Causal Loop Diagrams

A brief explanation about how to draw causal loop diagrams that help understand and describe the interdependencies between elements that form a system

To explain causal loop diagrams, I am going to use a horse and rider in a show jumping competition as a (relatively) simple example.  You have two key elements - the horse, and the rider:

The arrow between the elements indicates a cause-and-effect relationship.  The rider's performance will have an effect on the horse's performance.  The S indicates the way the relationship works.  S is used for the same direction - increasing one increases the other.  In this example, the better the rider's performance, the better the horse's performance, and vice versa.  The letter O is used for the opposite direction - if an improvement in the rider's performance made the horse worse, then the relationship would be O.

For a rider to win a show jumping competition, there is (contrary to popular belief) a bit more involved than just a good horse...

The rider's performance will influence the horse's performance - a bad rider will make it difficult for a horse to jump well, a good rider will make it all look easy.  The objective of a show jumping competition is to jump a clear round, so the horse's performance has an opposite effect on the number of faults collected - the better the performance, the fewer the faults.  The number of faults has an opposite effect on the rider's confidence.  The fewer the faults, the more confident the rider becomes, and confidence (should) improve the rider's performance, further improving the horse and so it continues as they complete the show jumping course.   If the number of faults starts to increase, the rider's confidence starts to diminish, affecting the rider's performance which then affects the horse's performance.  (In show jumping terms, this results in a 'cricket score' round - its not cricket and its not a good thing.)

This diagram is called a closed loop.  Closed loops can either be 'reinforcing' or 'balancing'.  A reinforcing loop strengthens with every turn, as this example demonstrates.  A balancing loop does just that, at least one element works in the opposite direction to the others, stabilising some of the effects caused by the loop.  Reinforcing loops have one of two outcomes - they are either virtuous or vicious.  This is because the effect they have is exponential.  When things go well, they go very well.  When things go wrong, they go very wrong.  A balancing loop is constantly working towards a specific goal, hence it seeks stability.

To identify if a loop is reinforcing or balancing, simply go round the complete loop and count the number of Os.  If the number is even (and that includes zero), the loop will be reinforcing.  If the number is odd, the loop will be balancing.  (This is actually a very profound statement, but no time to explain why here - read a book on the subject if you're interested.)

A causal loop diagram can also include inputs and outputs that influence the behaviour of the loop.  How many you decide to include depends on how you define the system boundaries.

So, in this example, the performance of both the horse and rider can be influenced by their levels of preparation (and preparation is a system in its own right - ability, fitness, training, mental attitude etc. but in this example, we are only interested in the end result of that system).  The horse's performance can be affected by the condition of the ground - the better the ground, the better the performance and vice versa (if the competition is outdoors, the ground becomes a system too, constantly changing dependent on weather conditions and ground maintenance).  The rider's performance is affected by the rider's confidence, which can be negatively affected by the difficulty of the course (nerves set in)...  and this represents the challenge with competing on horses - it's not just a case of 'best' horse wins...

There's a lot more to drawing causal loop diagrams, but this overview covers the simple foundations for getting started.

 

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