A Mixed-Initiative Visual Analytics Approach for Qualitative Causal Modeling
This addresses the fragmented and time-consuming task of qualitative causal modeling for analysts in socio-natural domains, representing an incremental improvement.
The paper tackles the challenge of modeling complex socio-natural systems by developing a mixed-initiative visual analytics approach within the Causemos platform, enabling analysts to rapidly assemble qualitative causal models and demonstrating through user evaluation that it accelerates model building and enriches mental models.
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.