Complex or complicated?

Is the problem posed by trying to understand the way the spine works a complex problem, or a complicated problem - or both?

What does it matter, you might ask? Well, let's just see if we can shed some light on the difference between the two.

I think that a complicated problem is one where there are lots of parameters. For example, if we have two members of a set (a, b) there can only be one relationship between the two. For three members, it is three. For 4, it is 6. We can see that as the number of members of a set rises, there is a step change in the number of interactions between the two (a triangular number sequence).

So far so good. Because all that means is that a complicated problem is one where it becomes more difficult to 'see' what is going on as the number of parameters rises - but, there are in principle no computational impediments to understanding the system, if we have sufficient time and a powerful enough number cruncher.

Now, I wonder if the spine is like this? To understand this, look at weather forecasting. People used to think that forecasting the weather was an example of a complicated problem. "If only we could measure all the starting parameters of a weather state, we could forecast the weather etc.". But, experience since the mid 60's and the work of people like Lorenz has shown how calculating changes in the weather state is incredibly difficult. This seems to be for two reasons. First, there are non-linear relationships between many of the different weather 'factors'; this makes it even more difficult to predict a change in the state of the system for any given number of, and relationship between, and change in variables. Secondly, the state of the system at any point therefore becomes extraordinarily sensitive to the starting position of the variables (the so-called 'measurement problem', where small changes in starting conditions have dramatic effects on the predicted system state). Thus we find weather forecasts are fairly reliable up to 4-5 days, but still then tail off dramatically, despite impressive models and incredibly fast super-computers compared to 20 years ago.

And the spine? Well, there are clearly a huge number of variables that affect spinal function and healing responses (two of the key things we are interested in). We know that physiology of function and of structure is all about non-linearity (for example, the way that collagen can suddenly fatigue, or how homeostatic negative feedback loops can fail). And we know it is impossible to measure accurately any of the variables in the system!

So the spine is more complex than it is complicated. Why does it matter if the spine is an example of a complex or a complicated problem?

Well, the reason is that despite the apparently awesome challenge posed by complexity, it is possible to see beyond the complex and to simplify in a way that adds value and does not just produce apparent insights. This is something you cannot do with a very complicated system (you can't extract insights from a complicated problem, you can only crunch it).

So because the spine is a complex problem we can legitimately try to generate reliable heuristics about reactions to treatment and testing (see the TED talk below for a neat explanation of this). Simplification might actually extract value. Ecologist Eric Berlow doesn't feel overwhelmed when faced with complex systems. He knows that more information can lead to a better, simpler solution. Illustrating the tips and tricks for breaking down big issues, he distills an overwhelming infographic on U.S. strategy in Afghanistan to a few elementary points.