On a causal loop diagram, each node (bubble) represents a variable that can go up and down, such as the price of alcohol, or something more difficult to quantify like an individual’s motivation to drink. Arrows represent cause-effect relationships. In this example, if the individual’s motivation to drink goes up, the solid arrow indicates that consumption will also go up. If the price of alcohol goes up, the dotted arrow indicates that consumption will go down (an opposite relationship).
We use causal loop diagrams to understand complex social systems, where there are many factors influencing each other, including human behaviour, economics, politics, environment, science, and technology.
Examining the wicked problem of alcohol addiction in this way allowed us to identify social factors such as Influence by family & peers. Knowing about that leverage point would enable policy-makers to consider new interventions, such as involving family members in addiction treatment programs.
Causal loop diagrams can get very complicated – our first iteration had 60 nodes before we clarified the scope, and generalized to combine some factors such as the many Consequences of addiction. We further simplified our model for display purposes:
In this simplified diagram, the causal loops become easier to see. On the left, there is a balancing loop: more Effort on Prevention reduces Consumption and the Consequences of addiction, which could reduce Effort on Prevention, leading to more addiction. Policy-makers should be aware that if prevention programs succeed in reducing alcoholism, funding should not be withdrawn, as the need will recur.
Guides to causal loop diagramming often focus on the loops, which can be balancing or reinforcing (either a virtuous or vicious cycle). In creating this and other causal diagrams, I’ve found that many of the interesting cause-effect relationships do not form a tidy loop, and that the loops are not necessarily important for policy decision-making. So for my Master’s Research Project, I will be developing a causation model format that improves upon the best features of causal loop diagramming.