Complication vs complexity?

Complication is one of the problem organisations, their managers and employees, social communities, politicians and members face — and ‘complexity' could be part of the solution!

Complication relates to the entangled factors that feedback on each other as well as limiting constraints that hinder solving complicated issues by the step-wise isolation and cracking of subproblems which are thought to be dealt with sequentially or in parallel.

However, this complicated entanglement is often called complexity for marketing purposes, i.e. to relate to the (pop) complexity ‘boom’ and / or due to limited thought given to the problem definition.

However, complexity (in the self-organization sense) could be part of the solution: a relatively simple formula (or ‘generator’) that generates a wealth of similar but varied ‘answers’:

Mathematically one can describe it as recurring circular trajectories around / along an attractor in state space. If at first seemingly uninterpretable and unrelated variation in the observed data points can ‘suddenly’ be ordered in state space trajectories and thus ‘reduced’ to a simple generator, than a much more thought economical model of what is going on has been reached. It can be used to ‘manage’ (or at least deal better with) the imputed driving (causal) factors.

Now to something completely different … and to make things complicated and complex:

The complicatedness and complexity issue meet to a certain degree as a number of complicated problems contain (feedback) loops where often several factors are linked back to themselves across a number of other factors — which means part of complication may be reducable to relatively simple ‘complexity generators’.

While complication-complexity is bound to increase in the future we can still deal with its complexity by

1) analyzing relationships between the elements that make up complex systems,
2) accepting the fact that our view of the complex system(s) and the interactions in and between them need to be updated regularly and likely in changing intervalls
3) forming a ‘good theory’ based picture of what needs to be done and what should not be done (that entails conflict and debate as different perspectives meet)
4) forming an opinion on and empirically testing what can be ‘cut’ away, so reducing the complexity and not cutting the lifeblood connections — hopefully and again conflict-laden
5) as we learn iteratively along the way, we will get better at doing it in the above way.