5 Complexity
5.1 Defining complexity
A critical characteristics of a complex system is not the difficulty of understanding it but the difficulty of predicting how it evolves. A good starting point to define complexity is looking at how it differs from complication. A mechanical clock is an example of a complicated piece of equipment. Building one requires excellent skills, a lot of time and very focused attention. Its behaviour, on the other hand, is very predictable. Relying on its predictability is actually the whole point of having a high-precision watch.
Quite differently, a dish of spaghetti is straightforward to describe and prepare: anyone can do it. Predicting the position of each spaghetto in the bowl, however, is no easy task. It would require a powerful computer running and advanced simulation program and exact knowledge of the position of each spaghetto before you toss them[^3].
In other words, complexity is not related to the difficulty to build or describe a system: an elementary system can display an extraordinarily unpredictable and complex behaviour. As shown in the figure, consider a pendulum, constituted by a rod free to rotate at one tip. The pendulum behaviour is fully described by the angle of the rod. The free end is constrained to be on the circumference defined by the length of the rod. For small oscillations, a simple formula available in an introductory textbook to classical mechanics describes the movement in terms of the mass and size of the rod and the gravitational pull at its location. For large oscillations, it’s a bit more complicated, but classical mechanics can sort it out quite simply. There is, however, always a difference between a theoretical model and reality. Imagine that you build such a pendulum and want to predict its position given an initial starting point. You just need to use the classical formula and plug in your length, weight, and gravitational pull values. Of course, you are bound to make errors in the measurements, resulting in errors in the calculated position of the pendulum versus the actual one. Reality will, however, not be way off your calculation. A small mistake in the measurement will result in a small error in the calculated position. Suppose your estimate of the initial starting point is a bit off. In that case, the actual amplitude and period of the pendulum oscillation will be somehow different than calculated but still relatively close.
Now let’s make just a little, simple addition to the set-up and add a second pendulum attached to the first. The double pendulum’s behaves in an infinitely more complex way than the regular one. The tip of the pendulum can be anywhere within the circle, not just on the circumference, and its actual position can vary significantly for minor variations of an initial starting point. Predicting the motion of a double pendulum is exceptionally challenging. Minor errors in the measurement of the length or the weight mean the pendulum may at a given time suddenly move to a position that is opposite to the one predicted.
The double pendulum is actually a textbook example in an introduction to chaos theory. It is a good, if simplistic, illustration of the challenges of managing complexity. Managing in a complex environment means that no matter how precise our assumptions are, the behaviour of the system we are interacting with can be astonishing. This type of chaotic behaviour gives rise to what is often called the butterfly effect: the flap of a butterfly’s wings in the Amazon can influence the weather in China. One possible implication for organizations is that managerial focus may have to move away from planning, command and control. It becomes crucial to sense and react quickly to emerging conditions and develop resilience to unexpected and unpredictable situations.
Currently, the worldview that we adopt to understand the surrounding environment, whether in economics or management, is heavily inspired by a classical explanation of the world. A deterministic, mechanical world where an action corresponds to a simple opposite reaction that has its roots in the works of Isaac Newton. This approach has proved highly effective for highly predictable systems, like the simple pendulum. Still, it tends to fail in real life when one adds just a single layer of complexity. The theory is still correct. It becomes impossible, in real life, to obtain the precise information that allows us to apply it. To manage in a complex world, we need a new worldview, one that considers the possibility that unexpected events emerge from the dynamic interaction of the different components of the system. We will see that modern physics can be the inspiration of this worldview like Newtonian Mechanics was the inspiration of the mechanistic worldview that is still predominant.
In the case of the double pendulum, complexity arises from the simple modification of adding another pendulum to the initial pendulum. In the rest of this book, we will explore the sources of complexity and start to design a worldview that can help us manage the complexity.
