There is strong tendency in the evaluation field right now to treat everything as if it were complex. In my view that is both incorrect and wasteful. Most evaluations cover situations that have simple, complicated and complex aspects. Methods designed to understand complex aspects are generally far more expensive and time consuming than simple aspects. Therefore Cynefin can help me design much more cost effective evaluations. For those aspects of a situation that fall within the simple domain, I can identify cheap reliable methods of data collection and analysis. For complicated aspects, often using single or multiple ToCs, I can identify rather more expensive but appropriate methods of data collection and analysis. For the remaining aspects that are complex there are few, often quite expensive methods in terms of time and cash but at least I am focusing them where they need to. These can be guided by a Theory of Action or at least a set of “when X happens do Y” set of rules. So this ontological aspect of Cynefin is invaluable to me. However, in this blog I want to reflect on the value in evaluation of the epistemological aspects of Cynefin.
If you read Rick Davies' comments on my last blog he mentioned that models were, essentially, representations of reality. Taking this slightly further you could say that the purpose of models is to reflect reality in a way that helps us cope with it. It is something very poorly understood within the evaluation field, which tends to use models as squashed down versions of reality. Let me explain with an example commonly used to help describe the difference between simple, complicated and complex aspects of a situation. It is often said that “baking a cake” is a simple endeavour, sending a rocket to the moon is a complicated endeavour and bringing up a child is a complex one. I'll deal with child rearing at another time, but for now let's think about the cake. I was originally trained as a biologist and my knowledge of physics and chemistry tells me that baking a cake is most certainly not a simple matter. It is a complicated perhaps even complex process. Yet we have developed simple models to make it possible to prepare something you can stand a candle on without a degree in biochemistry and atmospheric physics. It's known as a recipe. We know it's not perfect and that sometimes we end up with a gooey brown mess but it works most of the time and we accept that. We are treating this complicated situation as if were simple and mostly getting away with it.
This slipperiness of how we see things and what they may be has other uses. I was once running a workshop in Scotland using the Cynefin framework to evaluate activities relating to health care for the rural isolated elderly. I'd just done the Post-It business and there was a shout from the room. Turns out that two participants, both experts and senior in their trade, had put the same aspect of the rural elderly health care situation into two different quadrants; one complex and one complicated. It took us very little time to reveal that they had understood the that situation quite differently and consequently their approaches to manage it were at odds with each other. The two senior medics went into a huddle and within ten minutes had come to a resolution. It wasn't that either was right or wrong, or that the allocation to the Cynefin quadrants were right or wrong, or even that the model underpinning Cynefin was flawed. It was just that Cynefin had been used in an epistemological manner – as something that generated useful learning.
This epistemological use of models is often poorly understood, especially in the evaluation field. The evaluation field tends to argue whether its models reflect reality rather then whether they are “useful”. So I find Cynefin helpful in softening this orientation. At some stage in a Cynefin based process someone, somewhere, will always ask what happens when the same thing appears in two or more quadrants. It's a good question, and because I come from the “appreciative” end of the systems field (that subsequently led to soft and critical systems approaches) one I am familiar addressing. However, Cynefin stops me waving my arms around in some vague manner and allows me to point directly to a framework that illustrates the point that how we frame things determines how we respond to things. Evaluators like to reify, they like to have their feet on solid ground. It's in their blood. It's the field's great strength and its greatest weakness. Cynefin helps solidify ground that might otherwise be too marshy.
Enough about Cynefin, the next blogs will be meditations on the trials and tribulations of using Sensemaker within an evaluation context. A bit of a challenge it turns out.
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