文献类型：期刊文献

英文题名：A Novel Probability Weighting Function Model with Empirical Studies

作者：Wu, Sheng[1];Huang, Hong-Wei[2];Li, Yan-Lai[3];Chen, Haodong[1];Pan, Yong[1]

第一作者：Wu, Sheng

通讯作者：Wu, S[1]

机构：[1]Henan Univ Econ & Law, Sch E Commerce & Logist Management, Zhengzhou, Peoples R China;[2]Zhengzhou Aeronaut Ind Management Coll, Sch Math, Zhengzhou, Peoples R China;[3]Liaoning Univ, Business Sch, Shenyang, Peoples R China

第一机构：河南财经政法大学电子商务与物流管理学院

通讯机构：[1]corresponding author), Henan Univ Econ & Law, Sch E Commerce & Logist Management, Zhengzhou, Peoples R China.|[104849]河南财经政法大学电子商务与物流管理学院;[10484]河南财经政法大学;

年份：2021

卷号：14

期号：1

起止页码：208-227

外文期刊名：INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS

收录：;EI(收录号：20210809968136);Scopus(收录号：2-s2.0-85101152476);WOS:【SSCI(收录号:WOS:000617705000001)，SCI-EXPANDED(收录号:WOS:000617705000001)】；

基金：This paper was partially supported by the progect of Researching on the "Internet plus" WEEE recovery cooperation model and government regulation combination in Henan Province, progect no. 202102310639.

语种：英文

外文关键词：Probability weighting function; Decision making under risk; Lagrange interpolation method; Risk preference; Preference points; Empirical studies

摘要：Probability weighting is one of the key components of the modern risky decision-making theories, an effective probability weight function can more accurately describe the decision-makers' subjective response to the event probability. While the probability weighting functions (PWFs) with several different parametric forms and parameter-free elicitation methods have been proposed. This paper first introduces a Lagrange interpolation method (LIM) for building a parameter-free PWF model, then proposes a novel PWF model with the use of the LIM based on Prelec's PWF model. Furthermore, an experiment was designed and carried out. The results not only demonstrate that the novel PWF model could reflect the empirical regularities for maximizing the satisfaction degree of the curve fitting for the preference points obtained from experiment or questionnaire survey and better predict the preferences of decision-makers, but also are found to be consistent with the properties of PWF. This paper makes a significant methodological contribution to developing a numerical method, such as LIM, for constructing the probability weighting model. The finial error analysis suggests that the novel PWF model is a more effective approach. (C) 2021 The Authors. Published by Atlantis Press B.V.