When discussing complex situations, one faces a serious problem. Complexity is stubbornly complex. This means that what is radically uncertain cannot be comprehended with certainty and clarity. For different people, for instance, things are complex in different ways. And different perspectives of complexity routinely ignore and miscomprehend one another.
From its start, the idea of complexity has seemed quite a divisive issue. This is indeed very strange since it strikingly contradicts the original meaning of the word. Latin com-plexus is about the ability to bridge between opposite sides allowing for the emergence of a more integrative comprehension of radically uncertain (antagonist) issues.
It seems that the majority of prevailing approaches to complexity today nevertheless share some similar features. Probably the most striking is their determination to enforce a non-original view on complexity. Two schools of complexity dominate presently. They carefully differ in core principles but are nevertheless similar in their arrogant efforts to resolve complexity by making it compatible and manageable with the dominant discourse on systems or chaos, on order or anarchy, not as something completely different from polar setting.
For some, complexity is foremost about the chaos and essential uncertainty of things at the micro-level of observation. Complexity is like chaos but a little different since it is not located in the centre but on the edge of chaos. This is where spontaneous self-organising processes operate. The best thing one can do is to flow with the chaotic complexity. Their science of complexity is situated in the domain of mathematics, statistics, and computation. These help us to see through complexity, so that we can adapt, respond, and maintain control over it. No need to worry about complexity as long as you are equipped with models (and specialised departments) that can analyse big data in crazy dancing loops.
The others, however, understand complexity at the macro-level of consideration and so on the edge of order. Complexity is seen here as a very specific sort of system, contaminated with persistent uncertainty. Complex systems are indeed everywhere and they have proved so successful that we easily forget the complexity behind everyday normality. With systems, radical uncertainty linked to complexity can be somehow contained and managed. Such as with risk assessment plans, by precautious installation of new safety buffers, or by improving rules and updating standard procedures. This is the macroscopic perspective of complexity. It was for instance taken in the assessment of responsibility for the Fukushima nuclear disaster. The blame was eventually attributed not to the complexity of relations between nature, socio-economic interests, and human behaviour but to inappropriate operation procedures and inadequate risk assessment plans.
In both approaches, micro and macro, complexity is diverted from its original. Either by oversimplifying it or by accepting it as fate, with nothing in between.
Examples of the oversimplification approach to complexity are abundant. Today I focus on a recent blog post “Building complexity into development evaluation”, by Michael Bamberger, a senior research fellow at 3ie (The International Initiative for Impact Evaluation). Some other recent examples are here and here.
Bamberger addresses complexity by breaking it into the four components with a five-step approach (why not five components and a four-step approach?). He proposes an expert method that simplifies the explanation of complexity helping non-specialists (policymakers, managers) to deal with complexity. Experts are needed to differ between weak complexity, which must be ignored, and strong complexity, which must be simplified — with four categories and five steps. This basically demands that complexity must be either ignored (let go) or domesticated (‘simplified’) to a systemic or chaotic understanding of complex things.
The blog post advises experts, when faced with complexity, to treat their clients (decision-makers and managers) like dummies who are unable to comprehend the complex world in an original way. I think, the whole point is in fact precisely inverted. Dominant discourses on complexity are concerned with how to accommodate disorder to order or vice versa, instead of how to find the middle ground and stay between order and chaos. A complex issue contains both as a hybrid, but also neither because it is becoming something qualitatively new.
In other words, one needs to understand complexity in an original way, as mesoscopic, as the overlap between order and disorder not against one or the other (who is the dummy now?). A concept of the middle is radical — it is not about intermediation, the translation of legitimate differences, or a compromise between principal contradictions. Primarily it is a bridge between consenting opposite poles or river banks horizontally, as well as vertically in a hierarchy of relations from small to large, from stones to the bridge. In mesoscopic terms, first we find horizontal synergies which are a prerequisite for translating vertically between small and large. And vice versa, for instance, when individuals take socially responsible behaviour, self-restrained in relation to the achievement of important collective and social goals, say about vaccination or ecological behaviour.
When complexity is dealt with in the middle it becomes simple in a completely novel way. It can be nicely illustrated with Venn diagrams, organised in square matrices, manipulated with correlation procedures, and interpreted with evaluative thinking which connects between facts and values.
Simplification is good especially for dummies. They appear to me as the most interested in discovering the secrets of the original meaning of complexity as a prerequisite for more consistent governance of legitimately opposing social or business concerns in transformative conditions.
Expert approaches to complexity can nevertheless be valuable as long as they recognise themselves as being specific, instead of general description of complexity.