PSM is the process of building, analysing, and using PSM maps. The maps are causal models of a system, represented by a network of factors and their causal relations. They are almost always annotated and layered with information about the factors and their connections. Technically speaking, they are directed cyclic graphs, that is, the connections between factors have arrows and there can be feedback loops in the network. The maps are built by stakeholders, typically through a series of workshops and meetings, and the participatory nature of their development is paramount. So too is the approach to analysis, which uses information from stakeholders, the principles of network analysis, and looks at the ‘flow’ and chains of causal relationships (which we often refer to as ‘causal flow’) to create submaps focused on exploring specific questions or purposes, again in a highly participatory and iterative manner.
Let’s look at an example to get a feel for the basics of what makes a map. Figure 5.1 shows both a full map, which is too large to read on the printed page, and a zoomed-in subsection, so that we can see the detail. Nodes can be from any relevant domain; they need not be explicitly quantifiable or have data underpinning them, but should be expressed as variables, that is, things in the system that can increase or decrease. The nodes in the map are called ‘factors’, and there are often special types of factors such as outcomes or functions of the system we care about, or interventions we control. In Fig. 5.1, there are intervention-type factors coloured in blue and function-type factors in green.
The connections in the map represent causal relationships. These can either be positive causal connections (i.e. if A increases, or decreases, B changes in the same direction), negative causal connections (i.e. if A increases, or decreases, B changes in the opposite direction), or uncertain or complex connections (i.e. if causal relationships depend on other factors or contexts, or if the relationship is strongly nonlinear).
In PSM maps we are normally aiming to build reasonably large networks with at least 20 nodes, often more like 50 or 100. The number of connections is then usually several times larger, running into several hundred on some occasions. Because the analysis approach is centred on creating submaps, and thus we are not too worried about having large maps, the only hard constraint on the size of PSM map is time. Building large maps may require the process to be designed to have parallel streams creating different maps which are then brought together.
The maps are intended to be ‘owned’ by the stakeholders who create them, rather than researchers. They should capture all the complexity important to stakeholders and should use annotations and labels to represent any different beliefs. The final layout of a map can take many forms; sometimes it can recreate the layout that emerged in a workshop, or be rearranged thematically, or a network visualisation algorithm can be used to highlight patterns in the network structure.
Once a map is built (though they are often never really ‘finished’, we can always add more), we can analyse it. At the core of the approach to the analysis is the idea of creating submaps, that is, subsections of the full map, which we can use to focus in on particular questions or issues. The submaps are intended to allow us to get a handle on the map; they offer us a way in, to what can be an overwhelmingly large diagram. The question now becomes, how do we pick where to extract a submap and what do we include and exclude from it?
The starting point for a submap can be defined either by ‘stakeholder-suggested’ factors, that is, what factor(s) stakeholders have told us are important, that represent their suggested or current interventions, or that they think are vulnerable to change. Or we can start with ‘system-suggested’ factors, factors that different types of network analysis (e.g. centrality measures) tell us might have interesting properties in the network. For example, we might have factor with many connections, or a factor which bridges different parts of the map.
Once we have a starting point, the analysis uses one (or a combination) of the following ‘rules’ to generate a submap: one, two, or three steps ‘upstream’ or ‘downstream’ (i.e. following the arrow directions) of the starting factor; ego networks of the starting factor (i.e. any nodes and edges connected to it and the edges between them); or paths between multiple factors of interest (i.e. following the arrows from one factor to the next until we reach the other factor of interest). Figure 5.2 shows how these are defined visually. These rules for generating submaps can themselves also be combined using unions or intersections (i.e. showing multiple submaps together or showing the nodes and edges that are in multiple submaps). We might do this if we want to look both up and downstream from a node of interest, or if we want to see where the ego networks of different nodes overlap. These various approaches to analysis are summarised in Table 5.1.
As the example above and description of analysis shows, the subjective information we collect on factors (i.e. what is important to stakeholders, what is vulnerable, or controllable) is incorporated into analysis. When combined with network analysis this provides different insights. For example, an influential (high out-degree) factor, which impacts many important functions, is obviously significant. However, if it is vulnerable to change or controlled by an external actor, it may be a vulnerability. Whereas if it is controllable, it may be an opportunity to make change, a so-called system lever. Different types of information can be collected depending on what is relevant to the system and stakeholders. Analysis is also often combined sequentially, with one submap generating questions that lead to the creation of another. In practice, the process of generating submaps is a creative, iterative, and exploratory exercise, ideally done with stakeholders. It can be modified and recombined in numerous ways to address the questions that matter to participants.
There is a reasonable amount of variety in how PSM is used, but this normally stems from the needs and purpose of any given project, rather than different perspectives on how to use the method. Examples of variety include (i) different types of information collected about nodes and edges, such as edge weights, different classes of nodes, or more detail about complex causal relationships; (ii) differences in how a PSM process is designed, with almost infinite options of how to organise sequences of workshops, meetings, and interviews; or (iii) differences in how a full map is connected to other models or forms of knowledge (e.g. using a left-to-right inputs-to-outputs-type layout to make it resemble a Theory of Change map). In terms of terminology, there is not too much variation. PSM maps are not known by another name, but the phrase ‘Participatory Systems Mapping’ is quite generic, so we have seen it used in a more high-level way to refer to different methods such as Causal Loop Diagrams or bespoke system mapping efforts that emphasise participation.