Computer Science > Human-Computer Interaction
[Submitted on 12 Apr 2024 (v1), last revised 22 Apr 2024 (this version, v2)]
Title:A Typology of Decision-Making Tasks for Visualization
View PDF HTML (experimental)Abstract:Despite decision-making being a vital goal of data visualization, little work has been done to differentiate the decision-making tasks within our field. While visualization task taxonomies and typologies exist, they are often too granular for describing complex decision goals and decision-making processes, thus limiting their potential use in designing decision-support tools. In this paper, we contribute a typology of decision-making tasks that were iteratively refined from a list of design goals distilled from a literature review. Our typology is concise and consists of only three tasks: choose, activate, and create. Originally proposed by the scientific community, we extend and provide definitions for these tasks that are suitable for the visualization community. Our proposed typology offers two benefits. First, it facilitates the composition of decisions using these three tasks, allowing for flexible and clear descriptions across varying complexities and domains. Second, diagrams created using this typology encourage productive discourse between visualization designers and domain experts by abstracting the intricacies of data, thereby promoting clarity and rigorous analysis of decision-making processes. We motivate the use of our typology through four case studies and demonstrate the benefits of our approach through semi-structured interviews conducted with experienced members of the visualization community, comprising academic and industry experts, who have contributed to developing or publishing decision support systems for domain experts. Our interviewees composed diagrams using our typology to delineate the decision-making processes that drive their decision-support tools, demonstrating its descriptive capacity and effectiveness.
Submission history
From: Camelia Daniela Brumar [view email][v1] Fri, 12 Apr 2024 21:05:16 UTC (6,304 KB)
[v2] Mon, 22 Apr 2024 16:03:33 UTC (6,304 KB)
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