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.

翻译：尽管决策是数据可视化的核心目标之一，但该领域内对决策任务的区分研究尚不充分。现有的可视化任务分类法与类型学往往侧重于更细粒度的分析任务，这些任务层级过低，难以描述复杂的大型决策过程，从而给决策支持工具的推理与设计带来困难。本文提出一种决策任务类型学，该体系通过文献综述提炼的设计目标列表经迭代优化而成。我们的类型学结构简洁，仅包含三种任务：CHOOSE（选择）、ACTIVATE（激活）与CREATE（创建）。尽管其他学科已存在决策类型相关研究，但我们为这些任务提供的定义更适用于可视化领域。我们提出的类型学具有双重优势：首先，任务的可组合性与层次化组织能力使其能够灵活清晰地描述不同复杂度的决策过程；其次，该类型学通过抽象数据细节，促进可视化设计师与领域专家之间进行富有成效的对话，从而提升决策过程分析的清晰度与严谨性。我们通过四个案例研究展示了该类型学的应用价值，并通过对可视化领域资深专家的半结构化访谈评估了该体系——这些专家曾参与开发或发表面向领域专家的决策支持系统。受访者运用我们的类型学清晰界定了其系统支持的决策流程，验证了该体系的描述能力与有效性。最后，我们展示了该类型学在可视化设计适用性方面的初步研究成果。