From Information to Choice: A Critical Inquiry Into Visualization Tools for Decision Making
Oral, E., Chawla, R., Wijkstra, M. , Mahyar, N.
ORCID: 0000-0003-1781-0029 & Dimara, E. (2024).
From Information to Choice: A Critical Inquiry Into Visualization Tools for Decision Making.
IEEE Transactions on Visualization and Computer Graphics, 30(1),
doi: 10.1109/tvcg.2023.3326593
Abstract
In the face of complex decisions, people often engage in a three-stage process that spans from (1) exploring and analyzing pertinent information (intelligence); (2) generating and exploring alternative options (design); and ultimately culminating in (3) selecting the optimal decision by evaluating discerning criteria (choice). We can fairly assume that all good visualizations aid in the 'intelligence' stage by enabling data exploration and analysis. Yet, to what degree and how do visualization systems currently support the other decision making stages, namely 'design' and 'choice'? To further explore this question, we conducted a comprehensive review of decision-focused visualization tools by examining publications in major visualization journals and conferences, including VIS, EuroVis, and CHI, spanning all available years. We employed a deductive coding method and in-depth analysis to assess whether and how visualization tools support design and choice. Specifically, we examined each visualization tool by (i) its degree of visibility for displaying decision alternatives, criteria, and preferences, and (ii) its degree of flexibility for offering means to manipulate the decision alternatives, criteria, and preferences with interactions such as adding, modifying, changing mapping, and filtering. Our review highlights the opportunities and challenges that decision-focused visualization tools face in realizing their full potential to support all stages of the decision making process. It reveals a surprising scarcity of tools that support all stages, and while most tools excel in offering visibility for decision criteria and alternatives, the degree of flexibility to manipulate these elements is often limited, and the lack of tools that accommodate decision preferences and their elicitation is notable. Based on our findings, to better support the choice stage, future research could explore enhancing flexibility levels and variety, exploring novel visualization paradigms, increasing algorithmic support, and ensuring that this automation is user-controlled via the enhanced flexibility I evels. Our curated list of the 88 surveyed visualization tools is available in the OSF link (https://osf.io/nrasz/?view_only=b92a90a34ae241449b5f2cd33383bfcb).
| Publication Type: | Article |
|---|---|
| Additional Information: | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Publisher Keywords: | Decision making, Data visualization, MCDM, Urban areas, Surveys, Task analysis, Filtering |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Departments: | School of Science & Technology School of Science & Technology > Department of Computer Science |
| SWORD Depositor: |
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