Monthly Archives: October 2012

Industry Migration to Complicated

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As an industry matures, does it become less complex and move into the complicated domain on the Cynefin framework? Catherine Walker asked this question in a recent post and these are reflections.

Industry Migration to Complicated on the Cynefin Framework

As  a whole, the industry itself does not move from the complex domain to the complicated domain. It is closer to the images above.

Emerging Industry

When it starts, there are a lot of unknowns. As we learn more about the system, some practices become stable, but require expertise. Some practices even become well-known and move into the simple domain.

Developed Industry

Over time, there are more practices that are stable, more predictability and the industry can be well understood by experts. There are also more best practices. However, there is a danger in the simple domain (and probably also in the complicated domain) that something will tip the system into chaos – like when our Ferris wheel cracked in Melbourne.

With most industries, we will continue to push the boundaries and this is what will keep them active in the complex domain. It is likely that industries ‘pulse’ between the two diagrams, moving back towards the emergent picture when we learn that we did not really understand as much as we thought we did. An example is positive psychology of recent times.

Obliquity

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Thank you to Dave Snowden for his pointer to this John Kay post in a recent Cognitive Edge blog. It is a long read and I highly recommend making the time to read it.

Building on my recent post about the Gradient of Misinterpretation, we can be better off if we move indirectly towards a goal.

Many Paths to an Outcome

Here is my version of why obliquity can be good.

  • The first path taken is direct and leads us to our expected outcome of a box
  • The two middle paths lead to good and not-so-good outcomes, we are still looking for a box, and we find other things on the way that might be better
  • And the last path takes us under the mountain – sometimes other pathways are not so obvious

So how does this relate to the gradient of misinterpretation? It comes back to ambiguity, if we are a little ambiguous about what we want to achieve and how we describe it, then the pathways we take to understand it can take us to more interesting places.

 

Framework Examples

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It can be tricky to come up with examples to use for the Cynefin domains when we are facilitating workshops.

The examples need to be easy to understand, but not so close to the topic of the workshop that accidental bias is introduced.

It turns out that the word ‘chicken’ is  great source of examples.

  • Simple – Chicken, everyone knows what a chicken is
  • Complicated – Gender of chicks, it is a specialised skill to tell the gender when they are so young
  • Complex – Why did the chicken cross the road? The answer to this joke can be different every time and hard to predict
  • Chaos – A headless chicken
  • Disorder – a fox in the henhouse

Learning in the Cynefin Domains

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If you are not familiar with the Cynefin Framework by Dave Snowden, you might want to have a quick overview by watching The Cynefin Framework

Different management methods work better in some domains compared with others, so I was wondering if different learning styles might also be more effective in some domains than others.

Learning mapped onto the Cynefin Domains

Notice that the lower two domains, Chaos and Simple, focus on learning by repetition and the upper two domains, Complex and Complicated, focus on higher forms of learning (and unlearning).

In disorder, meta-learning might be more useful or perhaps the act of learning is one of the things that pull us out of Disorder into the other domains.

Learning in the Complicated domain is how we develop our expertise – the more patterns and interactions we can learn about, the better we get at predicting how to get good outcomes and avoiding re-work and waste.

Unlearning is more important in the Complex domain – the more patterns and predictions we are good at, the less likely we are to notice the emergence of something new.

Scope

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Is it better to start with a large scope and reduce it, or start with a small scope and increase it?

Argue Scope Out

It seems easier to start with the full idea – everything that we want – often this ends up costing more than we have and taking longer than we want.

So we do more work to come up with options in order to deliver something in the time-frame and for the money we have – so many decisions.

These can be hard to explain to the decision-makers and stakeholders and results in a lot of frustration, unmet expectations and further delays before we even start the work.

Argue Scope In

Here we start with the Minimum Viable Product, the smallest part of the idea that can provide learning and/or value.  If this cannot be done in the time-frame or for the money we have – then stop now.

The conversations we have about adding scope to the work are about the solid reasons to add it and about the cost and time-frame impacts.  Each piece of scope added is carefully considered before it is agreed to be added.

Any changes to costs and time-frames are much easier to explain to our stakeholders and decision-makers.  The conversations are more positive and productive, decisions are clearer and we can get on with doing valuable work more quickly.