Complexity science and complex adaptive systems have been studied in the hard sciences longer than in the social sciences or within the context of leadership and organizations, but people are catching on to the value they have to offer in the world of organizations. In Simply Complexity, Neil Johnson does a great job of introducing complexity theory, and I like the simple examples he uses early in the book. First, Johnson says, “At the heart of most real-world examples of Complexity, is the situation in which a collection of objects are competing for some kind of limited resource – for example, food, space, energy, power, or wealth.” While this might trigger memories of your college economics courses, take a step back, and you’ll realize business is nothing, if not a competition for limited resources. These limited resources are both within and outside your organization. Inside, especially in the busted economy of the past few years, organizations tend not to have enough people to get the work done. Outside, organizations are competing with others for customers and dollars.
To go a step further, though, complex adaptive systems can be thought of as multiple networks of agents that are connected and interact in various ways and are capable of learning and adapting. The study of complexity itself can be thought of as the study of the emergent phenomena that arise out of those systems. One of the key elements of complexity is that the system does not require a “controlling hand” in order for phenomena to emerge from the system. The actions of the agents themselves will produce outcomes, and in fact, many of the outcomes the system produces couldn’t be created by an outside coordinator, even if someone were to try to replicate the outcome. Johnson uses a simple example of a traffic jam to illustrate this principle of emergence.
Think of a daily commute. The agents are the people in the vehicles, and they all have knowledge about the traffic situation. That knowledge takes the form of memory, based on driving a particular route at a particular time of day in the past. There may also be additional information available to the agents, such as the road conditions reported on the news. Each of these agents needs to choose a path to drive to his or her destination. They use the information available to them to make their best guess at what route will be most efficient. In reality, they won’t know if they chose the best route until they see the actual traffic conditions on their way to wherever they are going. Imagine that at least one major route has a traffic jam.
Johnson goes on to say, “Just think about the level of coordination and communication which some central controller would actually require in order to be able to recreate a particular traffic jam. In other words, imagine the number of cell-phone calls he would have to make to ensure that all the drivers were on the same road at the same time, and in one particular pattern. It simply couldn’t be done in a reliable way.”
One of the things that makes complexity complex(as opposed to complicated – a car engine is complicated, but not complex, because it can be explained by its component parts, and its function is predictable) is the unpredictable nature of the emergent phenomena. You can easily argue that certain traffic jams are predictable, and that’s true, but look at it another way. Johnson explains, “… the precise nature of the crowd-like phenomena which emerge will depend on how the individual objects interact and how interconnected they are. It is extremely difficult, if not impossible, to deduce the nature of these emergent phenomena based solely on the properties of an individual object.” If you were to try to predict a particular traffic jam based on examining only one driver and the information available to him or her, you’d be hard pressed to do so.
I’ll close with Johnson’s well-written and more complete example:
“It is 6 p.m. You are leaving work – and the only thing on your mind is to get home quickly. But which route should you take? It turns out you have a choice. But so does everyone else. And this is the point: the best route is the one which is the least crowded – but it is the collective decisions of everyone else which determine which of the possible routes this turns out to be. In effect you are not deciding between routes home – instead you are trying to out-guess everyone else. In other words, you are trying to out-guess the crowd in the competition for space on the road. Of course, everyone else is trying to do the same.”
While Johnson’s book is not particularly written for business, it is full of examples of complex adaptive systems, and does a great job at making the principles of complexity understandable. I highly recommend the book. It gives a strong foundation of understanding which will allow you to take what complexity has to offer and visualize how your own organization is a complex adaptive system (in fact, it probably has multiple complex adaptive systems contained within it).