Does complexity science give us predictive tools or only descriptive ones?
Description is an important first step in prediction, in that we need to understand our situation before we can begin to sense the direction in which it is moving. However, complexity science does not stop there. Because it helps us capture non-linear interactions, dynamics, and connections in the system, it can help us see where the energy is and where it is going. This gives us a deeper sense of what lies beneath within the system. Traditional tools of prediction and forecasting often give us a more exact prediction than what we know deep down is truly possible. They can also be limited by reliance on linear behavior, as if nothing will change or interact between when we make the prediction and when the outcome happens. Complexity tools do not assume this linear exactness, but provide us with ranges, approximations, and storylines to consider. This offers us ways of keeping our finger on the pulse so we can adapt our strategies as the system changes.
