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This article presents a case for more rigorous application of complexity science in our efforts to evaluate activity that seeks to bring about transformative change. It builds on the work that is already going on in the evaluation community. Three constructs from complexity science are employed – sensitive dependence, emergence, and social attractors. The paper argues that if–then logic is recommended for small-scale change within transformation efforts, but that to evaluate transformation writ large, data from if–then evaluation must be embedded in, and interpreted in terms of, complex behavior. Methodologies for evaluating within this framework are presented. The argument is linked to a definition of transformation that is multidimensional, non-linear, and measurable. The paper is built around a generic model of transformational change and shows how that model can be customized for specific transformation scenarios. It also shows how evaluation with respect to complexity can be accomplished with methodologies that are well known and well-practiced in the evaluation community.
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