Abstract
In this paper we study the interval scoring rule as a mechanism to elicit subjective beliefs
under varying degrees of uncertainty. In our experiment, subjects forecast the termination
time of a time series to be generated from a given but unknown stochastic process. Subjects
gradually learn more about the underlying process over time and hence the true distribution
over termination times. We conduct two treatments, one with a high and one with a low volatility
process. We find that elicited intervals are better when subjects are facing a low volatility
process. In this treatment, participants learn to position their intervals almost optimally
over the course of the experiment. This is in contrast with the high volatility treatment, where
subjects, over the course of the experiment, learn to optimize the location of their intervals
but fail to provide the optimal length.
under varying degrees of uncertainty. In our experiment, subjects forecast the termination
time of a time series to be generated from a given but unknown stochastic process. Subjects
gradually learn more about the underlying process over time and hence the true distribution
over termination times. We conduct two treatments, one with a high and one with a low volatility
process. We find that elicited intervals are better when subjects are facing a low volatility
process. In this treatment, participants learn to position their intervals almost optimally
over the course of the experiment. This is in contrast with the high volatility treatment, where
subjects, over the course of the experiment, learn to optimize the location of their intervals
but fail to provide the optimal length.
| Original language | English |
|---|---|
| Article number | e0175163 |
| Pages (from-to) | 1-15 |
| Number of pages | 15 |
| Journal | PLoS ONE |
| Volume | 12 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2017 |