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


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.

Honorable Mention
Morgane Koval
CNRS, ISIR, Paris, France
Yvonne Jansen
Sorbonne Université, CNRS, ISIR, Paris, France


会議: CHI 2022

The ACM CHI Conference on Human Factors in Computing Systems (

セッション: Emotions & Communication in Visualizations

4 件の発表
2022-05-02 20:00:00
2022-05-02 21:15:00