When I first read a draft of the paper: Evaluating Complexity: Propositions for Improving Practice a few weeks ago, I found myself relaxing into the topic. I saw that it did, in fact offer some true–and useful–advice for those of us who look for ways to assess our impact as we work to see, understand, and influence patterns in complex systems. Why, I asked myself, did their work ring so true? I reflected on the characteristics of complex adaptive systems (CAS) that limit the capacity of traditional evaluation methods to inform our evaluation decisions in effective and meaningful ways.
In Adaptive Action: Leveraging Uncertainty in Your Organization, Glenda Eoyang and I define complex adaptive systems by three particular characteristics:
- A CAS is open to multiple forces. Those forces act in interdependent ways to bring about surprising and unpredictable events.
- These systems are high dimension. They are characterized by diversity at all scales and across all boundaries. This diversity is considered in terms of both frequency (how often diversity occurs in the system) and scope (the degree of difference between and among the agents in the system).
- Finally exchanges in a CAS are nonlinear. There is no direct cause/effect relationship. What happens today is informed by past experience, and what happens today shapes tomorrow’s reality in unique ways.
These three characteristics–open, high dimension, nonlinear–are at the heart of our work in human systems dynamics. They also represent challenges that recent, systems-based evaluation techniques attempt to address.
As I read the propositions that the FSG authors have identified, I recognize that each one can address evaluation challenges that are inherent in a CAS. Additionally I recognize that it is not a one-to-one relationship between the individual propositions and any of the characteristics. They are interdependent.
The underlying value in these propositions is that they provide a common place to stand in the conversation about evaluation in complex systems. Regardless of the approach an evaluation takes (CAS, systems, appreciative inquiry, developmental, etc.), these propositions can inform decision making and action throughout design, implementation, data collection, and analysis. They help us account for realities of our systems rather than forcing us to ignore them or pretend they don't exist.
Together, these propositions offer a “next step” in this emergent area of system and complexity-based evaluation, and they do so without dismissing traditional evaluation models and approaches. Those earlier practices work exceedingly well in systems that are closed, low dimension, and linear. However, now that we know more about the complexity in human systems, we can have our cake and eat it too. We can use traditional approaches for closed systems where they work, and use these propositions to guide our decisions in the turbulence and messiness of complex systems.
Royce Holladay, a lifelong educator, uses principles, models, and methods of human systems dynamics to help leaders see, understand, and influence patterns of behavior and decision making. She is currently the Director of The Network at Human Systems Dynamics Institute.