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Comparing input interfaces to elicit belief distributions

Author

Listed:
  • Paolo Crosetto

    (GAEL - Laboratoire d'Economie Appliquée de Grenoble - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes)

  • Thomas de Haan

    (UiB - University of Bergen)

Abstract

This paper introduces a new software interface to elicit belief distributions of any shape: Click-and-Drag . The interface was tested against the state of the art in the experimental literature—a text-based interface and multiple sliders—and in the online forecasting industry—a distribution-manipulation interface similar to the one used by the most popular crowd-forecasting website. By means of a pre-registered experiment on Amazon Mechanical Turk, quantitative data on the accuracy of reported beliefs in a series of induced-value scenarios varying by granularity, shape, and time constraints, as well as subjective data on user experience were collected. Click-and-Drag outperformed all other interfaces by accuracy and speed, and was self-reported as being more intuitive and less frustrating, confirming the pre-registered hypothesis. Aside of the pre-registered results, Click-and-Drag generated the least drop-out rate from the task, and scored best in a sentiment analysis of an open-ended general question. Further, the interface was used to collect homegrown predictions on temperature in New York City in 2022 and 2042. Click-and-Drag elicited distributions were smoother with less idiosyncratic spikes. Free and open source, ready to use oTree, Qualtrics and Limesurvey plugins for Click-and-Drag, and all other tested interfaces are available at https://beliefelicitation.github.io/ .

Suggested Citation

  • Paolo Crosetto & Thomas de Haan, 2023. "Comparing input interfaces to elicit belief distributions," Post-Print hal-04263759, HAL.
    • Handle: RePEc:hal:journl:hal-04263759
    DOI: 10.1017/jdm.2023.21
    Note: View the original document on HAL open archive server: https://hal.science/hal-04263759v1
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    Cited by:

    1. Becker, Christoph & Duersch, Peter & Eife, Thomas, 2023. "Measuring Inflation Expectations: How the Response Scale Shapes Density Forecasts," Working Papers 0727, University of Heidelberg, Department of Economics.
    2. Pedro Gonzalez-Fernandez, 2024. "Belief Bias Identification," Papers 2404.09297, arXiv.org, revised Nov 2024.
    3. Gonzalez-Fernandez, Pedro & Bosch-Rosa, Ciril & Meissner, Thomas, 2025. "Direct Elicitation of Parametric Belief Distributions: An application to inflation expectations," Journal of Economic Behavior & Organization, Elsevier, vol. 233(C).

    More about this item

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    JEL classification:

    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior

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