 In the previous module, we touched upon the idea of emergence, which is for many the central idea within systems theory. Our whole conception of organization and the profession of management is built around the idea of a hierarchy within organizations through which control is exercised in a downward fashion in order to coordinate the individual's actions towards some desired outcome. This approach may work well within relatively stable environments. After all, this form of management has created the industrial economies we live in today. But unfortunately, it is not designed for more complex environments, so let's firstly briefly outline some of the limitations to our standard approach to management. Firstly, reductionism focuses on the static components of the organization and the metrics we apply to evaluating them. In so doing, it systematically diminishes the space around and between them that is not measured. Thus, the system can become reduced to a simple set of metrics that are depleting some resource that may be more difficult to quantify, but is equally required to maintain the system in the long term. A good example of this is the failure of our metrics for economic growth to incorporate the natural resources they depend upon. A net result of this is a short-term profit at a long-term expense, making the system unsustainable. Secondly, inherent in the command and control paradigm is the idea that a person or few people in charge give the solution that is the only solution to other people who are in charge of implementing it. Thus, the organization becomes heavily dependent upon the limited cognitive and information processing capabilities of a few individuals at the top of the hierarchy. This also reduces the capacity for the majority of the organization's members to take initiative, act autonomously and respond to local information. Lastly, the reductionist model to organization is built on the axiom of a relatively static system in a relatively static environment. The primary focus of this form of organization is to remove surprise, to dampen down change and keep an organization moving stably through time according to the prior intentions of its members. All of these features mean that these systems are invariably resistant to change, innovation and evolution, and thus inept at dealing with dynamic and volatile environments. The standard approach is a linear model. It gives us exactly what we expect. By constraining the members, we reduce their autonomous engagement. The result is inevitably that we get business as usual. We're very unlikely to get exceptional results because the members are not fully engaged. Because of this, this model is well suited to normal circumstances but is not designed for dealing with exceptional circumstances. Emergence is the process whereby new phenomena are created as we combine things together. Our global economy is a good example of emergence. Through all of the systems of organization, we can collectively produce things like airplanes and laptop computers that none of us could produce if we all acted independently. These things are emergent phenomena of the way the parts are arranged. And this is part of Adam Smith's insight that even though all the members of the society may be acting in their own self-interest, we can get globally beneficial outcomes. We can also call this unintended consequences. For example, an arms race might be an example of an emergent unintended consequence. Neither actor in the dynamic may have wanted to end up in the arms race, but it was the way that their individual actions interacted that created that overall eventuality. As we'll be discussing, some of the most difficult and intractable problems in our world are unintended emergent consequences, such as economic inequality. Nobody really wants to live in a society with very high inequality, but at the same time, we as individuals still go on pursuing activities that lead to this emergent macro-scale phenomenon. We can see from this that emergent phenomena are not always positive results. It depends on the type of interaction between the elements in the organization, and we call these types of interactions synergies. A synergy is the interaction between two or more things that combine to create an effect that is different in some way from the two combined effects acting in isolation. Synergies may be both positive and negative, and negative synergy is a form of interference or what we might call a negative externality, where two things are working in a counteractive fashion, thus making the combined outcome less than the sum of its parts. Noise pollution might be an example of this. Two people talking at the same time doesn't make the conversation twice as good as they are interacting in a destructive fashion. We get the emergence of an effective organization through positive synergies, which involve people differentiating their activities while also coordinating them within some combined process. And this is the essence of a functional complex organization. They manage to differentiate and coordinate many parts, and out of that we get powerful emergence that makes the whole truly different from its parts, like a swarm of bees or an ant colony, both of which are vastly more sophisticated than the simple components that make them up. So from the complexity perspective, management of an organization is really about creating a context within which we can get these positive synergies to happen, and we do that by creating an attractor towards behavior that has positive externalities. For example, if we think about the failures of our healthcare systems, now no one wants to get obese, and no country wants to spend 10% of their GDP on healthcare, this is in many ways an emergent phenomena of micro level negative externalities, of people eating unhealthy food, lack of exercise, etc. Now if we wanted to try and change this outcome, we don't go into the top management and declare some new regulation. Instead, we look at the context within which the members of the organization are acting, and how, because of the local incentives they're under, we get this emergent dysfunctional global outcome. We ask, how can we make choosing the salad an easier option than choosing the Big Mac and fries? Or how do we create more green ways that might incentivize people to walk instead of taking their cars? These are examples of attractors that are specifically designed to create a context within which choosing an action that has a positive externality is simply easier and more attractive than choosing one that has a negative externality. And in this way we get synergies and the emergence of the desired outcome on the macro level. Whereas simple systems are nothing more than the sum of their parts. All complex forms of organization have two or more qualitatively different levels of organization to them. The macro level is qualitatively different from the micro level, and with complexity management, we don't try to directly affect the macro level. We try to alter it by affecting the local context within which the actors in the system are making their choices. And this helps to illustrate another important idea within complexity management, that of obliquity. Obliquity means not explicit or directly to the desired point. For example, in trying to land a rocket on the moon, one would direct it slightly off from the most direct line and let the gravity of the moon catch it and bring it in to the desired final point. Because complex systems are non-linear, meaning there is no direct cause and effect, the outcomes to the system are an emergent phenomena, we cannot directly affect them, but instead we have to aim around the phenomena that we wish to affect. We have to instead focus on the components interactions that are giving rise to this emerging outcome. That is to say we have to take an oblique approach to solving the problem. Mark Anderson, a previous manager at Trader Joe's, illustrates this phenomena when he writes quote, one of the key things I took away from my experience as store manager at Trader Joe's was just how much the bottom line of making money was enhanced when you focus on treating employees and customers well. At the end of the day Trader Joe's makes a lot of money, but they do so not by focusing on making as much money as they can, rather by seeking to provide the best service and products possible. He goes on to write, although I haven't termed it obliquity until now, I've advocated a similar focus for public schools. Rather than focus on the bottom line of student test scores, we need to focus on the content taught and the learning environment of the school and by focusing on these things test scores will naturally be enhanced. We can see clearly the idea of emergence here. Instead of focusing on the direct outcome, he's talking about using an oblique method of creating the context for these desired outcomes to emerge. Efforts to impose linear thinking on complex situations have often led to the opposite of what was intended, where explicit articulation of a goal will result in the complex environment pushing back in the opposite direction. As a result the principle of obliquity becomes relevant. In such situations oblique goals will often be more effective, for example the goal of delighting customers may make more money than an explicit direct goal of simply trying to make money. Another classical example of this is happiness. We all wish to have some happiness and quality of life, but if we focus too much directly on this outcome, it often in the long run leads to the opposite result. Quality of life is again a complex emergent phenomena, it is not something that can be directly achieved. In this video we've been talking about the idea of emergence, a central topic within systems theory. How, through the synergistic interaction of the individuals within the organization, we get the emergence of global behavior and structure that is qualitatively different from that of the micro level. We talk about positive and negative synergies that can give rise to novel, desirable outcomes or unintended consequences. We discussed how we do not try to directly control and affect the macro level of the system, but instead use an oblique approach, look at the nonlinear local interactions that are creating that behavior and try to alter the context within which those choices are made.