The Slicing Method: Determining Insensitivity Regions of Probability Weighting Functions

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The Slicing Method: Determining Insensitivity Regions of Probability Weighting FunctionsData
2022Cita bibliográfica
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
Resumo
[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.
Palabras chave
Behavioural economics
Probability weighting function
Subadditivity
Likelihood insensitivity
Probabilistic insensitivity
Probability weighting function
Subadditivity
Likelihood insensitivity
Probabilistic insensitivity
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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