Guided Visual Analytics for Decision Making under Uncertainty | TU Wien

Information

Publication Type: PhD-Thesis
Workgroup(s)/Project(s): not specified
Date: March 2026
Date (Start): 2021
Date (End): 2025
TU Wien Library: AC17745765
Open Access: yes
1st Reviewer: Jean-Daniel Fekete
2nd Reviewer: Anastasia Bezerianos
Rigorosum: November 2025
First Supervisor: Renata Georgia Raidou
Pages: 185
Keywords: Visualization, Visual Analytics, Guidance, Trust, Confidence

Abstract

Visual Analytics (VA) has emerged from the need to optimize decision making by involving human reasoning in sense making. The development of VA has been facilitated by significant technological advances in modern computer graphics and data processing capabilities. Involving humans in the loop aims to address high-risk scenarios where artificial intelligence (AI) automated approaches are insufficient. One active area of research with VA is the development of methods that enable the user to make efficient and effective decisions under high uncertainty. Yet, the field of VA research has not fully understood how user attitude, namely trust and confidence, interplay in VA decision making under uncertainty. Properties of the user attitude play a crucial role in optimizing VA decision making, but they are challenging to externalize and evaluate. For instance, user confidence in their decision emerges as an important indicator of effectiveness when the correctness of the decision cannot be measured. In this dissertation, we explore the use of guidance techniques to address uncertainties in VA decision making, focusing on scenarios where the correctness of decisions cannot be definitively established. Throughout this work, we learned that a multidimensional guidance mechanism can address uncertainties more effectively when uncertainties are challenging to quantify and visualize, especially in the case of subjective uncertainty. However, evaluating the effectiveness of guidance approaches requires a more comprehensive analysis of the interplay between trust and confidence within the sense-making process. Using provenance networks and SNA metrics can provide a more reliable and comprehensive assessment of user confidence, indicating that such approaches can be employed to support co-adaptive guidance.

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BibTeX

@phdthesis{Musleh_PhD,
  title =      "Guided Visual Analytics for Decision Making under
               Uncertainty",
  author =     "Maath Musleh",
  year =       "2026",
  abstract =   "Visual Analytics (VA) has emerged from the need to optimize
               decision making by involving human reasoning in sense
               making. The development of VA has been facilitated by
               significant technological advances in modern computer
               graphics and data processing capabilities. Involving humans
               in the loop aims to address high-risk scenarios where
               artificial intelligence (AI) automated approaches are
               insufficient. One active area of research with VA is the
               development of methods that enable the user to make
               efficient and effective decisions under high uncertainty.
               Yet, the field of VA research has not fully understood how
               user attitude, namely trust and confidence, interplay in VA
               decision making under uncertainty. Properties of the user
               attitude play a crucial role in optimizing VA decision
               making, but they are challenging to externalize and
               evaluate. For instance, user confidence in their decision
               emerges as an important indicator of effectiveness when the
               correctness of the decision cannot be measured. In this
               dissertation, we explore the use of guidance techniques to
               address uncertainties in VA decision making, focusing on
               scenarios where the correctness of decisions cannot be
               definitively established. Throughout this work, we learned
               that a multidimensional guidance mechanism can address
               uncertainties more effectively when uncertainties are
               challenging to quantify and visualize, especially in the
               case of subjective uncertainty. However, evaluating the
               effectiveness of guidance approaches requires a more
               comprehensive analysis of the interplay between trust and
               confidence within the sense-making process. Using provenance
               networks and SNA metrics can provide a more reliable and
               comprehensive assessment of user confidence, indicating that
               such approaches can be employed to support co-adaptive
               guidance.",
  month =      mar,
  pages =      "185",
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Research Unit of Computer Graphics, Institute of Visual
               Computing and Human-Centered Technology, Faculty of
               Informatics, TU Wien ",
  keywords =   "Visualization, Visual Analytics, Guidance, Trust, Confidence",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2026/Musleh_PhD/",
}