scholarly journals Editorial: Uncertainty Visualization and Decision Making

2021 ◽  
Vol 3 ◽  
Author(s):  
Roberto Theron ◽  
Lace M. Padilla
Keyword(s):  
Decision Making ◽  
Uncertainty Visualization

scholarly journals Effects of Uncertainty Visualization on Map-Based Decision Making Under Time Pressure

2020 ◽  
Vol 2 ◽  
Author(s):  
Michelle Korporaal ◽  
Ian T. Ruginski ◽  
Sara Irina Fabrikant
Keyword(s):  
Decision Making ◽  
Time Pressure ◽  
Uncertainty Visualization

scholarly journals Special issue introduction: Approaching spatial uncertainty visualization to support reasoning and decision making

2016 ◽  
Vol 16 (2) ◽  
pp. 97-105 ◽  
Author(s):  
Jennifer Smith Mason ◽  
Alexander Klippel ◽  
Susanne Bleisch ◽  
Aidan Slingsby ◽  
Stephanie Deitrick
Keyword(s):  
Decision Making ◽  
Spatial Uncertainty ◽  
Special Issue ◽  
Uncertainty Visualization

Uncertainty Visualizations

2012 ◽  
Vol 6 (1) ◽  
pp. 30-56 ◽  
Author(s):  
Xiao Dong ◽  
Caroline C. Hayes
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Makers ◽  
Uncertainty Visualization ◽  
Real Work ◽  
Decision Choices ◽  
Design Projects ◽  
Independent Decision ◽  
Alternative Designs ◽  
Domain Independent

Uncertainty is inherent in all real work contexts; it creates ambiguities that make decision making difficult. To help decision makers manage ambiguity, the authors developed and evaluated a domain-independent decision support system (DSS), the Uncertainty DSS. It is designed to help decision makers recognize situations in which uncertainty creates ambiguity in their choices and to identify information that can help reduce that ambiguity. It does so by providing an uncertainty visualization, which shows when the range of possible values for two or more alternatives overlap, indicating that one cannot identify the best alternative given the current information. To evaluate the Uncertainty DSS, the authors created a pared-down version, the Certainty DSS, which provides no uncertainty visualizations. They recruited 22 engineering designers and asked them compare alternative designs from real, ongoing design projects using no DSS, the Certainty DSS, and the Uncertainty DSS. The authors found that without the visualizations, participants did not distinguish between ambiguous and unambiguous choices. However, the Uncertainty DSS improved participants’ ability to recognize ambiguous decision situations. Additionally, it increased the likelihood that participants would form plans to seek clarifying information. These results suggest that a relatively simple visualization can change the way in which designers think about decision choices.

Developing A Theoretical Framework of Task Complexity for Research on Visualization in Support of Decision Making Under Uncertainty

2017 ◽  
Vol 61 (1) ◽  
pp. 1193-1197 ◽  
Author(s):  
Stephen M. Fiore ◽  
Samantha F. Warta ◽  
Andrew Best ◽  
Olivia Newton ◽  
Joseph J. LaViola
Keyword(s):  
Decision Making ◽  
Task Complexity ◽  
Theoretical Framework ◽  
Decision Making Under Uncertainty ◽  
Task Context ◽  
Uncertainty Visualization ◽  
Initial Validation ◽  
And Performance ◽  
Support Research

This paper describes initial validation of a theoretical framework to support research on the visualization of uncertainty. Two experiments replicated and extended this framework, illustrating how the manipulation of task complexity produces differences in performance. Additionally, using a combinatory metric of workload and performance, this framework provides a new metric for assessing uncertainty visualization. We describe how this work acts as a theoretical scaffold for examining differing forms of visualizations of uncertainty by providing a means for systematic variations in task context.

scholarly journals Coincident Visualization of Uncertainty and Value for Point Symbols

2019 ◽  
Vol 1 ◽  
pp. 1-2
Author(s):  
Petr Kubíček ◽  
Milan Konečný ◽  
Jie Shen ◽  
Zdeněk Stachoň ◽  
Radim Štampach ◽  
...  
Keyword(s):  
Decision Making ◽  
Uncertainty Propagation ◽  
Natural World ◽  
Decision Making Process ◽  
Uncertainty Visualization ◽  
Visual Semiotics ◽  
Open Questions ◽  
Uncertainty Representation ◽  
International Audience ◽  
Work Done

