Capturing multiple perspectives in causal loop diagrams to enhance reproducibility and transparency

Status: Completed in 2025

Collaborators: Yu Xuan Yio, Nethara Athukorala, Simran, Songhai Fan, Dr. Cynthia A. Huang, Prof. Lyn Bartram, Prof. Tim Dwyer, Dr. Sarah Goodwin

Causal Loop Diagrams (CLDs) are schematic representations that consist of causal links between variables. Positive and negative signs associated with links indicate whether the nature of the relationship is proportional or inversely proportional, respectively. They have been introduced for conceptual modelling as part of the systems thinking movement. CLDs accommodate multi-causality and non-linearity, making them particularly suitable for understanding complex systems.

In particular, collaborative model-building using CLDs, which we refer to as Collaborative Causal Loop Diagrams (C-CLDs), is considered a powerful tool for helping decision-makers to model complex systems and processes. However, existing tools offer little support for documenting the model-building process or capturing the provenance of stakeholder contributions. In this work, we map the C-CLD design space to derive concrete requirements for documentation and transparency. We introduce Perspectiva, an interactive prototype shaped by those requirements and refined through iterative feedback. Perspectiva enables side-by-side navigation and comparison of CLDs, codifies changes and conflicting relationships, and preserves term provenance and contributor attribution. Its core features include anchored nodes for topological consistency, node interaction, hover-activated provenance pop-ups, and colour-coded encodings. In user studies with domain and visualisation experts, participants reported that Perspectiva improved navigation, comparison, and provenance tracking relative to static diagrams, as well as highlighting opportunities for enhancement and future research.

This work has been awarded a Best Paper Honourable Mention at IEEE VIS 2025.