I have the deepest respect and high regard for Wardley Maps and the Cynefin framework. They share much of the same background and evolution. Both are extremely helpful and modern frameworks for understanding,
much like Porter’s five forces model was back in the 1980s.
I adopted the same terminology (novel, emergent, good and best) when writing about the development of governance for 2050. In the article Revising the S-Curve in an age of emergence, I used the S-curve as it has helped us on several previous journeys. It supported our understanding of adoption and growth; it can now be critical in helping us understand the development and evolution of governance towards a sustainable future. An evolutionary S-curve is more applicable than ever as we enter a new phase of emergence. Our actions and behaviours emerge when we grasp that all parts of our ecosystem interact as a more comprehensive whole.
A governance S-curve can help us unpack new risks in this dependent ecosystem so that we can make better judgments that lead to better outcomes. What is evident is that we need far more than proof, lineage and provenance of data from a wide ecosystem if we are going to create better judgement environments, we need a new platform.
The image below takes the same terminology again but moves the Cynefin framework from the four quadrant domains to consider what happens when you have to optimise for more things - as in the Peak Paradox model.
The yellow outer disc is about optimising for single outcomes and purposes. In so many ways, this is simple as there is only one driving force, incentive or purpose, which means the relationship between cause and effect is obvious.
The first inner purple ring recognises that some decision-making has a limited number of dependent variables. System thinking is required to unpick, but it is possible to come up with an optimal outcome.
The pink inner ring is the first level where the relationship between cause and effect requires analysis or some form of investigation and/ or the application of expert knowledge. This is difficult and requires assumptions, often leading to tension and conflict. Optimising is not easy, if at all possible.
The inner black circle - where peak paradox exists. Complexity thrives as the relationship between cause and effect can only be perceived in hindsight. Models can post-justify outcomes but are unlikely to scale or be repeatable. There is a paradox because the same information can have two (or more) meanings and outcomes.
The joy of any good framework is that it can always give new understanding and insight. What a Wardley Map then adds is movement, changing of position from where you are to where you will be.
Why does this matter?
Because what we choose to optimise for is different from what a team of humans or a company will optimise for. Note I use “optimise”, but equality could be “maximise”. These are the yin/yan of effectiveness and efficiency, a continual movement. The purpose ideals are like efficacy - are you doing the right thing?
What we choose to optimise for is different from what a team of humans or a company will optimise for.
We know that it is far easier to make a decision when there is clarity of purpose. However, when we have to optimise for different interests that are both dependent and independent - decision-making enters zones that are hard and difficult. It requires judgement. In complexity is where leadership can shine as they can move from simple and obvious decision-making in the outer circle to utilising collective intelligence of the wider team as the decisions become more complex. Asking “what is going on here” and understanding it is outside a single person's reach. High functions and diverse teams are critical for decisions where paradoxes may exist.
When it gets towards the difficult areas, leadership will first determine if they are being asked to optimise for a policy or to align to an incentive; this shines the first spotlight on a zone where they need to be.