Three Cynefin Ahas

Over the last year I’ve been increasingly influenced by ideas from Cynefin, created by Dave Snowden of Cognitive Edge. If you want a good introduction, Liz Keogh recently blogged a good explanation. I’ve realised that there are 3 key changes in my thinking, some completely new, and some reinforced by a better understanding of cognitive complexity. None of these are unique to Cynefin, and Cynefin contains much more. This list is my take, rather than any official list, although if you know Dave’s work I’m sure you’ll recognise a lot of the language!

1. Evolutionary Potential. Even though I’m a fan of Systems Thinking, I’ve realised that in complex situations, defining a future state and closing the gap isn’t the right approach. I still find system archetypes such as Tragedy of the Commons useful, but more in understanding the current situation than defining a future one. Instead I prefer to explore the evolutionary potential. There may be many different answers, some of which are not yet know, so experimenting, in a safe to fail way, helps evolve to the potential. An interesting case of this is exaptation, where a function is used for a purpose it was not originally adapted or selected for. My most recent aha related to evolutionary potential was that even though complex systems aren’t controllable, they are dispositional. In other words, while we still might not be able to know what the outcome of a change will be (let alone the output or activity to get there), but we can determine whether a change has a positive or negative impact on the overall system.

2 Sense-making. Cynefin is primarily a sense-making framework. This means that the data precedes the framework, as opposed to a categorisation framework where the framework precedes the data. Thus, rather than trying to figure out where an example should go in a matrix, examples are positioned relative to each other based on some criteria, and then boundaries are drawn subsequently. This makes sense-making much more dynamic, and what becomes interesting is not the classification of whether something is complex or complicated, but how things transition across the boundaries. No domain is better than any other as each is contextual. Moving from complex to complicated may be appropriate when optimising or exploiting. Equally, moving from complicated to complex (via a shallow dive into chaos) may be appropriate when wanting to innovate or explore. Further, any scenario is often in multiple places at the same time (after all Cynefin translates from Welsh into “place of our multiple affiliations”). Elements may be simple, complicated and complex, and narrative becomes an useful tool for understanding the differences.

3. Narrative. One of the main benefits of Kanban Systems that attracted me was the power of the contextual approach. A Kanban System is something that is overlaid on top of an existing approach to better understand and improve it and narratives are a great way of discovering, exploring and understanding aspects of a context. Collecting a set of anecdotes about best and worst experiences in a context creates a form of knowledge against which to pattern match for similarity of new situations, leading to better insights and decisions as to how to manage those situations.

Putting those three ahas together, I can imagine applying them through working with organisations to collect a range of narratives, help make sense of them by contextualising them with Cynefin, and then facilitate the creation of appropriate actions to make an impact on the business. Those actions might be safe to fail experiments, based on lean and agile principles, to explore the evolutionary potential for complex problems, or a more direct application of lean and agile practices for complicated problems. Or more likely a hybrid of both!


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2 comments on “Three Cynefin Ahas

  1. Very much appreciate you being brave enough in attempting these theoretical model mergers. It is so delectable thinking about blowing the doors of Agile and Lean principles again in attempts to better understand their contexts of relevancy and otherwise.
    Perhaps in contribution to extension of narrative databases that provide scientific merit to principle/context matching. Perhaps to inform on unlikely choices in the gambit of tactical implementation of the choices.

    The notion of Sense-making using significant data points and applied contexts to help inform our knowledge-model choices in deliberate attempts to drift intractable problems to the complicated space — a tasty drip I won’t be able to avoid for long.

    I think your highlight in recognition of items drifting the opposite way across the complicated to complex boundary is well spoken. Immediate thoughts spring to mind on skill development in the ability to extract the complex from the complicated aspects of a juicy problem.

    Cheers and thanks Karl.

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  2. This is my thought about cynefin:

    more thoughts:
    I feel Scrum is a process of dealing with complex domain. The process is iterative, so it fits the probe-sense-respond model. But when you look at each sprint, it will fit into simple or complicated domain. Since during each sprint, the requirements are clear, the expected results are clear, and actually each sprint can be treated as waterfall, so it belongs to simple or complicated domain. So I think the Scrum tires to split a complex problem into many small simple or complicated problems.

    But what I don’t understand is from the system thinking perspective, can a complex system be divided into smaller simple parts? I believe only complicated system is the sum of its parts.

    So far I feel Scrum just assume that split a big problem into smaller sub problem will work,
    I hope Scrum could provide more scientific explanation why it works.

    On the other hand, if we treat the complex problem using solutions of simple/complicated domain, like waterfall methodology, then the problem will move from complex domain to chaotic domain, that is why we have so many bugs, fire fighting…

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