In the book Natural learning – systems thinking in a learning school Jan Jutten describes causal loops as ‘language of systems thinking’:
One of the hallmarks of systems thinking is a different look at cause-effect relationships. The language we are used to speak is linear: A causes B.
But systems work differently: they consist of ‘circular lines’, of elements that work together and influence each other. Factor A does not just cause factor B, but A and B constantly influence each other. We call this cyclical thinking .
Factor A results in factor B. But factor B in turn influences factor A. A child’s performance influences the expectations of the teacher, but also vice versa!
Systems themselves continuously send signals via circular loops of cause and effect relationships. They are also called feedback loops or feedback loops. The term ‘feedback’ has a different meaning in systems thinking than in communication.
It is meant that a consequence of the feedback affects the cause, that a solution affects the problem.
In figures with causal loops this mutual influence is shown by means of arrows. From one element (a variable) to another and back again. In recent years, a ‘new language’ has been developed for systems thinking to map out how systems work. The causal loops are an important part of this language.
The alphabet of systems thinking
The way in which our language is structured encourages linear thinking: subject, person form, direct object. For example, if someone says, “I drive the car,” this sentence would look like this from a linear perspective:
Linear thinking versus systems thinking
In reality, however, the situation is more complex. After all, the behavior of cars is not only the result of the way of driving, but also influences driving behavior. For example, if the car threatens to hit the road and starts to swing, this has an effect on driving behavior. So there is constant feedback, also called feedback.
Cyclical thinking versus systems thinking
Our language has a great influence on our way of thinking. And our way of thinking determines how we act. A linear language leads to linear thinking. Linear thinking to linear action.
reckless driving —–> number of accidents —–> set speed bumps
Systems thinking requires a different language. A language with a different structure, which better reflects the way systems work. In principle, this system language is simple. We can map complex systems with a few basic concepts.
The system alphabet actually consists of just two main rules:
- positive and negative relationships between variables
- reinforcing and stabilizing causal loops.
A variable is a scalable quantity : that means that elements that we place in a behavioral pattern graph around a relationship circle or in a causal loop must be able to increase or decrease.
We distinguish between hard variables (measurable) and soft variables (not measurable, but always scalable). Formulating good variables is one of the most important but at the same time one of the most difficult parts of systems thinking.
Characteristic of a system is that the elements within a system work together and influence each other. There is a structure. There is a relationship between variables or elements of a system. This relationship can be positive and negative.
Positive relationships between variables
The relationship is positive if an increase in one variable leads to an increase in the other. Or (and this is sometimes confusing) if a decrease in one variable leads to a decrease in the other. So a positive relationship means here: both variables ‘go in the same direction’. If we put the variables in patterns of behavior, they both increase or decrease.
A positive relationship is often indicated with a ‘+’. But, in general, to avoid confusion, the ‘S’ for ‘the Same’ is used . If relationship is Same, we put an S at the arrowhead.
Negative relationship between variables
The relationship is negative if an increase in one variable leads to a decrease in the other or vice versa. So negative here means: both variables ‘go in a different direction’.
If we put the variables in behavioral pattern graphs, one variable increases and (therefore) the other decreases.
A negative relationship is often indicated by a ‘-‘. But, in general, an ‘O’ from Opposite is used.
Reinforcing and stabilizing loops
In some cases, the relationship between two variables is one-sided. An example:
reinforcing stabilizing loops systems thinking jan jutten
The more financial resources a school leader has available, the more opportunities there are to purchase new materials. The other way round, this is not the case.
In many situations there is a two-sided relationship. In such a case we speak of a causal loop.
Causal loop systems thinking reinforcing relationship
As the involvement of the participants increases, the return on the meeting will also increase. This is also the case the other way around: if team members experience that a meeting is beneficial, this will affect their involvement. Note: this story is plausible! So it will not always be the case everywhere. When a relationship is two-sided, there is a causal loop. We note this as follows:
Causal loops therefore offer the possibility to visually represent cyclical thinking in systems thinking. Two or more variables influence each other back and forth. Now we distinguish two types of loops in the system language: reinforcing and stabilizing.
Reinforcing loops: positive feedback
With reinforcing loops we see a form of feedback that ensures ever-increasing growth or decline. Then it can be in nature or in all kinds of human affairs. An example:
If the number of birds increase, there will be more eggs. The more eggs, the more birds etc. etc.
The system language uses an R in the middle of the loop to indicate that we are dealing with a reinforcing loop. Sometimes you also see a snowball. An appropriate symbol because of the expected snowball effect.
reinforcement reinforcing causal loop systems thinking jan jutten
Thus, a reinforcing loop can mean a continuous increase as well as a continuous decrease. This seems confusing: a continuous decrease in the variable is also called an amplifying loop.
If there is an amplifying loop, you will hear people say, “We are in an upward (or downward) spiral!” or “This has a snowball effect!”
Stabilizing loops: negative feedback
Most systems exhibit, as it were, a built-in resistance to too great a change. Despite the fact that there is so much talk about change in schools, we see many things remain the same. It seems as if the system itself is looking for balance. Stability is very important for a system to survive. After all, continuous growth or decline is not possible.
This stability is achieved through the so-called negative feedback. Stabilizing loops are also called balancing. Hence the use of the letter B in the diagram.
The stabilization processes ensure that a system never strays too far from its ‘natural’ range. It is as if they are some kind of built-in intelligence of the system.
If we put a stabilizing loop in a causal loop, it looks like this:
If the hunger increases, I will eat (more). This reduces hunger. If the hunger decreases, I will not eat, which increases the hunger again. The system balances itself in this way.
Stabilizing loops are always linked to a goal. Just as if the system knows “how it should be” and does everything in its power to achieve it again. Stabilizing loops are constantly aimed at keeping the system at a desired level. They resist change in one direction. They cause change in the opposite direction, canceling out the previous effects.
One of the properties of stabilizing loops is that they are much less visible than reinforcing loops. They ‘quietly’ ensure that everything remains as it was. This is much less noticeable than changes. Take your body temperature as an example. The body is constantly trying to keep this temperature constant, without us noticing it. If there is a fever and the temperature rises, we notice it immediately!
In summary: the most important rules for causal loops
Systems always consist of combinations of positive (S) and negative (OP relationships, of combinations of reinforcing and stabilizing loops.
The term positive or negative feedback can be confusing. In systems thinking it is understood in a different way than what is normally understood by it. In any case, positive and negative has nothing to do with compliments or disapproval. Positive feedback here means: feedback strengthens the process: the more people there are, the more people are born. Positive feedback therefore always leads to change in the same direction. Negative feedback perpetuates a certain situation: if I have too little food, I get hungry. When I eat then my hunger decreases and balance remains in ‘my system’ called body !!
It is of great importance with causal loops that the influence of the variables is mutual. If this is not the case, you can work with behavioral pattern graphs, but you cannot make a causal loop out of it.
All variables used in a causal loop must be scalable. That means they can increase or decrease. If the chosen variables do not meet this criterion, working with causal loops causes problems and therefore frustrations.
Source: Natural learning – systems thinking in a learning school, Jan Jutten