  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.
