Consider a system’s approach to identifying the vulnerable, strong and influential parts of a system. An institution can be thought of as a system. It might be explicitly organised with rules or it might be self-organising. It has an effect: internal and external to the institution/ system. Actions are performed within, by, and on, a system. It is composed of nodes roughly thought of as objects or positions, and relations between them. The characteristic of a system in the sense of systems science is that:

  1. 1.

    The relations matter as much as, or more than, the nodes or objects,

  2. 2.

    There are feed-back loops, internal re-enforcements or confirmations, or external feedback loops which are consequences felt outside the system, and that have repercussions back on the system. Loops act on the system qua system. The loops can spiral in any direction: outward from the system, influencing the context of the system. Loops can pull the system in on itself, causing strengthening or collapse.

  3. 3.

    Systems have emergent properties, (the whole is more than the sum of the parts).

    There are many definitions of what an emergent property is, but here it is enough to say that a property is emergent just in case it cannot be detected or predicted on the basis of the parts of the system. For example, the pleasing effects of a piece of music is not detectable if we list the notes and the instruments used. The colour (visible to the human eye) of, for example, a protein, cannot be determined by looking at individual proteins. Proteins are the size of between five and ten nano-metres. The wavelengths of light are 380–750 nanometers. Colour is an emergent property of proteins only detectable at a certain scale.

  4. 4.

    Systems are dynamic.

    Systems change internally and change as a result of influences from outside.

To make a systems analysis, we identify nodes in a system and the relations between them. Nodes might be roles, particular people, sub-organisations, projects, items, cells, places, traditions and so on. A systems scientist tries to take a neutral role,Footnote 1 and maps the system using a diagram. He, or she, invites members of the system to suggest the nodes and the relations. See Fig. 6.1.

Fig. 6.1
figure 1

An example systems diagram. (Source: Shmelev (2012), Shmelev & Shmeleva (2009))

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This is one simple sort of diagram. There are other much more complicated ones. This is enough to show the core idea – to identify nodes relations and directions. Feedback loops are represented by sets of arrows that cycle back to a node. We shall add a few complications here. The diagram can be expanded as more people participate in identifying nodes and relations. Each new participant notices different nodes and relations, or re-enforces existing nodes and relations. Eventually we get a sort-of convergence or stability in the diagram of nodes and relations since we start to make clusters. Of course, the stability is in the sense of stable at a time, since outside influences or internal changes will have an effect. Systems are dynamic.

Now we ask the question about the purpose of the diagram. There is always a normative element, a reason why we composed the diagram or thought it important to understand the system. In practice, if we study a failing system, we do so in order to put it right. We might also want to destroy or undermine a system. In both cases we look for important weaknesses – nodes or relations whose vulnerability threaten to collapse the system or change it significantly. To preserve the system, we re-enforce the vulnerable nodes or relations. To destroy the system, we attack the vulnerable nodes or relations.

To deepen our analysis, let us make a three-way distinction. A system under stress, or attack, can be resilient, adaptive or change significantly. If it is resilient to that stress, then, after not too much time, it bounces back to “normal”. If it adapts relative to the stress, it changes, but not in some “essential” features. If it changes significantly, it loses important or essential features that make it unrecognisable as that (its previous) system. Which of the three scenarios occurs depends entirely on our definitions of “normal” and “essential”. So, we can think of a system as having a normal functioning state with an inner core of essential relations. We accord these more weight. See Fig. 6.2.

Fig. 6.2
figure 2

A “weighted” systems diagram. (Adapted from Shmelev & Shmeleva (2009))

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Now view the idea of system, system resilience, adaptation or significant change in terms of the compass. We might have a wish spot for the system. The compass gives us a qualitative direction. We can identify the whole system or parts of the system or relations between parts as sharing and re-enforcing the qualitative direction, or moving the system away from the qualitative direction. Some parts of the system might deliberately be designed to steer away in order to enable the other parts to pull the system in the desired direction. The qualitative direction is a quality: harmony, discipline, excitement or a combination of qualities. If we are in favour of the present direction of an institution or system, then we seek to confirm and re-enforce the essential nodes or relations, especially the ones that are the most vulnerable. In terms of our table of data, the systems diagram will further help us to decide which data to enforce and which data to ignore. But now we have added the qualitative normative dimension explicitly.

The problem is that the table of data for the compass and the systems-identified vulnerable and important nodes and relations do not easily match.

A method for merging systems science diagrams, such as the one above, with the compass is to construct compasses for the systems-identified vulnerable and important nodes and relations. See Fig. 6.3. The compass table for the node or relation will guide us as to what aspect of the node or relation to enforce or attack – in order not to merely re-enforce or merely change (without direction) but to bring about the re-enforcement or change we want. This is the difference between blind re-enforcement or destruction and guided or strategic re-enforcement or destruction towards a quality.

Fig. 6.3
figure 3

A systems diagram with compasses at important nodes. (Adapted from Shmelev & Shmeleva (2009))

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To make such a diagram it will be necessary to have data about each node. This is also something rarely used in systems science, a table of quantitative data. We can use the table plus the compasses to efficiently target parts of systems, without forgetting to see the whole, both in terms of the wish spot, and in terms of making a generic compass for the whole system. There is no need to limit our data to data about nodes or relations between nodes. There might be data about clusters, and they might show up as emergent properties.

To summarise, we can add compasses to nodes or relations of a systems diagram in order to strategically address the system. In general, it is the vulnerable or strong nodes or relations that count. The others are less significant. What the compass construction adds is a sense of direction for change in a system. We choose that direction according to our ideological orientation, that of the institution or that of outsiders who want to change the system. To show the ideological orientation, we would accompany such a diagram by two others. A compass for the entire system and a wish compass. We then target the nodes or relations that point in the right direction or the wrong direction in order to orient the entire system towards the wish spot.

This was a simple way of merging systems diagrams with the compass. It gives us a way of thinking about the system and parts of the system qualitatively and quantitatively, of measuring the contribution of the parts towards the final arrow, while allowing for emergent properties that defy a reductionist analysis in terms of the sum of the parts.