Let’s not throw out our ability to see cause and effect with the complexity bathwater

 

We hear so much that we can’t make predictions in complex adaptive systems that we start to believe it when we have a huge amount of counter evidence. Complex systems are those in which a perfect understanding of the individual parts does not automatically convey a perfect understanding of the whole system’s behavior. This means one can’t be certain what a change in one part of the system will have on another. But this doesn’t mean that there isn't a large degree of cause and effect for the system as a whole.

Let’s consider the following situations:

• Product managers pushing more work on their teams than they can handle
• People working on multiple projects projects being staffed by part time people
• People working on large (3 months or more) batches of work
• Teams being motivated to focus on their own interests
• Managers micro-managing their teams
• No planning across the teams that need to work together
• Product quality is low

When you see these things I suggest you can readily predict the results. And that you can reasonably accurately predict improvement if you improve them. The question is how? But the first step is seeing these issues.

What’s forgotten in the complexity conversations is that seeing what our problems are is looking in the past – which complexity theory does not say we can’t do. We can see cause and effect after the fact. While we can’t accurately predict what our changes will be, we can accurately see what’s causing our problems.