Beyond system mapping. Meta Causal Loop Diagrams
Beyond system mapping. Meta Causal Loop Diagrams
Causal loop diagrams (CLDs) are incredible tools for making sense of complex problems. They use two types of relationships (direct or inverse) to show how an increase or decrease in the value of one variable (a synthesised description of an action) affects other variables, creating a logical cause-effect explanation. The trick here is to formulate a qualitative description of the phenomena we observe into a quantitative form to be able to explore the dynamics of this relationship. When a chain of relationships reconnects to the first variable in the chain, it creates a feedback loop. Feedback loops can explain the system’s behaviour and this is when the map comes alive.
This is not only my main working tool, but it is also my way of looking at and understanding complexity. Over the years of using it daily, I proved to myself that there is no better way to diagnose, design strategies and intervene in complex situations; until two years ago.
Causal loop diagram limitations
As I mentioned before, the nature of the relationship between causes and effects conditions the way we describe the situation we observe. This imposes some challenging limitations.
Describing complexity as if it was a see-saw going up and down captures only one out of the multiple elements involved in a complex problem. Using just direct or inverse relationships prevents us from understanding the social network around the situation, how people relate to the problem, their incentives, and the power dynamics that emerge from these interactions.
Static CLDs make it difficult to visualise how these relationships change over time as the problem evolves. It is also hard to add additional information to clarify the variables, making them more accessible for people that were not involved in the mapping process. Finally, CLDs don’t allow finding additional connections (beliefs, political affiliation, friendship or family ties, collaboration, competitions, etc.) that condition people’s behaviours.
Additionally, when CLDs pass a determined number of elements and relationships it becomes unreadable. For the map to be useful, it needs to synthesise reality in such a way that we can articulate it and make decisions. However, sometimes this synthesis oversimplifies the problem, losing important details for our decisions.
These limitations prevent CLDs from reaching their full potential.
How to overcome these challenges?
Two years ago, I started thinking about how to overcome the rules of the CLDs and the limitations on how we interact with them. The way I found to overcome these challenges was to look at the CLDs as if they were regular networks. It allowed me to apply well-developed concepts, tools and algorithms to create, explore and analyse them.
In CLDs and social networks relationships have different meanings. In a network, it would make no sense to say that the increase in one node would increase and decrease in another node. It would break its logic. On the other hand, we struggle to apply statistical analysis of centrality or rank measurements when the nodes are not necessarily the same type of entities and we use many types of relationships between nodes. For example, calculating the in-degree centrality in a CLD will be tricky if some of the nodes are actions, other resources, other subjective perceptions, other people and so on, and the relationships between them have opposite meanings.
To overcome these challenges I looked for a digital tool that allowed me to do two critical things
connecting concepts in networks creating as many relationships between nodes as needed to capture the multidimensional nature of human interactions, and
enabling a progressive interaction with complexity as our understanding of the situation improves.
This tool was the graph database, specifically Neo4j.
Meta Causal Loop Diagrams
Meta causal loop diagrams (Meta-CLDs) are the outcome of connecting the CLDs with the social network where the problem is embedded and its broader environment. The underlying assumption is that people are at the centre of any complex problem. People, through our interactions, create most of the conflicts we suffer and it is through our collaboration that we can improve them.
Meta-CLDs combine three different dimensions:
The cause-effect structure that conditions the situation’s behaviour
How the causes affect actors (Benefit or Damage) and how they respond to these causes (Action, Resist)
How people relate to each other (Collaborate, Compete, Command, Report, Follow, Support, etc.), as well as with other shared factors (Affiliate, Belong, Part of, etc.)
There is a fourth dimension necessary for Meta CLDs to work and this is time. A regular causal loop diagram represents time using delays to show that something happens at a different time scale than the rest of the diagram. However, Meta-CLDs can store time or dates, to represent relationships or events that happen at a very specific moment in time, sequence of events, or how nodes or relationships are created or disappear over time.
