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Flexible Utility Function Approximation via Cubic Bezier Splines
In intertemporal and risky choice decisions, parametric utility models are widely used for predicting choice and measuring individuals’ impulsivity and risk aversion. However, parametric utility models cannot describe data deviating from their assumed functional form. We propose a novel method using...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7599200/ https://www.ncbi.nlm.nih.gov/pubmed/32979183 http://dx.doi.org/10.1007/s11336-020-09723-4 |
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author | Lee, Sangil Glaze, Chris M. Bradlow, Eric T. Kable, Joseph W. |
author_facet | Lee, Sangil Glaze, Chris M. Bradlow, Eric T. Kable, Joseph W. |
author_sort | Lee, Sangil |
collection | PubMed |
description | In intertemporal and risky choice decisions, parametric utility models are widely used for predicting choice and measuring individuals’ impulsivity and risk aversion. However, parametric utility models cannot describe data deviating from their assumed functional form. We propose a novel method using cubic Bezier splines (CBS) to flexibly model smooth and monotonic utility functions that can be fit to any dataset. CBS shows higher descriptive and predictive accuracy over extant parametric models and can identify common yet novel patterns of behavior that are inconsistent with extant parametric models. Furthermore, CBS provides measures of impulsivity and risk aversion that do not depend on parametric model assumptions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11336-020-09723-4) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-7599200 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-75992002020-11-10 Flexible Utility Function Approximation via Cubic Bezier Splines Lee, Sangil Glaze, Chris M. Bradlow, Eric T. Kable, Joseph W. Psychometrika Theory and Methods In intertemporal and risky choice decisions, parametric utility models are widely used for predicting choice and measuring individuals’ impulsivity and risk aversion. However, parametric utility models cannot describe data deviating from their assumed functional form. We propose a novel method using cubic Bezier splines (CBS) to flexibly model smooth and monotonic utility functions that can be fit to any dataset. CBS shows higher descriptive and predictive accuracy over extant parametric models and can identify common yet novel patterns of behavior that are inconsistent with extant parametric models. Furthermore, CBS provides measures of impulsivity and risk aversion that do not depend on parametric model assumptions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11336-020-09723-4) contains supplementary material, which is available to authorized users. Springer US 2020-09-26 2020 /pmc/articles/PMC7599200/ /pubmed/32979183 http://dx.doi.org/10.1007/s11336-020-09723-4 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Theory and Methods Lee, Sangil Glaze, Chris M. Bradlow, Eric T. Kable, Joseph W. Flexible Utility Function Approximation via Cubic Bezier Splines |
title | Flexible Utility Function Approximation via Cubic Bezier Splines |
title_full | Flexible Utility Function Approximation via Cubic Bezier Splines |
title_fullStr | Flexible Utility Function Approximation via Cubic Bezier Splines |
title_full_unstemmed | Flexible Utility Function Approximation via Cubic Bezier Splines |
title_short | Flexible Utility Function Approximation via Cubic Bezier Splines |
title_sort | flexible utility function approximation via cubic bezier splines |
topic | Theory and Methods |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7599200/ https://www.ncbi.nlm.nih.gov/pubmed/32979183 http://dx.doi.org/10.1007/s11336-020-09723-4 |
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