Goldstein-Einhorn加权函数的公理化

IF 2.2 4区心理学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Journal of Mathematical Psychology Pub Date : 2022-08-01 DOI:10.1016/j.jmp.2022.102669

Arnaldo Nascimento , Che Tat Ng

{"title":"Goldstein-Einhorn加权函数的公理化","authors":"Arnaldo Nascimento , Che Tat Ng","doi":"10.1016/j.jmp.2022.102669","DOIUrl":null,"url":null,"abstract":"<div><p>In 1999, Richard Gonzalez and George Wu proposed an axiomatization for the widely used Goldstein–Einhorn probability weighting functions. Our present study analyzes the preference conditions in the axioms, leading to the discovery of a larger family of weighting functions. Furthermore, we present a new preference condition which is necessary and sufficient for the Goldstein–Einhorn weighting functions.</p></div>","PeriodicalId":50140,"journal":{"name":"Journal of Mathematical Psychology","volume":"109 ","pages":"Article 102669"},"PeriodicalIF":2.2000,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An axiomatization of the Goldstein–Einhorn weighting functions\",\"authors\":\"Arnaldo Nascimento , Che Tat Ng\",\"doi\":\"10.1016/j.jmp.2022.102669\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In 1999, Richard Gonzalez and George Wu proposed an axiomatization for the widely used Goldstein–Einhorn probability weighting functions. Our present study analyzes the preference conditions in the axioms, leading to the discovery of a larger family of weighting functions. Furthermore, we present a new preference condition which is necessary and sufficient for the Goldstein–Einhorn weighting functions.</p></div>\",\"PeriodicalId\":50140,\"journal\":{\"name\":\"Journal of Mathematical Psychology\",\"volume\":\"109 \",\"pages\":\"Article 102669\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2022-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Mathematical Psychology\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0022249622000220\",\"RegionNum\":4,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Mathematical Psychology","FirstCategoryId":"102","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0022249622000220","RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}

引用次数: 0

摘要

1999年，Richard Gonzalez和George Wu为广泛使用的Goldstein-Einhorn概率加权函数提出了一个公理化。我们目前的研究分析了公理中的偏好条件，从而发现了更大的加权函数族。进一步给出了Goldstein-Einhorn加权函数的一个新的充分必要条件。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

An axiomatization of the Goldstein–Einhorn weighting functions

In 1999, Richard Gonzalez and George Wu proposed an axiomatization for the widely used Goldstein–Einhorn probability weighting functions. Our present study analyzes the preference conditions in the axioms, leading to the discovery of a larger family of weighting functions. Furthermore, we present a new preference condition which is necessary and sufficient for the Goldstein–Einhorn weighting functions.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Journal of Mathematical Psychology 医学-数学跨学科应用

CiteScore

3.70

自引率

11.10%

发文量

审稿时长

20.2 weeks

期刊介绍： The Journal of Mathematical Psychology includes articles, monographs and reviews, notes and commentaries, and book reviews in all areas of mathematical psychology. Empirical and theoretical contributions are equally welcome. Areas of special interest include, but are not limited to, fundamental measurement and psychological process models, such as those based upon neural network or information processing concepts. A partial listing of substantive areas covered include sensation and perception, psychophysics, learning and memory, problem solving, judgment and decision-making, and motivation. The Journal of Mathematical Psychology is affiliated with the Society for Mathematical Psychology. Research Areas include: • Models for sensation and perception, learning, memory and thinking • Fundamental measurement and scaling • Decision making • Neural modeling and networks • Psychophysics and signal detection • Neuropsychological theories • Psycholinguistics • Motivational dynamics • Animal behavior • Psychometric theory