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Dagstuhl-Seminar 22331

Visualization and Decision Making Design Under Uncertainty

( 15. Aug – 19. Aug, 2022 )

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Gemeinsame Dokumente



Visualization has become a core component of any decision or risk analysis pipeline, and tools for creating visualizations are quickly becoming more and more accessible. In addition, the visual literacy of the general public has been increasing due to the pervasiveness of visualizations in everyday life. As the appetite for decision making tools grows, so does the need to convey error, confidence, missing, or conflicting data visually. However, practices around uncertainty visualization remain domain-specific, rooted in convention, and in many instances, absent entirely.

The goal of this Dagstuhl Seminar is to bring together experts with diverse knowledge of uncertainty visualization and comprehension toward building a foundation of accessible, practical knowledge that practitioners and researchers alike can rely on in addressing challenges related to uncertainty. Specifically, this seminar aims to bring together leaders in the field of uncertainty visualization and communication, along with experts on quantification and practitioners and domain experts dealing with uncertainty on a daily basis. Drawing on the knowledge of the participants, the seminar will work toward goals of synthesizing disparate findings and approaches from across computer science and related literature, noting current practices surrounding uncertainty, and identifying unsolved problems in common workflows, and areas needing further study. We plan to discuss three major topics related to challenges in visualization and decision making under uncertainty: Representation & Modeling, Comprehension and Social factors.

Representation & Modeling Challenges - Types of Uncertainty: Uncertainty comes in many forms. Can we develop a taxonomy of uncertainty types to help define categories that may be treated in a similar fashion? Big Data Problems: Many of the techniques to handle big data use data fusion, summarizations, sampling, or clustering, all of which introduce uncertainties. How can we handle these various sources of uncertainty? Do new algorithms need to be developed, or can we better use quantification and statistical methods already in play? Propagation: Uncertainty arises in all stages of the decision making pipeline. Is there a way to better understand these uncertainties and provide guidelines to how these different stages of uncertainty affect understanding and decisions?

Comprehension Challenges - Decision Making and Risk Analysis: Though decision making under uncertainty and risk are well studied fields unto themselves, the unique characteristics of decision making and analysis from visualizations raise questions that remain unanswered in those bodies of literature. For example, how does a visual channel change the perceived salience of uncertainty or risk relative to text presentations? Understanding User Differences: Risk interpretation changes from person to person, as does statistical literature. What do we know about the impacts of user characteristics and can we define guidelines for designing for specific audiences? Perception: How can we better understand the perceptual system’s strengths and leverage those for uncertainty visualization?

Social Challenges: Ethical Risks & Incentives: Depending on the situation, conveying uncertainty may not add value for users or decision-makers, may conflict with the visualization author’s incentives, or may have other ethical implications. What are common uncertainty communication scenarios where incentives and ethics may collide? Collaboration: Decision making is often a collaborative process that involves different types and levels of expertise. How can we design uncertainty visualizations that take into account the cognitive and relational levels within teams of decision makers? What are the triggers and barriers for revealing and communicating uncertainty in a collaborative context?

Copyright Nadia Boukhelifa, Christopher R. Johnson, and Kristi Potter

Vor Ort

  • Computers and Society
  • Graphics
  • Other Computer Science

  • Uncertainty Visualization
  • Uncertainty Quantification
  • Risk Analysis
  • Decision Making Under Uncertainty