions
title_fullStr The Slicing Method: Determining Insensitivity Regions of Probability Weighting Functions
title_full_unstemmed The Slicing Method: Determining Insensitivity Regions of Probability Weighting Functions
title_short The Slicing Method: Determining Insensitivity Regions of Probability Weighting Functions
title_sort slicing method: determining insensitivity regions of probability weighting functions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8972790/
https://www.ncbi.nlm.nih.gov/pubmed/35382141
http://dx.doi.org/10.1007/s10614-022-10252-8
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