Complexity theory and evaluation
Updated: Nov 16
Often, evaluation follows linear theories of change and predictive models of outcomes. However, one assumption that we make in our evaluation of our community partnerships is that organizations and communities are complex adaptive systems (CAS). A CAS is defined in terms of its parts, the behavior of those parts, and the emergent behavior of the whole. The characteristics of a complex adaptive system are that it is dynamic, massively entangled, scale independent, transformative, and emergent (Eoyang & Berkas, p.4, 1998).
Dynamic: “An evaluator may be able to frame expectations, but the self-organizing nature of the system may result in completely different outcomes than those expected” (Eoyang & Berkas, p.4, 1998). For this reason, our evaluation uses tools that help us to find unanticipated outcomes of our work and to frequently revise our program and evaluation design when the data indicates successes or failures.
Massively entangled: Essentially relationships are interrelated, “complicated and enmeshed” and as such the evaluation should reflect this level of interdependence and complexity. However, because a CAS has this level of complexity, we can only confidently use quantitative and concrete data for shorter periods of time and we find that qualitative data is more appropriate for longer time frames.
Scale Independent: “A CAS functions simultaneously at many different scales of an organization” (Berkas & Eoyang, p.6, 1998). One way we capture this is by considering our evaluation to exist within a dynamic ecosystem where we look for different levels of impact (eg. individual, organization, community). This would also encourage us to think of evaluation as part of the intervention and to make the evaluation process and results transparent to all stakeholders. (Berkas & Eoyang, p.10, 1998).
Transformative: Evaluation systems must adapt to the system. One way to encourage this is to involve as many stakeholders as possible in the evaluation design, to create feedback loops to improve the quality of programming, and to use evaluation to celebrate bright spots or programmatic strengths (Eoyang & Berkas, p.7, 1998).
Emergent: Small changes in the intervention system can have massive implications for the programming. To see emergence, CAS and evaluation encourages evaluators to look at patterns over time. (Eoyang & Berkas, p.8, 1998). Evaluators have used developmental evaluations as one tool to capture emergence.
Eoyang, Glenda & Berkas, Thomas. (1998). Evaluation in a Complex Adaptive System.