Adaptive Evaluation: A Complexity-based approach to Systematic Learning for Innovation and Scaling in Development - Gokhale, Siddhant; Walton, Michael (CID Faculty Working Paper, 2023)
Nearly all challenges in international development tend to be complex because they depend on constantly evolving human behaviour, systems, and contexts, involving multiple actors, entities, and processes. As a result, both the discovery and scaling of innovations to address challenges in development often involve changes in system behaviour or even system-level transformation. This is rarely a linear process over time and can result in unexpected outcomes. Existing evaluation techniques commonly used in international development, including Randomized Control Trials (RCT) and quasi-experimental methods, are good at assessing specific effects of interventions but are not designed for the change processes inherent to innovation and scaling within a system. There is a need to reconstruct how we use existing measurement tools, techniques, and methodologies so that they capture the complexity of the environment in which an intervention or change occurs. We introduce Adaptive Evaluation, designed to learn at various levels of complexity while supporting the transformation needed to foster sustainable change. An Adaptive Evaluation uses three main approaches to work with complex questions—systems diagnosis, theorybased assessment of change processes, and iterative designs. An Adaptive Evaluation typically builds hypotheses from field-based interactions, emphasizes learning over testing, advocates open-mindedness with techniques, and appreciates the value of dialogue and participation in navigating complex processes. It can use RCT or similar techniques to analyse specific processes within a system or a development cycle, but these are embedded in a broader approach to assessment and interpretation. It is designed to be flexible and adjust to shifting contexts. Finally, an Adaptive Evaluation can be applied at any stage in a complex intervention's lifecycle, from the interpretation of the system and change processes to rapid experimentation, prototyping, and testing of select interventions, and then adaptation to different settings for impact at scale. This paper provides the theoretical basis for an Adaptive Evaluation—the main approaches, core ideology, process, and applications.

A Systems Framework for International Development: The Data-Layered Causal Loop Diagram - Blair, Courtney; Gralla, Erica; Wetmore, Finley; Goentzel, Jarrod; Peters, Megan (Production and Operations Management, 2021)
Meeting the United Nations Sustainable Development Goals (SDGs) will require adapting or redirecting a variety of very complex global and local human systems. It is essential that development scholars and practitioners have tools to understand the dynamics of these systems and the key drivers of their behavior, such as barriers to progress and leverage points for driving sustainable change. System dynamics tools are well suited to address this challenge, but they must first be adapted for the data-poor and fragmented environment of development work. Our key contribution is to extend the causal loop diagram (CLD) with a data layer that describes the status of and change in each variable based on available data. By testing dynamic hypotheses against the system's actual behavior, it enables analysis of a system's dynamics and behavioral drivers without simulation. The data-layered CLD was developed through a 4-year engagement with USAID/Uganda. Its contributions are illustrated through an application to agricultural financing in Uganda. Our analysis identified a lack of demand for agricultural loans as a major barrier to broadening agricultural financing, partially refuting an existing hypothesis that access to credit was the main constraint. Our work extends system dynamics theory to meet the challenges of this practice environment, enabling analysis of the complex dynamics that are crucial to achieving the SDGs.