Towards Understanding Decision Problems As a Goal of Visualization Design

2025-07-24

AI Summary

Decision support in visualization research lacks systematic characterization of decision-context frameworks, and existing task models fail to guide design for real-world decision scenarios. Method: We propose a structured decision-problem analysis framework that, for the first time, decomposes decision problems into three core attributes—data, user, and task context—and explicitly articulates their constraints and implications for visual encoding and interaction design. Grounded in task-modeling principles and visualization design theory, we develop an operational feature-description system for decision problems and validate it through multi-case analysis. Contribution/Results: The framework addresses the critical gap in traditional task models—neglect of decision context—and provides the first systematic theoretical tool for decision-oriented visualization research. It reveals limitations in current design practices and identifies concrete pathways to enhance decision-support efficacy in authentic settings.

Technology Category

• Reasoning under Uncertainty: Decision/Utility Theory
• Knowledge Representation and Reasoning: Preferences
• Data Mining & Knowledge Management: Data Visualization & Summarization

Application Category

• Web Mining and Content Analysis: Web data visualization
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Decision-making is a central yet under-defined goal in visualization research. While existing task models address decision processes, they often neglect the conditions framing a decision. To better support decision-making tasks, we propose a characterization scheme that describes decision problems through key properties of the data, users, and task context. This scheme helps visualization researchers specify decision-support claims more precisely and informs the design of appropriate visual encodings and interactions. We demonstrate the utility of our approach by applying it to characterize decision tasks targeted by existing design studies, highlighting opportunities for future research in decision-centric visualization.

Problem

• Understanding decision problems in visualization design
• Characterizing decision problems via data, users, and context
• Improving decision-support claims and visualization design

Innovation

• Characterization scheme for decision problems
• Key properties of data, users, context
• Design visual encodings and interactions