A Mixed-Initiative Visual Analytics Approach for Qualitative Causal Modeling

TL;DR

This paper presents a mixed-initiative visual analytics system that enables analysts to efficiently build and explore qualitative causal models of complex socio-natural systems by integrating diverse data and expert knowledge.

Contribution

It introduces a novel user-centered, mixed-initiative approach within the Causemos platform for rapid qualitative causal modeling of complex systems.

Findings

Enhanced model building speed and accuracy

Improved user mental model enrichment

Effective integration of diverse data sources

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.