Nonparametric numerical approaches to probability weighting function construct for manifestation and prediction of risk preferences

Sheng Wu, Zhen-Song Chen*, Witold Pedrycz, Kannan Govindan, Kwai-Sang Chin

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

15 Downloads (Pure)


Probability weighting function (PWF) is the psychological probability of a decision-maker for objective probability, which reflects and predicts the risk preferences of decision-maker in behavioral decisionmaking. The existing approaches to PWF estimation generally include parametric methodologies to PWF construction and nonparametric elicitation of PWF. However, few of them explores the combination of parametric and nonparametric elicitation approaches to approximate PWF. To describe quantitatively risk preferences, the Newton interpolation, as a well-established mathematical approximation approach, is introduced to task-specifically match PWF under the frameworks of prospect theory and cumulative prospect theory with descriptive psychological analyses. The Newton interpolation serves as a nonparametric numerical approach to the estimation of PWF by fitting experimental preference points without imposing any specific parametric form assumptions. The elaborated nonparametric PWF model varies in accordance with the number of the experimental preference points elicitation in terms of its functional form. The introduction of Newton interpolation to PWF estimation into decision-making under risk will benefit to reflect and predict the risk preferences of decision-makers both at the aggregate and individual levels. The Newton interpolation-based nonparametric PWF model exhibits an inverse S-shaped PWF and obeys the fourfold pattern of decision-makers’ risk preferences as suggested by previous empirical analyses. First published online 17 April 2023
Original languageEnglish
JournalTechnological and Economic Development of Economy
Issue number4
Pages (from-to)1127–1167
Publication statusPublished - 14. Jul 2023


  • Newton inter¬polation
  • decision-making under risk
  • nonparametric numerical approach
  • preference points
  • probability weighting function
  • risk preference


Dive into the research topics of 'Nonparametric numerical approaches to probability weighting function construct for manifestation and prediction of risk preferences'. Together they form a unique fingerprint.

Cite this