Linear to Complex
Here are some sound pieces of advice: the more you know about a system, the better you are at predicting its behavior. If you want a large outcome, then put a large amount of effort into the process. For the best execution, plan ahead. These are all powerful strategies – but only if you are dealing with a linear system. For a complex system, this approach spells disaster. So how do we distinguish linear and complex systems and understand how they function? How we can make the paradigm shift from Linear to Complex?
THE THING ABOUT UNPREDICTABLE BEHAVIOR
If you consider a reasonably large moving object, such as a bird in flight, you can make overall predictions about its trajectory and speed. Of course, you will not know exactly how it will fly, but you can make ballpark assumptions, especially if you know something about the bird in question. In the case of a mechanical object, such as a satellite, you can make extremely accurate predictions about its trajectory.
Contrast this with a swarm of birds, a large flock of birds moving together. Perhaps you have had the opportunity to witness this remarkable exhibition. Hundreds of independent bird trajectories aggregate to the behavior of the whole, moving in surprising and seemingly random patterns. Since the birds in the swarm interact with one another, the behavior of the whole cannot be deduced from individual behavior. The same is true of swarms of fish, insects, and to some extent people. The swarm is a complex system; it is made up of interdependent agents. A single bird can be characterized as a linear system. Linear and complex systems obey different rules, and should be dealt with by using different strategies.
To get a sense of these different strategies, look no further than Brasilia. The Brazilian capital was designed in the late 1950s by a two architects and an urban planner. It was the epitome of the planned city, with different sectors for different city functions. It’s a stunningly beautiful city of white buildings in straight modernist lines. It’s also massively impractical. Crossing from one avenue to another usually requires a long detour, as the city is build to look symmetrical and avenues are separated by large lawns that cannot be crossed.
Contrast this with the Michigan State University campus. When the campus was built, the designers decided to leave out all the connecting paths between buildings, and just planted grass lawns. As people started using the campus, paths started appearing, reflecting preferred trajectories. Once these paths were sufficiently established, the university decided to pave them over. These paths are called, rather poetically, “desire paths”, as they indicate the desire of the user rather than the planner. You might notice a few around where you live – they typically involve a practical shortcut.
Linear systems, then, are ruled by independent behavior, and have predictable outcomes. An architect or city planner makes an independent design decision. Complex systems, by contrast, are made up of a large number of interdependent, adaptive agents, and are unpredictable in principle. Pedestrians walking on a university campus are interdependent, because the behavior of every person is influenced by, and in turn influences, other users of the paths. It is adaptive, because it will self-correct. When conditions change, people will start taking different routes, which will lead to different desire paths.
DISTINGUISHING LINEAR AND COMPLEX PARADIGMS
The paradigm shift from Linear to Complex is the shift from independent and predictable systems to interdependent adaptive systems. What does this shift entail? Let’s begin by getting two possible misconceptions out of the way. First, “complex” here is a technical term to denote an “interdependent adaptive system”. Complex systems, and the science of complexity which studies them, have nothing to do with how complicated or intricate something is. In everyday language, we may talk about a “complex problem” or a “complex situation”, but by this we mean that the solution is not simple or obvious. Here, we use Complex to describe a complex system.
Second, a system is either a linear system or a complex system; it cannot be both. How can you distinguish between them? According to Roland Kupers, “when interactions are not crucial, it is not a complex system. A plane is a complicated machine, but it is not complex; it behaves in a predictable and linear fashion”. The weather, on the other hand, is a complex system, because there are a lot of crucial interactions between currents, wind, temperatures, and so on. While our weather reports have become more accurate – in part because of our better grasp of complex systems -, the weather remains unpredictable in principle.
The Linear paradigm is a way of looking at the world as if it were composed of linear systems. The Linear paradigm rests on a number of core beliefs: systems behave in predictable fashion; the more data you have, the better you can predict the system; cause and effect and proportional; and planning works. These are strategies you use when you approaching the world through a Linear paradigm. For linear systems, it is also the correct approach. The trouble is that many systems are actually complex systems, and that linear strategies will be counter-productive.
The Complex paradigm is a way of understanding and acting in a world made up of complex systems.
