Capturing Complexity in Program Theories

One of the key challenges we face as evaluators is the need to distill our analysis down to a manageable number of indicators while capturing real-world complexities. In this presentation, three types of program theories are described that help visualize complexity in different ways. The first is the context-mechanism-outcome (CMO) configuration, which describes the generative processes (mechanisms) promoting behavioral changes (outcomes) in a given setting (context). The second is the causal loop diagram, which is designed to depict complex relationships between indicators and outcomes—including positive and negative feedback loops as well as emerging, delayed, or unintended outcome trajectories. The third is the stock and flow diagram, which describes how programs work in terms of variations in stocks (key program outputs and outcomes) and flows (program processes). In this session, the presenter will discuss key concepts in complexity science and how each of these three program theory models address complexity.