Abstractions for Visualizing Preferences in Group Decisions
Emily Hindalong, Jordon Johnson, Giuseppe Carenini, Tamara Munzner
摘要
Group decision making occurs when individuals collectively choose from a set of alternatives based on individual preferences. In these ubiquitous situations, it can be helpful for decision makers to visually model and compare stakeholder preferences in order to better understand others' points of view and reach consensus. Although a number of collaboration support tools allow preference inspection in some form, they are rarely based on a comprehensive understanding of the needs of group decision makers. The goal of our work is to study these demands, develop abstractions to model them, and create a framework to inform the design and assessment of existing and future tools. First, guided by decision analysis theory, we examine a diverse set of group decision making scenarios, characterizing variations in problem formulation, analysis goals, and situational features. Second, we amalgamate these findings into data and task abstractions that can be used to relate specific scenarios to the language of visualization. Finally, we use this framework to assess existing preference visualization tools in order to shed light on areas for future work in supporting group decision making.
问问这篇 Paper
问问你的智能体。
Lune 读过与它相关的顶会 Paper,每个回答都会注明依据哪几篇。
引用它的顶会 Paper2
- From Information to Choice: A Critical Inquiry Into Visualization Tools for Decision MakingEmre Oral, Ria Chawla, Michel Wijkstra, Narges Mahyar 等IEEE VIS 2023 · 被引用 31 次
- EchoMind: Supporting Real-time Complex Problem Discussions through Human-AI Collaborative FacilitationWeihao Chen, Chun Yu, Yukun Wang, Meizhu Chen 等CSCW 2025 · 被引用 5 次
相关 Paper
- A Critical Reflection on Visualization Research: Where Do Decision Making Tasks Hide?Evanthia Dimara, John T. StaskoIEEE VIS 2021 · 被引用 56 次
- Understanding Barriers to Network Exploration with Visualization: A Report from the TrenchesMashael AlKadi, Vanessa Serrano, James Scott-Brown, Catherine Plaisant 等IEEE VIS 2022 · 被引用 18 次
- Exploring the Robustness of the Effect of EVO on Intention Valuation Through ReplicationYesugen Baatartogtokh, Kaitlyn Cook, Alicia M. GrubbICSE 2025 · 被引用 1 次
- Towards Modeling Visualization Processes as Dynamic Bayesian NetworksChristian HeineIEEE VIS 2020 · 被引用 7 次
- Visualizing Urban Accessibility: Investigating Multi-Stakeholder Perspectives through a Map-based Design Probe StudyManaswi Saha, Siddhant Patil, Emily Cho, Evie Yu-Yen Cheng 等CHI 2022 · 被引用 17 次
