Now we understand what the static domains are, we can map behaviours into the framework and we see that we have a set of dynamics emerging that problems, organisations, and individuals experience within the complexity domains.
Dynamic 1: The grazing dynamic
The grazing dynamic is where the problem never leaves the complex domain. The individuals or teams constantly evolve and change the problem definition and solutions over time.
Dynamic 2: The stable dynamic
The stable dynamic is mostly commonly seen in Agile development where complex adaptive problems are iterated over, and then once a solution for a small period of time is found, the teams can develop and deliver with a reasonable consistency for a period of time. Then the environment, feedback, or mid of the stakeholder changes. The teams then iterate on a new problem and set of solutions.
The stable dynamic has a number of decision points on it that allow for elements of the problem to either remain in the complicated domain, or even become commoditized and move towards to the Obvious domain.
Dynamic 3: Moving through Chaos
If a problem is solved with tools applicable to the complicated domain and then the constraints applied are too tight or not reviewed through complacency, then the problem may re-occur as a crisis and temporarily move to the chaotic domain.
This is shown in orange in the drawing.
Dynamic 4: Catastrophic failure
If a problem is moved to the Obvious domain too soon, and constraints applied too heavily, and the environment changes or the constraints were not appropriate, then again, through complacency, the problem may fall over the Obvious/Chaotic cliff,and the team, individual or organisation may experience catastrophic failure. This may or may not be recoverable.
A closer look at the dynamics
Diving even deeper into the model, we can see that each domain is not a solid lump, but actually made up of many more states. Each state is a behaviour which has a tendency to move to an adjacent square on the model.