“You think that because you understand “one” that you must therefore understand “two” because one and one make two. But you forget that you must also understand “and.”―
There is growing recognition that if we want to become more effective in supporting peace then we need to get better at engaging with the complexity of contexts that perpetuate violence.
Cedric de Coning’s work on adaptive peacebuilding emphasises the importance of recognising the complexity of conflict-affected areas and avoiding simplistic linear solutions to resolving violence. Instead, peace practitioners and policymakers need to become more comfortable embracing uncertainty and experimenting with their way forward, allowing solutions to emerge over time. Building on this, a study by University College London’s Institute for Innovation and Public Purpose (UCL) suggests that an effective way to embrace complexity is to adopt a system-thinking approach to post-conflict transitions. This approach emphasises three key elements:
- Think in systems;
- Systems analysis;
- Systems innovation or Systems change.
Using these key elements as a reference, this article draws from the lessons we have been learning applying a systems approach in Yemen, Myanmar, Syria and Afghanistan. We explore our experience of working with practitioners and policymakers to think in systems, different ways to undertake systems analysis that increases the chance of uptake and viability, and ways this analysis can support systems innovation and adaptive peacebuilding.
Thinking in systems
The UCL paper emphasises that thinking in systems requires understanding the underlying causal factors that drive behaviours and how they are interlinked to create causal feedback loops that sustain these behaviours. The traditional approach (i.e., Political Economy or conflict analysis) to understanding complex challenges is to break them down into the power, interests and incentives driving component parts (inter-tribal conflict, governance, war economy). This tends to lead to interventions focused on specific areas. However, these types of challenges are not isolated, they are interconnected and influenced by a web of interlinking interests, incentives and power dynamics.
Thinking in systems synthesises these multiple variables and examines how they are connected. Doing so encourages us to see the multiple factors and feedback loops that contribute to a problem. Instead of delving into singular issues, that limit the ability to grasp the broader context, thinking in systems helps identify solutions that address the holistic nature of the challenges. Instead of contributing to disjointed efforts to solve complex problems, systems thinking can encourage greater collaboration and more comprehensive multi-sectoral solutions. Instead of an emphasis on immediate outcomes, greater consideration can be placed on addressing systemic issues that will have both immediate and long-term implications.
How can we help people to think in systems?
As part of our ongoing practice at Dialectiq, our focus has been on making thinking in systems easier by reducing the burden on resources and cognitive load. Together we have been helping practitioners gather diverse perspectives in a structured way, gain valuable insights into these differing views, encourage collaboration and undertake a journey of collective problem-solving.
We discovered a range of diverse approaches to describing conflict contexts, influenced by researchers’ and analysts’ objectives. To integrate these various perspectives, we devised a methodology that combines Political Economy Analysis, Systems Thinking, Complexity Theory, and Network Theory. By combining these theories in a structured way, we develop maps that show the differing relationships between actors, institutions, incentives, causal factors and events. By continually refining our analysis, we are assisting practitioners in gaining a more profound insight into the systemic factors that underlie behaviours and the actors responsible for influencing them.
The Dialectiq methodology can be broken down into five main stages as outlined in Figure 1 below. The methodology is not linear and requires an iterative process of experimentation to test the value of the analysis and insights.
Figure 1 – Dialectiq Methodology
During the scoping and purpose phase, we gain a clear understanding of each our partners’ needs, types of data, and capacities. The type of data that the practitioners have available or were collecting, combined with each research team’s needs informs the types of visuals that we develop. Meanwhile, during the data exploration phase, we work with our partners to gain insights and identify patterns or trends in the data. The goal of data exploration is to gain a better understanding of the data and to generate hypotheses on the different push and pull factors driving network behaviour. Through exploration, we can connect this data to other data sources that can uncover these underlying incentives, identities and interests. Finally, during the process of discovery and refining the analysis, our attention shifts to providing more in depth to the analysis. This focus of attention provides a deeper understanding of the underlying dynamics and their outcomes. This can help research teams identify leverage points that provide clear pathways for policymakers and practitioners to design their interventions.
How can thinking in systems improve our understanding of conflict and fragility?
Through our efforts, we are helping practitioners deeply understand peace and transition processes in changing conflict landscapes. We have been exploring how fragmentation occurs and how society is forced to reorganise itself into smaller networks that function by providing services, resources, security and governance.
Together we have been creating visuals of different networks so that practitioners can explore the nature of their relationships (e.g., collaborate, compete, influence). To develop these visuals, we structure data into a data model is a conceptual representation of the data and its relationships used to organize and understand it. Using a data model provides a framework for understanding the relationships and patterns of behaviours driving conflict dynamics allowing us to test hypotheses on the nature of the conflict.
Figure 2 shows how we connect different actors and the different types of relationships that can exist between Local actors within a network (Green circle) and with Key Actors between networks (Blue Circle). See Figure 3 for an illustrative example.
Figure 3 – An illustrative example of a network map using the network structure in Figure 2
Mapping the conflict problem
We are also creating visual representations of a conflict problem or system that is bringing greater clarity to the complexity. These maps explain the recurrent nature of a conflict problem through causal feedback loops. Figure 4 below shows how we structure data in such a way as to examine how different causal factors reinforce or balance the conflict problem.
Figure 4 shows how we connect different causal factors and the different ways we can show how these causes are connected. See Figure 5 for an illustrative example.
Figure 5 – An illustrative systems map showing causal factors are linked
Through this ongoing practice, we are discovering how smaller systems can act as a centrifugal force creating separation from the larger state system or a centripetal force leading to new alliances forging with the larger state system. In different incidences, these forces can result in further violence or peaceful outcomes depending on the context.
We are finding that applying a system thinking lens in practice can have a profound effect on how we understand post-conflict transitions. If we can provide practitioners with the tools and methods to better understand the systemic factors underpinning violence, we can help them to think more strategically and enhance their chances of achieving more sustainable peaceful outcomes. In our next blog, we will discuss our experiences developing systems analysis, and how to improve the uptake of systems analysis and ways to make systems analysis easier and less resource-intensive.