  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.
