International Journal of Computational Intelligence Systems

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Volume 14, Issue 1, 2021, Pages 208 - 227

A Novel Probability Weighting Function Model with Empirical Studies

Authors
Sheng Wu1, *, Hong-Wei Huang2, Yan-Lai Li3, Haodong Chen1, Yong Pan1
1School of E-commerce and Logistics Management, Henan University of Economics and Law, Zheng Zhou, China
2School of Mathematics, Zhengzhou Aeronautical Industry Management College, Zheng Zhou, China
3Business School, Liaoning University, Shen Yang, China
*Corresponding author. Email: wus_1988@163.com
Corresponding Author
Sheng Wu
Received 5 September 2020, Accepted 11 November 2020, Available Online 27 November 2020.
DOI
10.2991/ijcis.d.201120.001How to use a DOI?
Keywords
Probability weighting function; Decision-making under risk; Lagrange interpolation method; Risk preference; Preference points; Empirical studies
Abstract

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.

Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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<Previous Article In Issue
Next Article In Issue>
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
14 - 1
Pages
208 - 227
Publication Date
2020/11/27
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.201120.001How to use a DOI?
Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Sheng Wu
AU  - Hong-Wei Huang
AU  - Yan-Lai Li
AU  - Haodong Chen
AU  - Yong Pan
PY  - 2020
DA  - 2020/11/27
TI  - A Novel Probability Weighting Function Model with Empirical Studies
JO  - International Journal of Computational Intelligence Systems
SP  - 208
EP  - 227
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.201120.001
DO  - 10.2991/ijcis.d.201120.001
ID  - Wu2020
ER  -