Quantile Judgments of Lognormal Losses: An Experimental Investigation

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.

Counterpoint

Claims about alterations in perception based on manipulations of the energetics hypothesis (and other influences) are often framed as interesting specifically because they affect our perceptual experience. Many control experiments conducted on such perceptual effects suggest, however, that they are the result of attribution effects and other kinds of judgmental biases influencing the reporting process rather than perception itself. Schnall (2017, this issue), appealing to Heider’s work on attribution, argues that it is fruitless to try to distinguish between perception and attribution. This makes the energetics hypothesis less interesting.