Finding the Loops that Matter

TL;DR

This paper enhances the 'Loops that Matter' method by introducing an algorithm to identify the most important feedback loops in complex models, enabling better understanding of model behavior regardless of size.

Contribution

It presents a novel algorithm for discovering key feedback loops in complex models, improving the applicability of the 'Loops that Matter' method.

Findings

The algorithm effectively finds the most explanatory loops.

It performs well computationally on large models.

Including important loops improves model understanding.

Abstract

The Loops that Matter method (Schoenberg et. al, 2019) for understanding model behavior provides metrics showing the contribution of the feedback loops in a model to behavior at each point in time. To provide these metrics, it is necessary find the set of loops on which to compute them. We show in this paper the necessity of including loops that are important at different points in the simulation. These important loops may not be independent of one another and cannot be determined from static analysis of the model structure. We then describe an algorithm that can be used to discover the most important loops in models that are too feedback rich for exhaustive loop discovery. We demonstrate the use of this algorithm in terms of its ability to find the most explanatory loops, and its computational performance for large models. By using this approach, the Loops that Matter method can be applied to models of any size or complexity.