Seamlessly Integrating Loops That Matter into Model Development and Analysis
This work addresses the problem of model interpretability for researchers and educators, offering tools to enhance learning and explanation extraction, though it appears incremental as an implementation of an existing methodology.
The paper tackles the challenge of understanding model behavior by implementing the Loops that Matter methodology into a model development environment, which automatically identifies dominant loops and generates simplified causal loop diagrams to aid communication and analysis.
Understanding why models behave the way they do is critical to learning from them, and to conveying the insights they offer to a broad audience. The Loops that Matter methodology automatically shows which loops are dominating behavior at each point in time and generates simplified causal loop diagrams from a user adjustable set of important loops. This paper describes the challenges of implementing these tools into a fully functioning model development environment along with the solutions developed. The promise of the tools has, if anything, been amplified by the results of this implementation, and we give several examples of using the tools. For pedagogical models Loops that Matter can ease communication while speeding and deepening learning. For complex models the tools allow the extraction of realistic explanations of behavior in the form of animated simplified causal loop diagrams. For models with discrete and discontinuous elements, the bigger feedback picture is still easily discoverable. While there will doubtless be refinements and enhancement to the delivered tools, they represent a large step forward in our ability to understand models from conceptualization through delivery.