Complexity science is an emerging area of systems thinking and modelling. It is not a single theory and is highly interdisciplinary. Rather than focusing on the elements of a system it focuses on the interconnections between them. It can be used to aid the understanding of mechanisms through which unpredictable, unknowable and emergent changes may happen. It also recognises that with complex systems the best course of action may be highly context dependant.
Most analysis of future events looks at the likelihood of it occurring and the wide range of potential impacts. In many cases system dynamic modelling will be used. However, future events can be caused by a wide range of interconnected element and random events, with outcomes that cannot be predicted.
There is increasing understanding of the underpinning theories of complexity science and the potential applications to real world problems. It is likely that as the science progresses it will have an increasing role to play on anticipating the future.
The differences between system dynamic modelling and complexity science are summarised in the following table:
|System dynamic modelling||Complexity science|
|Dominant rules and potential equilibrium||Tends to defy calculated equilibrium|
|‘Control system’ that guides and shapes||Possibility of self organisation|
|Elements of system can be understood||Elements interdependent and change with context|
|Rational process and predictable results||Dynamic processes defies prediction|
|Changes with rules based learning||Perpetual change, so learning is a constant factor|
One on the current problems in the analysis of future events is the difficulty understanding the complex web of interdependences associated with them. As complexity science progresses, it could play a role in addressing these interdependences.
(Written by John Reynolds)