It rests on a different set of core beliefs: systems are unpredictable in principle; more data doesn’t necessarily help you; cause and effect are not proportional; and being prepared for whatever happens is better than planning, which doesn’t work. This is a completely different way of looking at the world, with a different set of strategies.
We are seeing a paradigm shift from Linear to Complex because of the realization that many systems are complex systems, and should be treated as such. Most natural systems are complex systems, for example ecological ecosystems, the weather, swarms of birds or fish, and ant hills. Complex human systems include large cities, the stock market, and traffic. Resilience, defined as the ability to absorb a shock and learn from that experience, is a property of a complex system. In order to make our cities, global economy, or traffic more resilient, we need to approach them through the Complex paradigm.
In the Industrial age, there was an emphasis on Linear systems, and strategies such as waterfall planning were quite effective in manufacturing due to a relatively predictable supply chain. With the rise of the Information Age, and the subsequent digitization of nearly everything, all parts of the supply chain are becoming increasingly fragmented and decentralized, disrupting formerly Linear systems and increasing unpredictability as well as previously accepted notions of cause and effect. Social, political, and economic systems normally change incrementally, but this recent technological change has been exponential.
Technological change (such as Moore’s Law) will initially only affect technology, but once critical mass is reached, the disruption takes place in other unrelated systems. The twin processes of globalization and digitization are binding together systems with radically different time scales. The behavior of each system influences the other in complex feedback loops in which effects may be difficult to trace, and latent dependencies exist between seemingly random things. Therefore, both uncertainty and risk increases with scale.
Memes can “go viral” very quickly in our complex social media system, which is composed of interdependent actors which influence others and are in turn influenced by them. This is also the case with the news. In the past, totalitarian rulers could control the news by controlling the media; today, they are but one of the players in a complex adaptive system that no-one controls.
ELEMENTS OF THE SHIFT FROM LINEAR TO COMPLEX
There are three core elements of the shift from Linear to Complex. The first element is the shift from predictable to unpredictable in principle. Linear systems are predictable: the more data you have, the more accurate your prediction will be. You can launch a satellite across space to a distant planet with amazing accuracy, as it will behave according to the laws of physics. On the other hand, no amount of data will allow you to make fail-safe predictions in the stock market.
The second element is the shift from proportional effects to disproportional effects. In linear systems, small causes have small effects, and large causes have large effects – think of the volume button on your music system or the accelerator pedal in your car. This is not true in complex systems. Small causes can have very large effects. This is the so-called “butterfly effect”: the flapping of a butterfly’s wings in one continent can lead to a hurricane developing in another. The technical term for this is “sensitive dependence on initial conditions”.
This happens all the time when things go viral, for example in the fashion world or the music biz. Large effects can have small causes through multiplication and scaling up. The reverse is also true: you may have a large cause, such as a massive marketing campaign, which has no effect whatsoever.
The third element is the shift in strategy from planning to preparing. Planning works well in a linear system. When nothing unexpected happens and there is no outside interference, things go according to plan. Planning is useless for a complex system. It can even be dangerous, because it gives you the illusion of control. In the Complex paradigm, you have to let go of planning, and focus on preparation. Preparation is not the same as planning. If you are prepared, you are ready for unpredictable things to emerge. Improvisational theatre has wonderful techniques for this: as you have no idea what the other actors will do, you prepare for any eventuality.
IT'S YOUR MOVE
The key to using the Complex paradigm is realizing that it works according to rules which might at first sight seem counter-intuitive. Actually, what feels as counter-intuitive, for example that large causes can have no effect, or that extra data won’t help you predict the system any better, is our familiarity with and reliance on the Linear paradigm. We think that this is the way the world works – and this turns out to be true only for specific systems. We may believe that we understand the Complex world and already act accordingly, but our Linear habits are deeply entrenched and barely visible.
Once you have understood the approach and strategies of the Complex paradigm, think of where you can start making changes in your organization or business model. Where do you use the Linear paradigm, and are those actually linear systems? Some companies like to plan how their users should interact with their products, and typically don’t allow them to configure their own solutions. What if you started preparing rather than planning?
The greatest benefit of understanding the Complex paradigm is your ability to start intervening in other ways. You can go from avoiding uncertainty to embracing uncertainty. You can make your project, organization or system more resilient. It will be more productive, less frustrating, and – who knows? – even truly enjoyable.