Building a culture of learning at scale: learning networks for systems change. - McKenzie, Fiona (Orange Compass & Paul Ramsay Foundation, 2021)
This scoping paper explores the question ‘what would it take to build a culture of learning at scale?’. It focuses on systems-wide learning that can help to inform systems change efforts in complex contexts. To answer this question, literature was reviewed from across diverse disciplines and the realms of education, innovation systems, systems thinking and knowledge management. This inquiry was also supported by in-depth interviews with numerous specialists from the for-purpose sector and the examination of several case studies of learning across systems. The goal was to derive common patterns to inform a ‘learning for systems change’ framework. Learning for systems change is critical when working with complexity. The dynamic nature of complex adaptive systems requires an ability to continually sense and learn from the system and adapt accordingly. This is because the nature of the challenge and ‘what works’ to meet the challenge is continually shifting (Lowe and Plimmer, 2019). This requires an ongoing process of iterative inquiry that draws upon wisdom and insights from across the system. Such learning challenges traditional siloes of expertise and organisational boundaries (Clarke et al., 2019). Learning is not simply a nice to have. It is critical for greater impact and improved outcomes, particularly in mission-driven organisations and initiatives (Price et al., 2019). In this paper, a ‘learning networks’ approach is proposed, one that draws upon individual, group and systems-wide learning to build capacity and resilience for systems change in uncertain environments. This fills a gap in the literature where the focus is largely on learning within organisations. Instead, the focus here is on what is required to support learning to occur across scales and boundaries - from the individual to system-wide. A simple meta-framework for developing learning networks is proposed that includes high level guidance on the enabling conditions - the mindsets, relationships, processes and structures- that would enable learning networks to flourish.

Managing complexity (and chaos) in times of crisis. A field guide for decision makers inspired by the Cynefin framework - Snowden, Dave; Rancati, Alessandro (Publications Office of the European Union, 2021)
This field guide helps to navigate crises using the Cynefin framework as a compass. It proposes a four-stage approach through which we can: - assess the type of crisis and initiate a response; - adapt to the new pace and start building sensing networks to inform decisions; - repurpose existing structures and working methods to generate radical innovation; - transcend the crisis, formalise lessons learnt and increase resilience. The guide stresses the importance of setting and managing boundaries, building informal structures, keeping options open, distributing engagement and keeping an ongoing assessment of the evolving landscape. Action items, real life examples and demonstrations complement the references to the developing theoretical framework.

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.
_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/poms.13492

Complexity-Aware Monitoring and Evaluation - Hertz, Tilman; Brattander, Eva; Rose, Loretta (Journal of MultiDisciplinary Evaluation, 2021)
Background: Addressing today’s sustainability challenges requires adopting a systemic approach where social and ecological systems are treated as integrated social-ecological systems. Such systems are complex, and the international development sector increasingly recognises the need to account for the complexity of the systems that they seek to transform. Purpose: This paper sketches out the elements of a complexity-aware monitoring and evaluation (M&E) system for international development programmes in the area of sustainable development.  Setting: Not applicable. Data Collection and Analysis: The authors draw on existing literature on complexity and evaluation and on their own experience from working in the field of M&E. Findings: An M&E system should not be seen simply as a tool to track compliance against a pre-determined theory of change. Instead, it is most useful as a real-time approach, constantly defining and re-defining narratives for change that help push systems along trajectories of interest. Dealing with complexity involves embracing uncertainty; and this challenges established notions of accountability—something which funders and implementers must begin to redefine together. Keywords: monitoring; evaluation; complexity; social-ecological systems; international development programmes; narratives for change; theory of change
Number: 41

Applying Systems Thinking to Education: The RISE Systems Framework - Spivack, Marla (Research on Improving Systems of Education (RISE), 2021)
Many education systems in low- and middle-income countries are experiencing a learning crisis. Many efforts to address this crisis do not account for the system features of education, meaning that they fail to consider the ways that interactions and feedback loops produce outcomes. Thinking through the feedback relationships that produce the education system can be challenging. The RISE Education Systems Framework, which is sufficiently structured to give boundaries to the analysis but sufficiently flexible to be adapted to multiple scenarios, can be helpful. The RISE Framework identifies four key relationships in an education system: politics, compact, management, and voice and choice; and five features that can be used to describe these relationships: delegation, finance, information, support, and motivation. This Framework can be a useful approach for characterising the key actors and interactions in the education system, thinking through how these interactions produce systems outcomes, and identifying ways to intervene that can shift the system towards better outcomes.