Systems thinking is very helpful in dealing with messy situations, wicked problems or frustrating puzzles where the overall complexity involved appears overwhelming. But what do we actually mean by complexity in this context and how can we deal with it effectively?
John Casti, a mathematical modeller and writer on complex systems, says that:
… when we speak of something being complex, what we are doing is making use of everyday language to express a feeling or impression that we dignify with the label complex.
(Casti, 1994)
He also argues that the meaning we give to the term complex is dependent on the context. Complexity is not just a matter of there being many different factors and interactions to bear in mind, of uncertainty concerning some of them, of a multitude of combinations and permutations of possible decisions and events to allow for, evaluate and select. It is not only a technical or computational matter, such as what engineers and operational researchers deal with. Complexity is also generated by the very different constructions that can be placed on those factors, decisions and events. Complexity arises from the different perspectives within which they can be interpreted and the degree of emotional involvement people have in the situation. This is so important, and in my experience, so difficult to come to terms with - especially if you have a technical or engineering background - that it is worth discussing further.
It will help to put a label on these different aspects of perceived complexity. The first aspect, which I have referred to as generating difficult computational problems, can be called ‘hard complexity’, and is illustrated by the game of chess. With up to sixteen pieces on each side at any one time and many moves that could be made by each one, the range of possibilities is enormous: a vast number of move and counter-move sequences may have to be considered and assessed. It is, unquestionably, complicated. Nevertheless, the nature of the game, the moves of the pieces, the fundamental purposes of the players – all these are unproblematic.
Figure 3 What’s the answer?
By contrast consider the situation in a detective story at the end of the penultimate chapter when the detective is about to unravel the mystery. Once again, the situation is complicated, but in a quite different way. Usually the number of possible murderers is quite limited - perhaps only half a dozen. So on the face of it, choosing among them should be a fairly manageable task. But in this case the complexity arises not from the ‘facts’, but from the variety of quite different constructions that can be put on them. Such information as the author has given you may in principle be sufficient but it is seriously incomplete, and also contains much that will prove quite irrelevant or misleading. To solve the problem you have to recognise the significance of chance remarks and relate these to alternative explanations for behaviour or events. And very little can be taken for granted: it may not even have been a murder, but a suicide designed to incriminate someone else, or a case of a corpse disguised and disfigured to look like the person who has escaped with the loot to Paraguay and so on. Each reader will, before the final unravelling, have different hunches about who did what, how and why. The description of events is ambiguous, and deliberately so, while the reader’s degree of emotional involvement can also be high. Complexity of this sort can be called ‘soft complexity’. Figure 4 illustrates the distinctions and similarities of hard and soft complexity.
Figure 4 Characteristics of hard and soft complexity.
Distinguishing between these two sorts of complexity further clarifies the difference between difficulties and messes.
But ambiguities and different interpretations that can be overlooked or ignored when working on one’s own or with close colleagues are harder to avoid when more people are involved. Other people’s input will often help one see that the problem is messier than first thought (although not every difficulty is a mess in disguise). Indeed, only the most trivial difficulties involve no soft complexity at all. But the more soft complexity there is in a situation, the messier it is likely to be. Working out what to do with a mess is no longer a matter of thinking the situation through, but of rethinking or reframing it as well. Too often general principles and techniques (e.g. for project planning, work study, etc.) assume that the elements of soft complexity either don’t exist or can easily be resolved. That is, they assume you already know what sort of situation you are dealing with. If techniques help in recognising some tractable elements in a messy problem or a promising approach to aspects of it, then they are of considerable value. But equally a personal commitment to particular techniques can tie a person’s thinking to a narrow conception of the issues. In any event, by the time one is sure what principles or techniques to apply, the mess is already resolving itself into a set of related difficulties.
Allow approximately 10 minutes for this activity.
In the light of the discussion of hard and soft complexity, review your notes on the messy situations you have faced and answer these questions.
There is no viewpoint or perspective that can appreciate the full variety of a situation (you will return to this issue in Week 5). It is from the recognition of these limitations that a range of systems approaches has been developed (which we deal with in Week 7). The notion of perceived complexity addresses one of the ways I experience the word complex. But are there other ways complexity is currently used? The short answer to this is: yes, lots.
The principal term under which complexity is addressed is complexity science which is broadly the scientific study of complex systems. This course does not cover these understandings of complexity other than to note the distinctions between complex situations (as has been done so far) and complex systems.