Nonlinear Decision Weights or Moment-Based Preferences? A Model Competition Involving Described and Experienced Skewness

34 Pages Posted: 13 Aug 2015 Last revised: 17 May 2017

See all articles by Leonidas Spiliopoulos

Leonidas Spiliopoulos

Max Planck Society for the Advancement of the Sciences - Max Planck Institute for Human Development

Ralph Hertwig

Max Planck Society for the Advancement of the Sciences - Max Planck Institute for Human Development

Date Written: May 16, 2017

Abstract

The predictive power of Cumulative Prospect Theory (CPT) and Expected Utility Theory (EUT) is typically compared using decisions from description (DfD), wherein lotteries’ outcome values and probabilities are explicitly stated. In decisions from experience (DfE), individuals sample (without cost) from the return distributions to learn outcome values and their relative frequencies; therein CPT and EUT require the calculation of probabilities from experience. Individuals, however, may be more attuned to the experienced moments of outcome distributions. We therefore test the Mean-Variance-Skewness (MVS) model, and estimate the proportion of EUT (over income), CPT and MVS populations using a latent-class hierarchical Bayesian model across six large datasets. For simple lotteries (with 1-2 outcomes), we find a mixture of CPT and MVS populations in both DfD and DfE. For more complex lotteries (with 2-3 outcomes), all participants are classified as CPT types in DfD, but as MVS types in DfE. This suggests that in decisions from experience with more complex return distributions, preferences for skewness are more predictive than nonlinear probability weighting.

Keywords: risk, uncertainty, moment-based preferences, prospect theory, mixture models, Bayesian hierarchical estimation, decisions from description, decisions from experience

JEL Classification: C11, C52, C91, D81, D83

Suggested Citation

Spiliopoulos, Leonidas and Hertwig, Ralph, Nonlinear Decision Weights or Moment-Based Preferences? A Model Competition Involving Described and Experienced Skewness (May 16, 2017). Available at SSRN: https://ssrn.com/abstract=2642829 or http://dx.doi.org/10.2139/ssrn.2642829

Leonidas Spiliopoulos (Contact Author)

Max Planck Society for the Advancement of the Sciences - Max Planck Institute for Human Development ( email )

Lentzeallee 94
D-14195 Berlin, 14195
Germany

Ralph Hertwig

Max Planck Society for the Advancement of the Sciences - Max Planck Institute for Human Development ( email )

Lentzeallee 94
D-14195 Berlin, 14195
Germany

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
118
Abstract Views
1,141
rank
320,435
PlumX Metrics