Motivated by a survey I was asked to complete, I spent some time distilling my thoughts about how complexity can be applied to the evaluation of transformation. It came down to six principles. I have written a lot on this topic, so if anyone wants the gory details, just ask.
- Evaluators deal with complexity in a metaphorical sense, but not in a technical sense as is found in research and theory in Complexity Science.
- To evaluate transformation, one needs to take Complexity Science seriously because complex behavior does explain how transformation works.
- Complexity touches on the evaluation of transformation in two ways. First, it speaks to the models, methodologies, and data interpretation that make up so much of the evaluation enterprise. Second, it affects understanding of what transformation is and how it behaves.
- It is not useful to think in terms of “complex systems”. What matters is what complex systems do. It’s knowledge of that behavior that affects evaluation work.
- Three constructs in Complexity Science suffice for most of what we need for evaluating transformation: 1- sensitive dependence, 2- emergence, and 3- attractors.
- Most of the methodologies we need to evaluate transformation are well known and much practiced in the evaluation community.
Evaluation Uncertainty 