5.2 Networks of complex heterogeneous actors
Explain ANT theory
5.3 Quantum theory and decision making
5.x Thinking in systems
System Thinking
5.4 Understanding the world outside
I often use the analogy of sailing when explaining strategic thinking. I usually explain that to define a strategy, you need to know an objective, which is like having a destination, so that you can draw a route, which will be the equivalent of your strategy. But the route will also depend on the weather forecast, which is the equivalent of the economic environment, and ,if you are in a regatta, of the potential choices and resources of your competitors, which is the equivalent to the competitive context in which the organization has to operate. And of course, your choices will depend on the quality of your boat and the capabilities of your crew. The weather may offer you a fast route to destination, but if you know that your boat cannot sustain the physical stress it would imply or your crew does not have the experience to confront the demanding conditions, it may be wiser to choose a slower, but safer one. In a stable world, though years of experience, m decision makers develop a sense of the competition and the economic environment that allows them to make sound decisions. If needed, market or social research can be commissioned to expert consultants to address specific questions : it’s pricey, but the additional intelligence acquired may be well worth the expense.
Unfortunately, in the current context, those mental maps of the environmnent that allow for sound decision become rapidel obsolete. We need a new,more flexible way to chart the waters, if we do not want to foind ourselfe making decision on old, approximate and outdated maps, like the great world explorers of centuries ago.
In the next section I will share the approach that we are promoting with our friends and partners to provide just that.
In the interconnected world , new economic actors emerge constantly, new ides, new.. Thankfully all those actors, trying to influence each other, pushed by self interest or by the common good interest generate and exchange information , and the potential for creating dynamic mapping int the specific context we need for a specific decision is there, if only we were able to tap on the immense ocean of information that is constantly generate. All we need to know, to shed light on a subject is freely available on the internet. Only it is buried in a sea of useles data. Of course, with enough money and time, we can sieve through the big data and collect , compile and analyze what is important to us. WOuldn’t it be nice to be able to draw a pciture of the keypalyers in a given context and and get a sense of who is infuencing whom, and who is learning from whm and who is creating generating new ideas vs who is just coping and following along . And maybe get a feeling if something new and unexpect is quiteely bubbing on an hidden area ofthe netwoprk of players that may burt to our faces in an unexpected disruption down the road in a few monts or years?
In order to understand this world it can help to reframe our picture of the the world, by putting information at its heart. We all agree that the world is interconnected and we have a shered picture of a global network wre information is constantly exchange. But this picture is misleading. Where it drives us to imagine nodes that transfer information by maintaining the integrity of the content of the information. This is exactly what the internet does, but the actual network is subtler than that. While the infractucture connects nodes that exchange strings of bith with an oustanding prcision, where each node trasfer exactly the same information, the actual master of the network are ideas, controversies, discussions, power struggels that take up a life or their own. Information and concepts are exchanged, modified, updated, distorted and redistributed . Take the example of fake news. The initiator may have an objective in mind, or not, but once they are sent in the wild, the take their own existenca and almost their own agency that goes beyond direct control. Populist like Trump, Johnson, Bolsonaro, LePen, or Salvini have become masters of playing with truth , launching or having their allies lounchin, statements thar rely on “alternative” realities while reaping the benefits of the political consequences, while escaping the accountability of the social, economical, or public health disasters they trigger.
In order to navigate is this surreal world, we need different tools and recognize that the actors, or the forces that impact our societies are not just the traditional “brick and mortar” institutons like companies, governements, powerful individuals. Virtual actors, that sometimes represent them but often have their own life and agncy are just as important. Foucault (Quoted in Akrich, Callon, and Latour 2013, 237 as Foucault 1975 p 191) was among the first to recognize that individual become embedded in a network of documents and statements and hat the transformations of those statements are critical to social evolution and impact directly our lives. To embrace this complexity it helps giving up a representation where only human being and recognized social institutions ar the actors involved in the social and economic space, but accept that other, multiple heterogeneous actors exist, defined and described by the documents, descriptions and discussions that other actors generate on their name. Such actors cna be object, biological entities , like COVID19, ideas or emotions. Actors aren’t interacting in social field. The social field is the result of their interactions(Akrich, Callon, and Latour 2013, 146, 267)
There is actually since th 1980’s a prolific social theory that promotes this approach. It is called ANZ Acotr-Network Theory and emerges from studies on the development of scientific ideas. Actions and network are actually two aspects of the same reality, hence the name Actor-networkwww
Irreversibilité, extension, boites noires, varieté (Akrich et al 2013, 249
In the scientific world , communication is highly codified, with peer reviewed publications that provide officill tracking of creation of new ideas and a referencing system that allows diffusion of those ideas in other works(Akrich, Callon, and Latour 2013, 218).
In the real world, it is very difficult to identify let aloe follow all the actors. Entr MILEVA