Complex and adaptive

On the complexity of change

Let’s forget about changing anything. Just for a little while. Let’s just think about complexity. Something complex. A complex process. Got it? Why is this process complex? What makes it complex?

First answers are simple: a process with more than one actor is more complex than a process with just one actor. But it is not just the participants. Many natural or industrial processes are complex, and they do not necessarily even have participants (humans) that act in them. So, there can also be many components. And if that were not enough, more often than not there are many variables. You remember these variables from math classes in school.

x + 7 = y

This is a nice linear equation – and thus not complex. For each x there is exactly one y, which can be calculated, if you know how do do this sort of thing.

Think about the complex process you have in mind. It does not just have one variable, one x, that changes or can be changed. Most processes in life, in society, in biology, in physics, in nature, … in most places where we care to look, have more than one variable. More than one (in)observable trait, characteristic, or feature that can change or that can be changed.

Now that’s OK, you say. We just have to look at a few more things. Right! Problems arise when there are very many, often too many, to always keep our eyes on, to look out for, to consider. And not only that. Each of these variables, each x, if you like, does not just have one dependent y. More than one variable can depend on each changing variable.

I am changing the period of time I use for exercise in the morning. I am changing time t. Time t influences my fitness level; I am increasing muscle mass and flexibility. Because of the increased muscle mass, my metabolism changes during the day. I feel better, I am more agile, I move more and quicker, burning more calories than on the days prior. And by increasing time t for exercise, I am reducing time r for reading … Twitter, my favorite book, a newspaper, or some emails. I am also reducing time c for cooking, so I will have to have my lunch prepared the evening before or will have to go to the cafeteria to buy something to eat.

You get the point.

A complex phenomenon does not just have many variables. Each of these variables potentially interacts – metaphorically speaking bounces off and changes – one or more other variables. Overstating just a little bit: each of the many variables changes all the time, in concert and against each other.

Did I say at the beginning: Let’s forget about change for a little while? Impossible. We quickly returned to the concept of change. Change is part of complexity and complexity is part of change. We cannot – and should not – consider one without the other. [Maybe just for a quick thought experiment, or if we are really tired in the evening.]

What are the consequences? Complex phenomena are in constant flux, change constantly. That’s why we often talk about complex dynamic systems. Variables interact with one another, components interact, actors (participants) interact. In these many continuous or iterative interactions, each variable, component, and actor are also prone to change. They co-adapt. Especially for social systems, we often use the label complex adaptive systems (CAS). And if we want to understand change better, be able to influence it a little bit, or just deal with it, it is useful to look at some of the characteristics of complex adaptive systems.

• CAS are likely to be on a nonlinear trajectory, which means a change I put in does not necessarily result in a proportionate change to come out.
• CAS are sensitive to initial conditions. The variables, however small they might be, that were there from the very beginning are most likely to have had a relatively large impact on the whole process, simply because they have been around for long enough.
• CAS have attractor states – states they are more often and more likely in. They also have repeller states, states which they could reach theoretically but never or hardly ever reach.
• CAS are likely to reach an equilibrium – like a standstill, change is very close to zero – if no new energy enters the system.

I am glad I got this out of the way. And maybe so are you. Remember that I said it is often useful to apply a theoretical lens to gain a better understanding of a problem? At some stage I had to introduce the lens. In subsequent posts, I will look at these characteristics of complex adaptive systems, one by one. And I will show for each one what role their understanding can play in solving personal problems, problems at work, in social interactions, or just around the house.

The neat thing with these CAS is that there has been a lot of research that tried to figure out how to get a better handle on the complexity. And I am as sure as one can be that what we learn about the ever-changing complexity will come in handy almost every day, when solving problems. Whether this is in your personal life, when making leadership decisions, or simply when you are trying to fix something that you believe needs fixing.

And to finish off, if you’d rather read the texts on the Complexity of Change in one possible order … a table of contents is emerging.