Systems Thinking with Causal Loop Diagramming (CLD)
“More software projects have gone awry from management’s taking action based on incorrect system models than for all other causes combined.” – Weinberg-Brooks’ Law.
From the words of “The Fifth Discipline” author, Peter Senge, “Systems thinking is a conceptual framework, a body of knowledge and tools that has been developed over the last 50 years, to make full patterns clearer, and help see how to change them effectively.”
Systems thinking is something we think we do well but typically don’t. There are many reasons for not being good at this. I will cover those in another post. This post will cover an introduction to one of the tools that helps enable systems thinking. That tool is Causal Loop Diagramming (CLD).
Causal Loop Diagramming is a visual aid for understanding how different variables in a system interrelate. They consist of nodes and edges. A node is a variable in the system and an edge connects two variables representing their relationship. It is best to diagram with a small group of people around a whiteboard. The most important thing is the conversation and surfacing of mental models that may be impacting the identification of major systemic problems.
Let’s dive in. The Causal Loop Diagrams most common notation to model the nodes and edges, in my experience, are the variable, causal link, opposing causal link, and delay. I will use a common issue that some of us (including myself) struggle with to show how the notation is used.
The variable represents an element of a system that has a cause and effect on another element of the system. Variables are typically measurable. For example, “Degree of Hunger” could be a variable in trying to understand a complex human system (e.g. moi).
The causal link notation represents the cause and effect linkage between two elements in the representation of a system. Most commonly causal links connect two variables. In our example a second variable is introduced, “Amount of Intake.” A causal link can be drawn to show the relationship of “Degree of Hunger” to “Amount of Intake,” as shown below.
When interpreting this model, one might read it as, “when the degree of hunger goes up the amount of intake will need to go up.”
The opposing causal link notation represents an opposite cause and effect between two elements in the representation of a system. It is common to link two variables and use an “O” or “-” on the link. This represents the fact that when the cause variable changes, the effected variable changes in the opposite direction. In our example, an opposing link can be drawn from the “Amount of Intake” to the “Degree of Hunger” as shown below.
When interpreting the model, one might read this as, “when the amount of intake increases the degree of hunger decreases.”
Not all cause and effects happen immediately and it is worthwhile to identify and appreciate those delays when trying to understand a system. In our example, a delay between “Amount of Intake” and the “Degree of Hunger” is introduced.
When interpreting the model now, one might read this as, “when the amount of intake increases it will have a delayed decrease in the degree of hunger.” I think it is safe to say many of us have experienced this.
Now we are starting to get a better understanding of why I sometimes eat too much. I tend to eat fast and my full stomach signals can be delayed. And … now I have to adjust my belt. Ugh.
Systems thinking is a way to understand systems at the level that can enable effective changes. Causal loop diagramming is one tool that allows understanding a system. The basic notations of a CLD were covered. These notations alone are sometimes all that is needed to gain a significantly better understanding of a complex adaptive system. So go get started.
There are some additional notations used in Causal Loop Diagramming (CLD). I will go over those in the next article and expand on the human system model example.