Have you ever heard of complexity science? The name itself is enough to make one run in the opposite direction but all it refers to is seeing the bigger picture. At its core, complexity science asks us to look at things as a whole and identify the universal patterns that they may contain. It asks us to step back and take a look instead of diving in and fine combing through every small detail.
Sounds like it goes against everything we have ever been taught to do right? However, complexity science can be extremely informative once you know how it works. It is possible to connect the dots on events that seem completely unrelated and that is what makes it truly fascinating.
In this book summary readers will discover:
- Complexity science and complex systems explained
- The difference between complexity and chaos
- Examples of complex systems
- Complexity science in love and war
Key lesson one: Complexity science and complex systems explained
Neil Johnson defines complexity science as the study of phenomena which emerge from a collective of interacting objects. If you think about it in these terms, complexity practically surrounds us every day. The crowds we walk in can be defined as a group of interacting people and being stuck in traffic is nothing more than the interaction of cars. In all these instances of complexity, one thing remains constant – competition. A car in traffic will compete for space on the road, people at a concert compete for space in the crowd and even cancer can be considered as a competition between healthy and cancerous cells.
When this competition takes a wrong turn and leads to unexpected results, the complexity that arises in each scenario can be revealed by complexity science. This means that complexity science can be used to solve problems in similar systems as once a connection is identified, it is easier to see in different situations. This may seem pretty confusing at first but when it is actually applied, it makes perfect sense. Complexity science has the potential to be applied to a multitude of fields for this very reason.
The complex systems that we deal with vary in our day to day lives. The situations or phenomena that arise from these systems can happen without any outside interference or any form of coordination. The changes that occur in these systems can also vary and can be completely random or extreme. Just consider the financial markets. Often, the exact cause of a crash is difficult to identify. Thus, the system seems to change between ordered and disordered behaviour and this seems to be constant. Why? Because the objects in a complex system are affected by feedback from past events that affect current events or feedback from one location that affects another. An example of this would be your usual route home from work. If the traffic on this route has been bad the last few days for no apparent reason, you may decide to try a different route to avoid this situation. This represents a change in behaviour as you change your route due to knowledge of past events or feedback. Likewise, in complex systems, feedback can create either order or disorder with these changes. It is this complicated interaction that makes complex systems seem alive.
Key lesson two: The difference between complexity and chaos
It is not unusual for complexity to be confused as chaos. However, they are actually very different. In fact, chaos can be the output of a complex system. Normally, the output of a complex system refers specifically to the number that is produced by the collection of objects in the system. Chaos would occur when this number varies greatly and thus seems completely random.
Although this can occur in any situation at any given moment, complexity does not imply chaos. At times, complex systems can remain stable with no chaos in sight. Thus, it should always be remembered that although chaos is complicated, it is not complex. The best way to understand this is to consider an example of a librarian who has to sort out books using a very defined system. It is fine if they have just one shelf of books to organize, but as the number of shelves grows, it becomes more difficult to figure out where each book fits in the organized order. Furthermore, if a new employee comes in, they might find the order completely chaotic if they are not aware of the system used in the sorting process. However, when it comes to assessing the complexity, you don’t look at the behaviour changes caused by the sorting, but rather the interaction of all objects within the system and the changes caused by feedback.
Key lesson three: Examples of complex systems
Firstly, let’s consider the complex system of a crowd. A crowd is made up of a collection of people. Each person has their own behaviour, preferences and thoughts within the system which implies increasing complications. However, it turns out that in a large group, these differences tend to cancel each other out. This can even be seen when you consider reality TV shows that feature random groups of people. We can see similarities in their behaviours in our own groups at home. Likewise, group behaviours such as those seen in wars, traffic jams and even financial markets are similar across the globe regardless of the differences of the people involved. In addition, behaviours of opposing personality types cancel each other out.
Secondly, as humans, we are social animals. In our lives, both personal and professional, we create networks of connected people. We know who is connected and what their interactions are. These networks surround us. It could be the people we travel with on the bus or the group of colleagues we have drinks with after work. Networks are also an example of complex systems and feedback in these networks is important for its complexity. The information or feedback that flows through a network thus creates its complexity. Studying networks and their behaviours can therefore be very informative because it can be applied to other similar systems. In biology, for example, complex systems in the form of networks also exist in the form of the vascular networks of trees and humans. We depend on these networks for the transport of nutrients and understanding these networks can be important in diagnosing and treating problems. Even the transmission of an infectious disease is representative of a network. It is important to understand both the biology of the disease as well as how it moves within a network of people in order to stop the spread of the disease.
Lastly, the financial market is also an example of a complex system. Traders represent the individual objects that interact within the system deciding whether to buy or sell. There is no foolproof way for traders to predict the outcomes of financial markets and even if one did exist, it would not work for long. This is because of the amount of feedback generated in financial markets. If the prediction model worked once, we would use it on our next trade and therefore distort the market. According to complexity science, markets are neither predictable nor non-predictable. They behave as all complex systems do and fluctuate between order and disorder.
Key lesson four: Complexity science in love and war
Believe it or not, complexity science can even be applied to dating. For all those people out there searching for their other half, you are part of a collection of people that are also searching. Every one of you is thus competing to find your partner! But there is good news! Complexity scientists have found that despite everyone’s individual preferences in a partner, it did not have an impact on the ratio of singles to non-singles. This means that although dating is complicated because let’s face it, people are searching for the person right for them at that moment in time, it is still quite possible to meet them!
On the other end of the spectrum, we must consider the complex systems that are formed in times of war. Overall, groups are working together, competing for limited resources such as land and power. However, war can become complicated as more than one side can be fighting the same war. This results in an asymmetrical situation because the behaviour of these subgroups cannot be predicted. Will they join forces and fight together or will they go up against each other? When complexity science is applied in these situations, however, it becomes clear that patterns can be seen in the behaviours of groups at war. This is because the way wars occur and what happens has less to do with where it is occurring or the reason behind it and more to do with the way in which human groups interact.
Therefore, understanding the behaviours of groups through complexity science gives us the possibility of resolving conflict better as well.
The key takeaway from Simple Complexity is:
Complexity science can seem like a complicated field of study but when it is understood and applied correctly, it has the potential to be a lifesaver. It is still a fairly new area of study but as it gains traction, people begin to realize the implications of its applications in the real world. It has the ability to solve some of the biggest problems we face today. Whether it is used to better understand financial markets, wars or even diseases, the insight that complexity science can provide is priceless.
How can I implement the lessons learned in Simple Complexity:
Take the time to step back and assess the interactions around you. Whether you are thinking about dating or financial markets, be aware of the feedback in the complex systems you are surrounded by. By keeping feedback in mind, you will be able to better identify the interactions and outputs they result in. This can be used in similar situations in future.
