Data visualisation for decision making under deep uncertainty
Current challenges and opportunities
Bibliographic Data
| ID | 15544882 |
|---|---|
| Authors | Antonia Hadjimichael (0000-0001-7330-6834, Pennsylvania State University, corresponding author), Julius Schlumberger (0000-0003-1837-2390, Deltares), Marjolijn Haasnoot (0000-0002-9062-4698, Utrecht University) |
| Year | 2024 |
| Volume | 19 |
| Issue | 11 |
| Pages | 111011-111011 |
| Publication date | 2024-10-10 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environmental Research Letters (JOURNAL) |
| Journal identifiers | ISSN: 1748-9326 • E-ISSN: 1748-9326 |
| Publisher | IOP Publishing (PUBLISHER • GB) |
| DOI | 10.1088/1748-9326/ad858b |
| OpenAlex | W4403288797 |
| Language | EN |
| Citations received | 1 |
| References cited | 31 |
This perspective article explores the role of data visualisation in decision-making under deep uncertainty (DMDU), a growing discipline tackling complex socio-environmental challenges, such as climate impacts and adaptation, natural resource management, and preparedness for extreme events. We discuss the role of visualisation for both analysis (or exploratory ) purposes, as well as communication (or explanatory ) purposes, including to stakeholders and the public. We identify a lack of comprehensive guidelines on how visualisations are currently used and their potential in enhancing DMDU processes. Drawing on literature and insights from a recent workshop, we identify key challenges DMDU analysts face when visualising data: managing complexity and dimensionality, effectively communicating uncertainty, and ensuring user engagement and interpretability. We propose a research agenda to address these challenges, by taxonomising and evaluating the effectiveness of different visual forms in decision-making contexts, and fostering interdisciplinary collaboration. We argue that, through these efforts, we can improve the communication and usability of DMDU analyses, ultimately aiding in more informed and adaptive decision-making in the face of deep uncertainty
Adaptation (eye · Data science · Human–computer interaction · Interpretability · Knowledge management · Management science · Political science · Preparedness · Usability · Visualization · Complex Network Analysis Techniques · Computer Science · Data Visualization and Analytics · Engineering · Mental Health Research Topics · Psychology · Artificial Intelligence
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |