Slipping into simplicity
By Pete Barbrook-Johnson, (UKRI Innovation Fellow and Research Fellow, University of Surrey, @bapeterj) and Alexandra Penn (Senior Research Fellow, University of Surrey, @DrAlexPenn)
We and our colleagues often talk a good game when it comes to complexity. We want to understand nuance and context, and we know things are messier than we realise. Yet, we still struggle to grapple with complex issues and can feel frustrated with our lack of progress, or others’ seemingly foggy thinking. We groan as we think we see yet another slick report, high-profile journal article, or politician, being openly simple and linear in their thinking, and proud of it too!
But in reality, we are (and we think most of our colleagues and others are) also guilty of lapses into linearity, slips into simplicity. We desperately want to feel like we are making progress, to feel as if we have some purchase on the cliff-face of complexity in front of us, and we want to feel like someone, somewhere, even if it’s not us, has some power; please God, let someone be in charge of this madness! Lapses let us do this.
Forgive us for getting all ‘high-church complexity’. If we are serious about taking on the ideas and approaches of complexity (and all those from related approaches, old and new), and applying them, usefully and lastingly, in research, public policy, business, and the third sector, and more widely in everyday understandings of common-sense, we need to quickly recognise when we are making these slips, falls, lapses and the rest, ourselves. We’re not going to suggest concrete ways to stop making them – we haven’t mastered this! But for now, naming and shaming some of them is hopefully useful.
So, here is wholly non-comprehensive list of ways we make these slips into simplicity:
Reliance on narratives and metaphors: in discussion around complexity we understandably rely on a range of narratives and metaphors to share ideas quickly. People say ‘everything is connected’, ‘history matters’, allude to ideas such as the Butterfly effect, or point to historical examples like the competition between VHS and Betamax. These are helpful, but dangerous. The issue is when these are either overly simplistic in the first place (e.g. ‘everything is connected’), are not actually functionally or structurally equivalent to the cases that we are looking at (e.g. using fractals as an analogy for organisational structure) or are relied on so much that they start to replace the idea, concept, or description of complexity itself (e.g. VHS vs Betamax becomes synonymous the concept of path dependency, making it feel like a consumer lock-in issue alone). We often want to turn our ideas into neat narratives and metaphors to help communicate with time-poor decision makers, and again this is entirely appropriate in many situations, but we must critically reflect on the simplifications we are introducing and ensure they don’t become embedded in our thinking when ease of communication is not a priority. If we have neat narratives and metaphors from one context, which we then rely on, we can’t easily transfer them (or the ideas behind them) to new settings. In short, narratives and metaphors can set like concrete, and hold us back.
Poor examples: we struggle to come up with intuitive examples of complexity in social settings. We fall back on examples from the physical and natural world (the flock of birds, the introduction of wolves in Yellowstone Park woods), which hold us back because we don’t know much about them (in our immediate experience, relative to social settings). When examples are from physical or ecological systems with no human element at its centre, they cannot capture or communicate the social complexity that arises from human activity. We need examples of social complexity to be closer to hand, to be more accessible. For our money, ‘people joining queues’, or ‘pedestrians in crowded areas’ don’t quite cut the mustard – not when we’re talking about big public policy and societal issues. We are not convinced we have the examples that carry in-common understanding,to hand yet. Candidates might be ‘herd immunity’, tipping points in public opinion, the emergence and local stability of markets and prices for goods and services, but these are still quite dense or have obvious limitations.
Misused modelling: we have a whole bunch of cool modelling methods at our fingertips. We love to use them. But, by definition, all of them force us to simplify and abstract the world. This is a perfectly reasonable thing to do when we knowingly do it with a clear purpose, and always hold that fact at the front of our mind. But we often don’t do this, we forget, or we don’t even realise, or we have an ill-defined purpose. The models we use shape everything from the variables we think about to the questions we ask, all the way up to our entire way of thinking. Models have influence on us and through us on the system they model, and it tends to be to simplify our thinking. We often find ourselves looking for, or being asked for, simple linear reasons (see narratives above) about why a model gives a certain result, a result which probably comes from a counter-intuitive and complex process. This simplification in communication is seen as good practice (‘if you can’t explain the model behaviour simply, you don’t understand it’). We should question it more.
Missing perspectives: short and sweet this one. We often forget to, or choose not to, take into account other peoples’ perspectives. We prioritise our own unconsciously and implicitly, and it makes us partially right at best, wholly wrong at worst. We also often want to ‘get our thinking straight’ before talking to stakeholders. This is almost always a bad idea. We should be getting our thinking straight with stakeholders.
Simple solutions: ‘complex problems need simple solutions’ they say. Perhaps some simple rules of thumb for thinking or managing a situation can be helpful, but the underlying interaction with or management of a system won’t be simple. It will be ongoing, indefinite, changing, iterative, human and learning, and complex in its own right. Moreover, the idea of ‘solutions’ doesn’t make much sense in the face of genuine social complexity – this is not a new idea. At best, the simple solutions we think we have are just helpful hooks to start out on that cliff face. They are not solutions in and of themselves; at worst, they are inadvertently counter-productive, or limit our capacity for ongoing interaction.
Hierarchy: we sometimes hold tightly onto old-fashioned or ideological ideas about hierarchy and governance. We either want leaders to understand complexity and shape things for us such that they or we can ‘deal’ with it, or we see self-organisation at ‘lower’ levels as a one-stop solution. This worries us. Our default governance position should actually be to find the most ‘appropriate scale(s)’ of governance for an issue depending on the scale of its drivers, effects and feedbacks. We should focus on understanding what situation we are in at each level and then structuring co-ordination between scales or levels. Each level may need its own assessment of the complexity at play. The inconvenient fact is that the world has structure at multiple scales which may not be directly visible or provide feedbacks at the right spatial or temporal scales to make good decisions possible, especially if they are carried out at the wrong scale. This is why we need polycentric governance with multiple actors. In short, as well as being sceptical of all-knowing leaders, we should also be sceptical of the idea that self-organisation at lower levels optimises for higher levels. That being said, in practice in public policy settings, we are often already operating in systems with lots of top-down coordination, and an instinctive push towards lower level action is understandable.
So, there’s our hopefully provocative and short-ish list; it is underdeveloped and highly debatable, but that’s the point!
We hope this list does not come across as preachy or dogmatic. We strongly believe using and embedding a complexity lens for us to understand the world is important, but equally that this does not mean (as some assume) we need more heavy science, fancy modelling, or impenetrable mathematics. The point is to find a way of making our understanding and use of complexity, to be both participatory and actionable without slipping into being simplistic.