<p><strong>Abstract.</strong> The issue of uncertainty as a generic phenomenon in the natural world has been at the centre of both the cartographic and GI communities since the beginning of geographic data quality research. In accordance with the development of theoretical aspects of cartographic visualization and methods of uncertainty propagation in models, the generally accepted opinion is that uncertainty has to be presented to users in an unambiguous and understandable way. Despite reasonable amounts of work done in the field of uncertainty visualization methods (MacEachren1992, Leitner and Buttenfield 2000) and the testing of impact of visualization on decision making (Senaratne et al. 2012; Kinkeldy et al. 2015), there is still a wide gap between the uncertainty visualization theory and widely accepted use of uncertainty representation within decision making process. MacEachren et al. (2012), Fabrikant et al. (2010) initiated the discussion towards optimization of uncertainty visualization regarding visual semiotics and use of specific representations of uncertainty within complex mapping compositions and application context. However, their studies left also some open questions to be solved regarding the international audience of users.</p><p>The presented study focused on two unresolved topics, namely how would users perceive the uncertainty point map signs within a complex map field and what would be the appropriate visualization in case if there is a need to combine value and uncertainty together. Moreover, we performed the testing in two different cultural environments in Brno (Czech Republic, Europe) and Nanjing (China).</p>

scholarly journals Examining Training Comprehension and External Cognition in Evaluations of Uncertainty Visualizations to Support Decision Making

2019 ◽  
Vol 63 (1) ◽  
pp. 1654-1658
Author(s):  
Jihye Song ◽  
Olivia B. Newton ◽  
Stephen M. Fiore ◽  
Corey Pittman ◽  
Joseph J. LaViola
Keyword(s):  
Decision Making ◽  
Extended Cognition ◽  
Uncertainty Visualization ◽  
Cognitive Efficiency ◽  
Course Of Action ◽  
Baseline Task ◽  
Metacognitive Judgments ◽  
Improved Accuracy ◽  
External Cognition ◽  
Support Decision Making

Recent advances in uncertainty visualization research have focused not only on design features to support decision making, but also on challenges of evaluating the effectiveness of uncertainty visualizations, such as the degree to which individuals’ baseline task comprehension may alter their performance on experimental tasks regardless of a visualization’s effectiveness. Building on recent work, we investigated the effect of training comprehension on performance across varying representations of uncertainty and varying degrees of visualization interactivity using a simulated course of action selection task. Additionally, we explored how extended cognition theory can be applied to visualization evaluations by incorporating interface features that afford externalization of knowledge within the task environment. Our findings suggest that regardless of how uncertainty is represented, training comprehension leads to superior transfer, reduced workload, more accurate metacognitive judgments, and higher cognitive efficiency. Our findings also suggest that external cognition during decision making leads to improved accuracy and cognitive efficiency. The present study contributes to research on the design and evaluation of uncertainty visualizations. In addition, this study extends previous work by demonstrating how extended cognition theory can inform the design of human-machine interfaces to support decision making.

Visualization of Uncertainty Aids Spatial Judgments but Fails to Improve Metacognitive Efficiency

2017 ◽  
Vol 61 (1) ◽  
pp. 1390-1393
Author(s):  
Olivia Burton ◽  
Diane Pomeroy ◽  
Vanja Radenovic ◽  
Jason S. McCarley
Keyword(s):  
Decision Making ◽  
Order Type ◽  
Judgment Task ◽  
Uncertainty Visualization ◽  
Decision Making Processes ◽  
Potential Benefits ◽  
Spatial Judgment ◽  
Performance Research

Uncertainty is an element of many decision-making tasks and inherently compromises performance. Research has found only equivocal evidence that uncertainty representations—displays that explicitly denote data quality—offset the performance costs of uncertainty. As yet, though, no work has examined the potential benefits of uncertainty displays to metacognition, display readers’ ability to assess the quality of their own decision-making processes. The current study examined the benefits of uncertainty visualization to first-order (Type 1) and metacognitive (Type 2) sensitivity in a spatial judgment task. Data revealed only small improvements in Type 1 and Type 2 sensitivity with visualized uncertainty displays, and gave no evidence of disproportionate gains to metacognition.

Decision Making and Constraint Tradeoff Visualization for Design Under Uncertainty

2007 ◽  
Author(s):  
Sirisha Rangavajhala ◽  
Achille Messac
Keyword(s):  
Decision Making ◽  
Design Space ◽  
Optimization Problems ◽  
Decision Making Process ◽  
Uncertainty Visualization ◽  
Design Alternatives ◽  
Final Design ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Constraint Feasibility

In design optimization problems under uncertainty, two conflicting issues are generally of interest to the designer: feasibility and optimality. In this research, we adopt the philosophy that design, especially under uncertainty, is a decision making process, where the associated tradeoffs can be conveniently understood using multiobjective optimization. The importance of constraint feasibility and the associated tradeoffs, especially in the presence of equality constraints, is examined in this paper. We propose a three-step decision making framework that facilitates effective decision making under uncertainty: (1) formulating a multiobjective problem that effectively models the tradeoffs under uncertainty, (2) generating design alternatives by solving the proposed multiobjective robust design formulation, and (3) choosing a final design using filtering and constraint uncertainty visualization schemes. The proposed framework can be used to systematically explore the design space from a constraint tradeoff perspective. A tolerance synthesis example is used to illustrate the proposed decision making process.

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