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Do You See What You Mean? Using Predictive Visualizations to Reduce Optimism in Duration Estimates

Authors :
Jansen, Yvonne
Koval, Morgane
Institut des Systèmes Intelligents et de Robotique (ISIR)
Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
Popular interaction with 3d content (Potioc)
Laboratoire Bordelais de Recherche en Informatique (LaBRI)
Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
ANR-19-CE33-0012,EMBER,Visualisations situées pour l'analyse de données personnelles(2019)
Source :
CHI 2022-Conference on Human Factors in Computing Systems, CHI 2022-Conference on Human Factors in Computing Systems, Apr 2022, New Orleans, United States. ⟨10.1145/3491102.3502010⟩
Publication Year :
2022
Publisher :
Open Science Framework, 2022.

Abstract

International audience; Making time estimates, such as how long a given task might take, frequently leads to inaccurate predictions because of an optimistic bias. Previous attempts to alleviate this bias, including decomposing the task into smaller components and listing potential surprises, have not shown any major improvement. This article builds on the premise that these procedures may have failed because they involve compound probabilities and mixture distributions which are difficult to compute in one's head. We hypothesize that predictive visualizations of such distributions would facilitate the estimation of task durations. We conducted a crowdsourced study in which 145 participants provided different estimates of overall and sub-task durations and we used these to generate predictive visualizations of the resulting mixture distributions. We compared participants' initial estimates with their updated ones and found compelling evidence that predictive visualizations encourage less optimistic estimates.

Details

Database :
OpenAIRE
Journal :
CHI 2022-Conference on Human Factors in Computing Systems, CHI 2022-Conference on Human Factors in Computing Systems, Apr 2022, New Orleans, United States. ⟨10.1145/3491102.3502010⟩
Accession number :
edsair.doi.dedup.....211f1d55653c3519f862917c94313bdc
Full Text :
https://doi.org/10.17605/osf.io/bwqkh