Reversed Description-Experience Gap

Date created: | Last Updated:

: DOI | ARK

Creating DOI. Please wait...

Create DOI

Category: Project

Description: Previous literature has suggested that risky choice patterns in general – and probability weighting in particular – are strikingly different in experience-based as compared to description-based formats. In two reanalyzes and three new experiments, we investigate differences between experience-based and description-based decisions using a parametric approach based on Cumulative Prospect Theory (CPT). Once controlling for sampling biases, we consistently find a reversal of the typical Description-Experience gap, that is, a reduced sensitivity to probabilities and increased overweighting of small probabilities in decisions from experience as compared to decisions from descriptions. This finding supports the hypothesis that regression to the mean effects in probability estimation are a crucial source of differences between both presentation formats. Further analyses identified task specific information asymmetry prevalent in gambles involving certainty as a third source of differences. We present a novel conceptualization of multiple independent sources of bias that contribute to the Description-Experience gap, namely sampling biases and task specific information asymmetry on the one hand, and regression to the mean effects in probability estimation on the other hand.

Files

Loading files...

Citation

Components

  • Data & analysis files


    Recent Activity

    Loading logs...

  • Paper


    Recent Activity

    Loading logs...

Recent Activity

Loading logs...

This website relies on cookies to help provide a better user experience. By clicking Accept or continuing to use the site, you agree. For more information, see our Privacy Policy and information on cookie use.
Accept
×

Start managing your projects on the OSF today.

Free and easy to use, the Open Science Framework supports the entire research lifecycle: planning, execution, reporting, archiving, and discovery.

Create an Account Learn More Hide this message