Complex is not (the same as) difficult (5)

Pieter Jansen and Fredrike Bannink


Causality, correlations, and averages.

We find causality convenient. Many athletes perform certain rituals before a game which they also performed before a previous victory. Superstition? Some people stop using a certain product after they have had an unpleasant experience (abdominal pain or skin irritation) with that product. Coincidence?


Okay, now it gets trickier. Scientific studies show that running therapy has a beneficial effect on recovery from depression. Causal relation or correlation? Remember there are also people who suffer from depression who do not benefit from running therapy. High blood pressure, smoking, hypercholesterolemia and obesity are often referred to as causes of cardiovascular disease. They are, of course, risk factors.
Smeets gives, in a wonderful TED talk about correlations, an example of research from the seventies in the US showing that children who performed well in school also felt very confident. This study received a lot of attention and for decades parents worked on the self-confidence of their children, because high self-esteem would lead to good school performances. From research many years later, it turned out to be the exact reverse: children who did well in school became more confident.


Complexity is fascinating, however, we find causality convenient. We are prone to construct causal relations when there is merely coincidence or correlation. Kahneman (2011)  did a lot of research on ‘biases’ (systematic errors or intuitive prejudice) in our thinking. In 2002 he received the Nobel Prize for Economics for his work.


The medical model (e.g., examination, diagnosis, treatment) is an example of the cause-and-effect model. It is based on causality. The model has proven to be useful in our medical profession. Treatment options often refer to scientific research. This research should have (sufficiently) large numbers in the research population, given the variation in the components to be studied. If we want to draw conclusions from these studies we have to make use of averages. Compare this to the bed of Procrustes.


In Greek mythology Procrustes was an innkeeper. He ensured his guests fit perfectly into their beds by either stretching or chopping off their limbs.


In order to perform reductionist research on an irregular issue, we use an artifice. The irregular elements are converted into averages. We replace individual goals by norms. And the process must follow guidelines, directives or blueprints (the average approach). This does not do justice to variation. A functional approach, such as the solution-focused model (see Complex 4 and 6), does.


The average Dutch male is 182.5 cm tall; the average Dutch female is 168 cm.
Does that make a Dutch person of 182.5 cm a male?


Kahneman, D. (2011). Thinking, fast and slow, New York: Farrar, Straus and Giroux.

Smeets (2012), TED-talk via