For various complex reasons which started with a casual throw away remark at Lean Agile Scotland followed by a brief twitter storm I am spending two days in Dubai with a Kanban group. That includes the co-authors of Essential Kanban Condensed, namely David Anderson and Andy Carmichael. I have the latter to thank for this picture he drew during one of the sessions where I was talking about Cynefin and placing emphasis the boundary conditions, including seeing the complex-complicated boundary as a domain in its own right. To make that easier I used the plane model of Cynefin which was originally draw with the incomparable Max Boisot at the Academy of Management in Washington many years ago. It shows the boundary between Obvious and Chaos as a cliff, in the sense of catastrophe theory.
The reason for doing this is the creation of a deeper understanding of the boundary conditions and their nature. Now I’m not trying to over complicate Cynefin. The basic representation remains as is, but the value of Cynefin is that it can be easily understood in that form, but can also be progressively understood at deeper and deeper levels; this is one of those. So lets look at the four main boundaries, ignoring for the moment the ones to disorder:
- Obvious to Chaos
This is the complacency boundary, the imposition of inappropriate best practice or standard process regardless of consequence. Both wilful and accidental incompetence come into play here. It’s also the failure because of your competence in an old way of looking at things, to reference Clayton Christian’s work. To be very good at doing things the old way can be deeply problematic with the eco-system has shifted. I’ll pick that up tomorrow I expect as I will be talking about dominant predator theory then. Recovery (Chaos to Obvious) is both expensive and inappropriate so I have described this as a one way boundary.
An easy boundary to cross and I portrayed it as a slippery slope. In the Complicated Domain there is a degree of permitted variation, the system has adaptive capacity with governing rather than rigid constraints. It is all too easy, especially in consultancy drive improvement exercises to shift from a degree of variety in good practice to a monotone rididity of best practice. Done well, operating theatres for example, the shift has value but it is a heavy investment to achieve results and there is a lack of flexibility in change in consequence. So it is a journey that should have a wall with gate keepers, but lack of definition means that it is all to easy to dive into it. You can make the return journey, but if that is likely it is better the avoid the need and live with some degree of variation in the short to medium term.
Again a two way boundary, but each routing is different. A chaotic system naturally becomes complex as constraints emerge, in fact maintaining a chaotic state (no constraints, no connections in Cynefin terms) takes a lot of energy and resolution. The reverse is more difficult as systems don’t like randomness. So creating chaos for the use of a tool like MassSense requires a commitment and as such the boundary needs to be a gate. If you want a story metaphor think about the town of Wall in Neil Gaiman’s Stardust, named after the stone barrier that separates the town from a grassy meadow (if you haven’t read it do, if you haven’t listed to the Radio 4 adaption do, if you haven’t seen the film with Michelle Pfeiffer and Robert De Niro in magnificent form go seek it out but prepare to be disappointed by Ricky Gervais). The entry to the world of Faerie is fraught with danger, and for the star to pass is to loose life.
To my mind the most interesting and one I am thinking of making a domain in its own right in come representations. I’m playing with different metaphors here, but I’m thinking of an interesting valley where it is easy to linger but from which you have to move. I have one in mind a short walk north of me on the Marborough Downs with neolithic field systems and the Grey Wethers of free lying sarsen stone on the walking path to Avebury. The transition from complex to complicated can be achieved with the imposition of constraints produces repeatable results. So parallel safe-to-fail experiments run and create sustainable practice which is taken through linear iteration to refine, correct and scale it (for example SCRUM in Agile). The shift to complicated is desirable as it allows scale at reduced cost with increased certainty of outcome. The mistake people make is to stay in the domain when the constraints are starting to fail to produce repeatable results – then we need to move back to the complex.
So at a high level that describes the boundaries. However I am starting to think that the boundary between complex and complicated should at times be maintained as a semi-permanent space – something I have talked about as a grazing dynamic. We should maintain a space of partial certainties, while maintaining options. But that is for a future post.