Meta-CLD example
Meta-CLDs can be used to map complex situations. Over the last year, at Dialectiq.org, we have been working as part of the PeacRep program on mapping conflicts to identify entry points for developing peace. From this experience, I have developed a simplified generic example to show how Meta-CLDs work and their possibilities, not only in peacebuilding but in many other complex contexts.
Power shift in authoritarian governments
In this example, I explore a transition in power in a country where there are no solid institutions, so an authoritarian government can exert its control over the population and its resources. This political coercion prevents any political participation, which leads to an extractive economy and, over time, to its stagnation, eroding the living conditions of the population. Crossing a threshold, communities start protesting increasing the pressure for political change. This creates momentum for a shift in the power structure. The current government responds to this pressure by repressing the protests, which deteriorates even further the living condition of the population and reinforces the protests.
The instability opens an opportunity for newcomers to gain control of resources, creating an incentive for foreign actors to support new prominent figures, new business networks and new power structures to access power with the promise to remove possible competitors to exploit the resources..
The visuals show the underlying structure that drives the political transition dynamics, such as
The tendency of the government to become authoritarian in the absence of strong institutions.
The extractive economy leads to the deterioration of the living conditions of communities.
Social protest and repression.
The window of opportunity for foreign actors to engage in extractive practices.
The eventual shift in power.
Even though these dynamics can provide us with a reasonable explanation of the situation, it would be interesting to see how they affect the main actor of the conflict and how they create an imbalance in the current power structure.
Who are the actors involved and how are they affected?
One way of approaching actors is looking at how the causes benefit or damage them and, as a consequence of this, how they respond.
Following this idea, we can reveal an “incentive loop”, as shown in the visual, where the “current power network” actions “protest repression” damaging communities. As a consequence of this, the communities “pressure to change” the political situation and “support” other actors to do the same. This dynamic can lead to the escalation of violence. It also leads to the formation of alliances between different actors that are damaged by a situation or that can benefit from the destabilisation of the current power structure.
Communities see the “new business network” as an alternative source of funding and support. ”New business networks” and “new power network” benefit themselves from the control of resources. This mutual benefit enables them to work together to consolidate themselves in power. The community and the new business network have different purposes, communities fight against the deterioration of their living conditions and repression, while the new business network wants to gain control over the resources. However, they share the same means to achieve their purposes, removing the current power structure. These common means to achieve different purposes creates the space for a temporary alliance.
Competition between the current and new power networks
The last dimension we can analyse is the competition power structures and their dynamics.
In this example, I used the relation of “supports” to show the flow of people, resources, money or military aid from one actor to another. As I showed before, every actor is affected by one or more causes that create incentives for them to behave in the way they do. One type of behaviour is creating formal and informal networks through which they can exert power.
Additionally, they can share more than incentives, they also can share ideology, values, religious beliefs or grievances. These subjective factors can create strong bonds between people contributing to consolidating the networks of powerful actors.
Why is this valuable?
Unfortunately, there are many situations that erode or threaten our living conditions, from climate change, pollution, the degradation of our cities or open violent conflicts for the control of strategic resources. In these situations, we need to act, even though we do not always have the resources, access to the right actors, or the legitimacy to do it. In this situation, we can create the conditions for the people that are involved in the problem and find a way to improve it.
This way of mapping complex situations can help identify the main dynamics and obstacles that prevent situations from improving, reveal the incentives that emerge from these dynamics, and show how they shape and condition how the network of actors organises themselves to exert power.
As the visuals show, these Meta-CLDs are interactive, enabling us to collaborate with other people that work on the same problem and have a different perspective, different data or different knowledge to integrate them into a cohesive whole. Meta-CLDs allow us to explore specific areas of interest without suffering data overload.
In my perspective, Meta-CLDs reduce the complexity of the standard maps by being able to query and traverse them. This querying capacity makes it easy to explore it as our understanding of the situation improves and makes it easier to make decisions based on our discoveries.
Finally, these maps help to create a common ground for people, teams and organisations that have to intervene in a complex situation. It brings together their knowledge and helps challenge underlying assumptions and find entry points to engage with actors to move things forward.
