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A Typology of Decision-Making Tasks for Visualization

Camelia D. Brumar, Sam Molnar, Gabriel Appleby, Kristi Potter, Remco Chang

TL;DR

This paper contributes a typology of decision-making tasks that were iteratively refined from a list of design goals distilled from a literature review, and provides definitions for these tasks that are suitable for the visualization community.

Abstract

Despite decision-making being a vital goal of data visualization, little work has been done to differentiate decision-making tasks within the field. While visualization task taxonomies and typologies exist, they often focus on more granular analytical tasks that are too low-level to describe large complex decisions, which can make it difficult to reason about and design decision-support tools. In this paper, we contribute a typology of decision-making tasks that were iteratively refined from a list of design goals distilled from a literature review. Our typology is concise and consists of only three tasks: CHOOSE, ACTIVATE, and CREATE. Although decision types originating in other disciplines exist, we provide definitions for these tasks that are suitable for the visualization community. Our proposed typology offers two benefits. First, the ability to compose and hierarchically organize the tasks enables flexible and clear descriptions of decisions with varying levels of complexities. Second, the typology encourages productive discourse between visualization designers and domain experts by abstracting the intricacies of data, thereby promoting clarity and rigorous analysis of decision-making processes. We demonstrate the benefits of our typology through four case studies, and present an evaluation of the typology from semi-structured interviews with experienced members of the visualization community who have contributed to developing or publishing decision support systems for domain experts. Our interviewees used our typology to delineate the decision-making processes supported by their systems, demonstrating its descriptive capacity and effectiveness. Finally, we present preliminary findings on the usefulness of our typology for visualization design.

A Typology of Decision-Making Tasks for Visualization

TL;DR

This paper contributes a typology of decision-making tasks that were iteratively refined from a list of design goals distilled from a literature review, and provides definitions for these tasks that are suitable for the visualization community.

Abstract

Despite decision-making being a vital goal of data visualization, little work has been done to differentiate decision-making tasks within the field. While visualization task taxonomies and typologies exist, they often focus on more granular analytical tasks that are too low-level to describe large complex decisions, which can make it difficult to reason about and design decision-support tools. In this paper, we contribute a typology of decision-making tasks that were iteratively refined from a list of design goals distilled from a literature review. Our typology is concise and consists of only three tasks: CHOOSE, ACTIVATE, and CREATE. Although decision types originating in other disciplines exist, we provide definitions for these tasks that are suitable for the visualization community. Our proposed typology offers two benefits. First, the ability to compose and hierarchically organize the tasks enables flexible and clear descriptions of decisions with varying levels of complexities. Second, the typology encourages productive discourse between visualization designers and domain experts by abstracting the intricacies of data, thereby promoting clarity and rigorous analysis of decision-making processes. We demonstrate the benefits of our typology through four case studies, and present an evaluation of the typology from semi-structured interviews with experienced members of the visualization community who have contributed to developing or publishing decision support systems for domain experts. Our interviewees used our typology to delineate the decision-making processes supported by their systems, demonstrating its descriptive capacity and effectiveness. Finally, we present preliminary findings on the usefulness of our typology for visualization design.
Paper Structure (28 sections, 9 figures)

This paper contains 28 sections, 9 figures.

Table of Contents

  1. Introduction
  2. Background & Related Work
  3. Decision Making & Visualization
  4. Visualization Typologies
  5. Methodology
  6. Application Survey Corpus
  7. Coding Process
  8. Design Goals for a Decision Task Typology
  9. Designing the Typology
  10. Typology of Decision-Making Tasks
  11. Task Definitions
  12. Discussion of Definitions
  13. Using the Typology for Decision-Making Problems
  14. Properties that help describe decision problems
  15. Diagramming Decision-Making Problems
  16. ...and 13 more sections

Figures (9)

  • Figure 1: The three decision tasks: CHOOSE, ACTIVATE, and CREATE.
  • Figure 2: A diagram of the example wildfire evacuation decision-making problem. The CREATE task has an arrow into the ACTIVATE task, indicating that the CREATE task must occur before the ACTIVATE task. The ACTIVATE task has a self-loop, which indicates the possibility that this task is executed more than once. Lastly, the ACTIVATE task has an arrow into the CHOOSE task, indicating that the locations decided on in the ACTIVATE task are the input to the CHOOSE decision.
  • Figure 3: Homefinder Revisitedweng2018homefinder decision diagram.
  • Figure 4: Umbrawang2020umbra decision diagram
  • Figure 5: Centaurusdowling2020semantic decision diagram.
  • ...and 4 more figures