Improving Loops that Matter

Abstract

The Loops that Matter approach to understanding behavior has proven easy to use and broadly applicable, but it has a shortcoming in its original formulation in that it does not give the same results when flows equations are combined or separated. This is because the original formulation treats the impact of a flow on a stock relative to the net flow, so that all scores tend to get very large in magnitude as a stock approaches equilibrium, but how big depends strongly on how the flows are specified. By reformulating the link scores from a flow to stock as the score from the flow to the net flow for the stock, this topological dependency is removed. Using this approach makes it easier to see how two loops, especially balancing and reinforcing loops, can work together to achieve an equilibrium or steady state. This makes the analysis of models showing a transition to a steady state both easier and more insightful. In addition, the mathematics behind this approach lines up more closely with the Pathway Participation and Loop Impact analysis methods making the relationship among these different approaches clear. The result of this, when applying the analysis to a variety of models, is that the determination of the structure responsible for behavior is clearer, and more clearly tied to work already done using other techniques.