1. A Mixed-Initiative Visual Analytics Approach for Qualitative Causal Modeling
- Author
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Pascale Proulx, Holland M. Vasquez, Fahd Husain, Rosa Romero-Gomez, and Meng-Wei Chang
- Subjects
FOS: Computer and information sciences ,Creative visualization ,Visual analytics ,Computer science ,media_common.quotation_subject ,Computer Science - Human-Computer Interaction ,Human-centered computing ,Data science ,Human-Computer Interaction (cs.HC) ,Visualization ,Variety (cybernetics) ,Disparate system ,Set (psychology) ,media_common ,Causal model - Abstract
Modeling complex systems is a time-consuming, difficult and fragmented task, often requiring the analyst to work with disparate data, a variety of models, and expert knowledge across a diverse set of domains. Applying a user-centered design process, we developed a mixed-initiative visual analytics approach, a subset of the Causemos platform, that allows analysts to rapidly assemble qualitative causal models of complex socio-natural systems. Our approach facilitates the construction, exploration, and curation of qualitative models bringing together data across disparate domains. Referencing a recent user evaluation, we demonstrate our approach's ability to interactively enrich user mental models and accelerate qualitative model building.
- Published
- 2021