Cynefin Framework: Unordered Systems


In my last post, I discussed the right-hand side of the Cynefin Diagram, the Ordered Systems. Today, I’ll discuss the left-hand side of the diagram, the Unordered Systems.

We hear a lot about chaotic systems these days, but Chaos is always a short term event. Chaos produces Novel Actions, stuff we can’t predict, and our best response is to act according to our values (not our past practice), see what happens, and support the best of the novelty that appears. It is difficult to have an effective change strategy in a genuinely chaotic situation. But, engagement by your organization or group will let you learn what you can from the turbulence.

Complex systems are largely the place where advocacy groups plot and execute their change strategies. Most typical organizational planning is focused on the Complicated Realm, however, and we often try to make our interactions as part of complex systems change fit the logic and “predictability” of the complicated realm.

This is a mistake, and continuing to interpret the complex as complicated (and amenable to procedurally focused planning and execution models) is a strategic error, weakening our change strategies and constraining the possibilities for real change.

In this complex realm, your organization or group and your change target become part of a single system that is changing together or coevolving. The longer you pursue change in a particular target, the deeper the coevolutionary relationship becomes. Put simply, you change the target and the target changes you.

Part of the change in you involves the learning you acquire about your target, which in turn is largely a result of the responses that your target uses to blunt or counter the change strategy you are implementing. Ideally, your learning from this coevolutionary relationship improves the effectiveness of your change strategy for this effort and for future ones.

In advocacy organizations, people who have been around for a while and have created and experienced change efforts against a target will often recognize a pattern in a current change effort that has either appeared before or which is a variation of one they have worked on in the past. This recognition of a pattern doesn’t dictate a specific advocacy action but provides a guide for creating a customized response to the current circumstances of the advocacy effort. Interestingly, efforts (I have seen many) to identify such patterns and make them explicit so they can be shared  with people who haven’t experienced them are largely failures. The pattern is best recognized and used in a real situation, and it is hard to pull out in the abstract (say for an advocacy manual); it is also hard for new advocates to grasp the importance of the pattern outside a real change situation. This is clearly different from the Complicated System where you can create a user’s manual for each component, and reuse already existing explicit information (from blueprints, procedures, experts, etc.) to solve a very specific problem.

In complex systems advocacy, you should try probing the target for its range of responses so that you can learn more about how it views your change effort, and how sophisticated its experience really is in the current engagement. Which is another way of saying that the success of your advocacy depends on your ability to learn from your current engagement with a specific target.

I know that the above is a mouthful. In my next post, I will use concrete examples from actual advocacy efforts to show how important this coevolution with our change target is, and better illustrate the weakness of a “complicated system, detailed planning, measurable outcomes” approach.

Interventions in Complex Systems


Author: disabilitynorm

hubby2jill, 2dogs, advocate45+yrs, change strategist, trainer, geezer, pa2Loree, gndpa2Nevin

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