Virginia Lacayo, Virginia. 2007. “What complexity science teaches us about social change?”
Virginia. Lacayo. n.d. “The limitations of the machine metaphor in research and evaluations of communication for social change interventions.”
David Peter Stroh. 2009. "Leveraging Grantmaking - Part 1: Understanding the Dynamics of Complex Social Systems". The Foundation Review.
Complexity science theorists argue that the world of social change efforts are oversimplified into linear processes that are not meaningful and representative of the actual ways that social change happens. As such Lacayo argues that: “Needed are new theories and methodologies that respond to the notion of social change as a complex, nonlinear, contradictory, emergent and self-organizing process, instead of address it as a complicated one.” In this project, complexity science principles help to make explicit patterns within complex adaptive systems, such as organizations, the relations that exist between patterns, how they are organized, sustained, and produce different results and outcomes. In contrast to models that ask us to articulate the reasons why a program should work after actual implementation, complexity science theorists argue that we should focus on how systems actually behave. There are a few shared principles, which give shape to understanding the dynamic ways that living and adaptive systems evolve and sustain themselves.
- The whole is greater than the sum of its parts
- Order is emergent and self-organizing
- The system changes when it chooses to be disturbed
- Free flow of diverse information is essential for the system to evolve
- Planning the unpredictable
- Complex adaptive systems are history and context dependent
For more specific descriptions, see the link to Lacayo’s article.
In order to evaluate these trends, multiple steps may be taken. At the same time, Lacayo acknowledges that switching entirely to a complexity evaluation framework may be overwhelming to some. As such, she suggests that even adapting some of your evaluation approaches and models to resonate with key principles of a complexity science approach will yield positive results. Some examples of these good principles include:[1]
- Replace the “search for best practices” with “facilitating good principles”. To support this goal, the evaluation design should be as simple and self-documenting as possible. It should include simple, iterative activities, and it should be totally understood by as many stakeholders as possible.
- Adjust M&E approaches to allow for learning from unexpected outcomes (e.g.outcome mapping), rather than retrospectively rationalizing that they were intended all along. Incorporate multiple strategies, methodologies, cycle times, dimensions and informants and triangulate the information obtained from these sources often. Because a complex system has a structure that is nonlinear, open, and multi-dimensional (micro and macro levels), an evaluation design cannot pre-determine all factors that will be of interest. Triangulation of informants, strategies and timeframes will help the evaluation program represent the complex dynamics of the system better. By including a wide range of approaches, CAS [complex adaptive systems] methods of evaluation integrate the best of many disciplines and methods that were previously irreconcilable.
- Make information about the evaluation process open and accessible to all stakeholders from the designing phase. By being explicit about decisions and processes, evaluation becomes an effective transforming (reinforcing) feedback loop (so the evaluation becomes a part of the intervention, rather than some irrelevant activity).
- Few, simple rules: A short list of simple rules gives coherence across scales of a complex system…
The following rules might be sufficient to establish such a reflective evaluation process:
- Evaluate to inform action.
- Communicate findings to others in terms they care about and understand.
- Focus on "differences that make a difference."
If all stakeholders of a program followed these three rules, they would generate a cluster of evaluation activities that would look different than many traditional evaluation plans.
5. Evaluate and revise the evaluation design often. Because the CAS baseline is constantly shifting, the evaluation plan should include options for frequent and iterative reconsideration and redesign.
6. Make learning the primary outcome. Effective adaptation is the best indicator of success in a complex system. Match the type of evaluation to the maturity level of the system.
For an example of key systems thinking and complexity sciences tools, see David Stroh’s "Leveraging Grantmaking - Part 1: Understanding the Dynamics of Complex Social Systems".
Strengths:
- Complexity science notions of change align with feminist notions of change as complex and longer-term processes stimulated through relational, normative, and structural shifts.
- Focusing on assessing unpredictable or unexpected outcomes as well as reversals of progress are central for better understanding the change that women’s organizations seek.
- The focus on learning is central to better understanding how to improve organizations and programs and is a central goal of many women’s organizations.
- Attention on the evolving nature of social structures, relations, and organizations and the need to continually adapt our models strengthens assessments and its results.
- Engaging stakeholders in the design and assessment phases ensures local relevance, ownership, and more correctly specified variables for evaluation.
Weaknesses (or not designed for):
- The multiple strategies and feedback loops associated with systems thinking can be difficult to assess and to communicate to others.
- The focus on the state of broader systems at play may obscure the learning about how implementation strategies contributed to or did not contribute to change.
- The complex processes that are identified and evaluated can be difficult to assess and track in a systematic and efficient way over time.
[1] Guidelines reproduced from Lacayo. N.d. “The limitations of the machine metaphor in research and evaluations of communication for social change interventions.”