Quantile Judgments of Lognormal Losses: An Experimental Investigation

Sulian Wang () and Chen Wang ()
Additional contact information
Sulian Wang: Department of Industrial Engineering, Tsinghua University, 100084 Beijing, China
Chen Wang: Department of Industrial Engineering, Tsinghua University, 100084 Beijing, China

Decision Analysis, 2021, vol. 18, issue 1, 78-99

Abstract: The present study aims to investigate the quality of quantile judgments on a quantity of interest that follows the lognormal distribution, which is skewed and bounded from below with a long right tail. We conduct controlled experiments in which subjects predict the losses from a future typhoon based on losses from past typhoons. Our experiments find underconfidence of the 50% prediction intervals, which is primarily driven by overestimation of the 75th percentiles. We further perform exploratory analyses to disentangle sampling errors and judgmental biases in the overall miscalibration. Finally, we show that the correlations of log-transformed judgments between subjects are smaller than is justified by the information overlapping structure. It leads to overconfident aggregate predictions using the Bayes rule if we treat the low correlations as an indicator for independent information.

Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1287/deca.2020.0423 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ordeca:v:18:y:2021:i:1:p:78-99

Access Statistics for this article

More articles in Decision Analysis from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().