Models are (useful) simplifications of reality

The only true measure of a model is if it works.

Applies to Systems Thinking, as well as to Cynefin, or to my Positive Complexity model.


Because all models are simplifications of reality.

Yes, we need models, i.e. simplifications, so that we understand reality. But it is important to know that they are limited, and to know their limitations.


Some are too simple, some are too complicated or complex for a specific problem.


The simplest example is Newton's laws. They were super useful for hundreds of years, and still are. 

We know that they do not model exactly the reality - because no model does. You cannot fly to the moon using only Newtonian physics. Einstein's théories are a better model for space navigation. But still a model. Einstein's model is more advanced, more complex. It is nevertheless less practical for designing bicycles (you only need classic mechanics for bicycles).


So, there is no such thing as a correct or wrong model. Especially in social sciences, in management, or in engineering.

Thus models are better measured by appropriateness: useful or less useful.


The only true measure of a model or theory is if it works.

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