What is it about the challenge of climate change that makes it so difficult to solve?
Clearly, it’s a complicated problem involving many interacting components. These interacting parts include the Earth system (and its billions of components), people (you and me), states and countries; organisations and institutions; unknowns; tradeoffs; winners and losers. We’ve spent decades of effort addressing this issue – including billions of dollars on research – and yet the problem of mounting levels of carbon emissions and accelerating environmental decline only seems to get worse. (Have you seen what’s happening in the northern hemisphere at the moment? And it’s only spring!)
Clearly, climate change is a big and complicated problem but it seems to me, having watched us deal with this challenge (and fail) over many years, what we’re not acknowledging is that it’s also a complex problem, and we’re not dealing with this complexity very well.
‘Complicated’ and ‘complex’ are words often used interchangeably but they are fundamentally different ideas. Do you know the difference? I’ll confess that for most of my life I didn’t.
So, what is complexity?
Complex systems scientists have been attempting to pin down what complexity is for decades. To me, most of their definitions are highly technical and only understandable by other complex systems scientists.
Here’s one commonly used definition set out by the famous evolutionary biologist Simon Levin in 1998 that encapsulates many of the ideas floating around complexity. It’s relatively short and sets out three criteria for defining a complex adaptive system. Complex adaptive systems have:
-components that are independent and interacting;
-there is some selection process at work on those components (and on the results of local interactions); and
-variation and novelty are constantly being added to the system (through components changing over time or new ones coming in).
Sounds straightforward but what does it mean and why is it important? Here’s how I attempted explain it in the book Resilience Thinking*.
Cogworld vs Bugworld
Consider these two situations: Cogworld and Bugworld.
Everything in Cogworld is made of interconnected cogs; big cogs are driven by smaller cogs that are in turn driven by tiny cogs. The size and behavior of the cogs doesn’t change over time, and if you were to change the speed of the cogs of any size there is a proportionate change in speed of other connected cogs.
Because this system consists of many connected parts some would describe it as being complicated. Indeed it is, but because the components never change and the manner in which the system responds to the external environment is linear and predictable, it is not complex. Really, it is just a more complicated version of a simple system, like a bicycle with multiple gears.
Bugworld is quite different. It’s populated by lots of bugs. The bugs interact with each other and the overall performance of Bugworld depends on these interactions. But some sub-groups of bugs are only loosely connected to other sub-groups of bugs. Bugs can make and break connections with other bugs, and unlike the cogs in Cogworld, the bugs reproduce and each generation of bugs come with subtle variations in size or differences in behavior. Because there is lots of variation, different bugs or subgroups of bugs respond in different ways as conditions change. As the world changes some of the subgroups of bugs ‘perform’ better than other subgroups, and the whole system is modified over time. This system is self-organizing.
Unlike Cogworld, Bugworld is not a simple system but a complex adaptive system in which it’s impossible to predict the emergent behavior of the system by understanding separately its component subgroups. It meets the three criteria outlined by Levin: it has components that are independent and interacting; there is some selection process at work on those components; and variation and novelty are constantly being added to the system.
Complicated vs Complex
In Cogworld there is a direct effect of a change in one cog, but it doesn’t lead to secondary feedbacks. The cogs that make up Cogworld interact but they are not independent, and the system can’t adapt to a changing world. Cogworld might function very ‘efficiently’ over one or even a range of ‘settings’ but it can only respond to change in one way – that is working all together. If the external conditions change so that Cogworld no longer works very well – the relative speeds of the big and little cogs don’t suit its new environment – there’s nothing Cogworld can do.
In Bugworld the system adapts as the world changes. There are secondary feedbacks – secondary effects of an initial direct change. The bugs of Bugworld are independent of each other though they do interact (strongly – though not all bugs interact with all other bugs).
In our Bugworld, if we attempted to manage a few of the subgroups – eg, hold them in some constant state to ‘optimise’ their performance – we need to be mindful that this will cause the surrounding subgroups to adapt around this intervention, possibly changing the performance of the whole system.
Ecosystems, economies, organisms and even our brains are all complex adaptive systems. We often manage parts of them as if they were simple systems (as if they were component cogs from Cogworld) when in fact the greater system will change in response to our management, often producing a raft of secondary feedback effects that sometimes bring with them unwelcome surprises.
The real world is a complex adaptive system. It is more like Bugworld than Cogworld and yet it seems most of our management, policy and leadership is based on a Cogworld metaphor.
The consequences of complexity
Complex adaptative systems are self-organizing systems with emergent properties. No-one is in control and there is no optimal sustainable state that it can be held in. These are just two of the consequences that fall out when you begin to appreciate what complexity is all about, and they are pretty important consequences if you reflect on it.
Our political leaders will tell you they are in control, and that they have a plan, a simple solution that solves the problem of climate change without anyone having to change the way they do things. This is the message that Australians have been hearing for the past decade from our (recently defeated) conservative government. But we grew skeptical of these claims as we saw our coral reefs bleach and our forest biomes burn.
Why is climate change so difficult to solve? Yes, it’s complicated with many interacting components. However, more importantly, it’s complex and complexity is something humans don’t deal with well (let alone understand).
As one piece of evidence on this, consider how we think about thinking. What’s the image that immediately comes to your mind? For most people it’s a set of mechanistic cogs encased in a head (like in our banner image this week). If you thought my ‘Cogworld’ was fanciful, how many times have you seen this representation of human thinking as mechanistic clockwork without questioning it. Because what you’re seeing is a representation of a complex system (you thinking) as a non-complex simple system (a set of cogs). The ‘cogmind’ is a fundamentally disabling metaphor.
And if you scale this up to the systems around us, how many times have you accepted that someone is in control, and that the answer is in just making the world a bit more efficient, a bit more optimal? How is that going for us at the moment?
If, however, we are living in a complex world, then maybe we should stop looking for the illusory optimal solution and start dealing the complexity in which we are all embedded. How is that done?
One set of ideas I have found helpful lies in resilience thinking. Rather than prioritising efficiency, command-and-control, reductionism and optimisation, resilience thinking encourages reflection, humility and co-operation, aspects on which I’ll expand in my next blog on complexity.
*Two decades ago I was asked by a group called the Resilience Alliance to write a book on resilience science. That book, co-authored with Brian Walker, one of the world’s leading authorities on resilience science, became the text Resilience Thinking. As I learnt about resilience science I discovered that it was all about dealing with complexity, an insight that transformed the way I understood the world.
Banner image: If you thought my ‘Cogworld’ was fanciful, how many times have you seen this representation of human thinking as mechanistic clockwork without questioning it. (Image by Pete Linforth from Pixabay)