Seamlessly Integrating Loops That Matter into Model Development and Analysis

TL;DR

This paper introduces the 'Loops that Matter' methodology, which automatically identifies dominant feedback loops in models, simplifies their causal structure, and enhances understanding and communication of complex model behaviors.

Contribution

The paper presents a fully integrated implementation of the 'Loops that Matter' tools within a model development environment, enabling automatic analysis and visualization of influential feedback loops.

Findings

Tools improve understanding of model behavior

Facilitate communication of complex models

Work effectively with models containing discrete elements

Abstract

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.