Complexity has consequences for how we reason about prediction, explanation, and causality. With a complexity lens, we gain knowledge about program activity and outcome that we could not otherwise obtain. Such knowledge is important because the programs we evaluate, and the outcomes they produce, comprise complex systems that exhibit complex behaviors. Evaluation must recognize these realities. To convey these concepts the workshop will begin with an overview of elements in Complexity Science that are particularly appropriate for evaluation, and for the evaluation of transformation in particular. We will then move to tools to define appropriate context-specific definitions of transformation, and models for depicting transformation. Embedded in tool use will be knowledge of behaviors of complex systems, decisions about when and when not to invoke complexity, choosing appropriate methods for evaluating transformation, and appreciation of what we can and cannot know about change in the complex system that constitutes transformation.