Do You See What You Mean? Using Predictive Visualizations to Reduce Optimism in Duration Estimates
Morgane Koval, Yvonne Jansen
摘要
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.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了最后一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper3
- Swaying the Public? Impacts of Election Forecast Visualizations on Emotion, Trust, and Intention in the 2022 U.S. MidtermsFumeng Yang, Mandi Cai, Chloe Mortenson, Hoda Fakhari 等IEEE VIS 2023 · 被引用 30 次
- How Do HCI Researchers Study Cognitive Biases? A Scoping ReviewNattapat Boonprakong, Benjamin Tag, Jorge Gonçalves, Tilman DinglerCHI 2025 · 被引用 19 次
- Designing Resource Allocation Tools to Promote Fair Allocation: Do Visualization and Information Framing Matter?Arnav Verma, Luiz Augusto de Macêdo Morais, Pierre Dragicevic, Fanny ChevalierCHI 2023 · 被引用 7 次
它引用的顶会 Paper2
相关 Paper
- Playing Planning Poker in Crowds: Human Computation of Software Effort EstimatesMohammed Alhamed, Tim StorerICSE 2021 · 被引用 18 次
- To Plan or Not to Plan? A Mixed-Methods Diary Study Examining When, How and Why Knowledge Work Planning is InaccurateYoana Ahmetoglu, Duncan P. Brumby, Anna L. CoxCSCW 2020 · 被引用 21 次
- Evaluating the Use of Uncertainty Visualisations for Imputations of Data Missing At Random in ScatterplotsAbhraneel Sarma, Shunan Guo, Jane Hoffswell, Ryan A. Rossi 等IEEE VIS 2022 · 被引用 19 次
- My Model is Unfair, Do People Even Care? Visual Design Affects Trust and Perceived Bias in Machine LearningAimen Gaba, Zhanna Kaufman, Jason Cheung, Marie Shvakel 等IEEE VIS 2023 · 被引用 20 次
- Revealing Perceptual Proxies with Adversarial ExamplesBrian D. Ondov, Fumeng Yang, Matthew Kay, Niklas Elmqvist 等IEEE VIS 2020 · 被引用 22 次
