Understanding model behavior using loops that matter

TL;DR

This paper introduces a novel numeric method to identify and analyze the most influential loops in System Dynamics models, enhancing understanding of model behavior and aiding visualization.

Contribution

The paper presents a new numeric approach for determining the impact of each loop in a model, surpassing traditional eigenvalue and pathway metrics.

Findings

Effectively identifies dominant loops during simulations

Improves visualization and interpretation of model behavior

Validated across diverse System Dynamics models

Abstract

The link between structure and behavior is central to System Dynamics, but effective tools for understanding that relationship still elude us. The current state of the art in the field of loop dominance analysis relies on either practitioner intuition and experience or complex algorithmic manipulation in the form of eigenvalue analysis or pathway participation metrics. This paper presents a new and distinct method to find the 'loops that matter' in generating model behavior. This is a numeric method capable of determining the impact for every loop in a model and identifying which dominate behavior at each point in time. The method was inspired by observations on variable value changes during simulations and has been refined using empirical evaluation on a variety of different models. In addition to explaining behavior, the method shows promise for improving visualization and aggregation of simulation results.