dc.contributor.author | Crosetto, Paolo | |
dc.contributor.author | de Haan, Thomas | |
dc.date.accessioned | 2023-09-19T12:10:59Z | |
dc.date.available | 2023-09-19T12:10:59Z | |
dc.date.created | 2023-09-17T14:15:12Z | |
dc.date.issued | 2023 | |
dc.identifier.issn | 1930-2975 | |
dc.identifier.uri | https://hdl.handle.net/11250/3090450 | |
dc.description.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/. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Cambridge University Press | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Comparing input interfaces to elicit belief distributions | en_US |
dc.type | Journal article | en_US |
dc.type | Peer reviewed | en_US |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | Copyright 2023 The Author(s) | en_US |
dc.source.articlenumber | e27 | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |
dc.identifier.doi | https://doi.org/10.1017/jdm.2023.21 | |
dc.identifier.cristin | 2179835 | |
dc.source.journal | Judgment and decision making | en_US |
dc.identifier.citation | Judgment and decision making. 2023, 18, e27. | en_US |
dc.source.volume | 18 | en_US |