We see complexity through a lens of our emotions, biased memories and very limited objective assessment. Complexity science sees complexity in a more objective, quantifiable and consistent manner. The best way to understand this gulf between our everyday (mind’s eye) perception of complexity and the scientific view is to see it. To do this I have put together a very simple example.
Let’s assume we have two processes and we have measured a characteristic of the process in some way over a period of time, say 50 days. Take a good look at the graphs. Intuitively we would say that the plotted behaviour of both processes ‘looks’ complex. If we were in the thick of the process and just perceiving what was going on, we might think it was not too complex or may be chaotic and our view may change yet again when we see the graphs. But in fact in complexity science terms, process 2 is not complex, but random. The telling difference is if you plot a graph for each process of each value against the previous day’s value.
From the right hand graphs you can see that process 1 has a bounded shape to it. That is because there is a very clear dependency between the value at a point in time and the previous value. This is due to feedback, which is in the maths that simulated process 1.
In the random process 2 there is no feedback and so there is no relationship between the value at a point and the previous value so the graph is a mess. Yes in theory the randomness of process 2 is still complex, but it is a very different domain of behaviour in terms of complexity science, but the big point is that we cannot tell the difference between randomness and highly complex behaviour. What’s more important is that for process 1 we may be able to moderate the behaviour using ideas from complexity science, but for process 2 we cannot. So we could make an erroneous decision to either not bother to moderate them, or try to moderate them and find that we have wasted time and effort with process 2!
The general point is that differences in complex behaviour can be very subtle and we cannot pick out the differences, only a level of objective analysis can.
So the idea of applying ideas of complexity science to our intuitive view of complexity can lead us to make completely false assumptions about the behaviour of our organisations and hence about the appropriate actions to take. To understand complex behaviour we have to look at it analytically and not through the mind’s eye.
 For those who are interested it is a modified logistics map.
 Ok, there is because it is pseudo-random, but it is very deeply hidden.