The Basic Idea
Imagine you’re a scientist who studies ants. If you grabbed a magnifying glass and examined an ant’s daily routine, you would conclude that ants are simple creatures. Each day, an ant will decide on a task to fill its time. Whether it be foraging, digging holes, rolling dirt up hills, or any other ant antics they can think of, this individual behavior is often predictable and easy to understand. But what happens when ants interact with each other?
After a few weeks of examining an ant colony, you would be shocked about the efficiency, teamwork, and complex structures that emerge from ants’ collective effort. Ant colonies are capable of performing incredibly complex tasks, ranging from building bridges to farming aphids as livestock. These behaviors are so complex that if you looked at the colony collectively rather than individually, you would see that it characterizes a living organism. Like a living organism, ant colonies have goals, adapt, expand, and have a life cycle. However, there is no “brain” behind this “living system”1. While it may be hard to imagine, no ant governing body masterminds all of the colony’s actions. By themselves, ants aren’t very smart. They’re only capable of doing very simple tasks. Despite their tiny brains, each day an ant makes a personal decision about what they are going to work on, and what emerges is a living system capable of tasks any individual ant couldn’t even dream of. How does this happen?
Complex systems are networks of small parts which interact to produce something that could not be predicted by analyzing any one individual part by itself. The general idea of a complex system is encapsulated by the common saying “the whole is greater than the sum of its parts”. While they are typically difficult to predict, the products of a complex system often result in broader trends that are observable over time.
Typically, these complex systems are built with 3 building blocks:2
- They are composed of a number of different actors who make their own decisions.
- These agents interact with each other.
- The system produces something greater than the sum of its parts.
While this may seem abstract, complex systems are everywhere if you know how to find them. Because they are so prevalent, complexity has gone from a niche academic idea to a key facet of several academic disciplines. The academic reach of complex systems is also vast: economics, education, biology, meteorology, urban planning, physics, mathematics, and many other fields have integrated its models. From something as small as your immune system to something as global as capital markets, complex systems are constantly operating around the world.
Systems: A sum of parts that operate together through dependencies, interactions, and feedback loops to make up a greater whole.
Complexity: A concept borrowed from mathematics, system complexity entails that the cogs in a system parts do not result in any predictable behavior.
Emergence: When a system is observed to have characteristics, the parts that make it up do not. Emergence only occurs when these parts interact, resulting in complex behaviors or creations.
Nonlinearity: A characteristic of complex systems, nonlinearity occurs when there is no straight cause-and-effect relationship between an input and an output. This is what causes unpredictability in complex systems.
Complex systems have a complex past. While we’ve been observing, creating, and interacting with these systems since the dawn of time, the academic study of complex systems themselves did not emerge until recently. Before such studies, the majority of academics relied on reductionist frameworks. Reductionism, in essence, involves viewing systems as a function of their parts.3 Examples of this are physics’ classical mechanics, neoclassical assumptions in economics, or biology solely attributing behavioral differences to genetics. However, these reductionist assumptions run into trouble when novel behaviors emerge from systems: they often can’t explain increasingly complex phenomena.
Like a complex system itself, the study of complex systems emerged from the independent study of multiple academics. Each of these sectors edged closer to complexity with each new discovery: physicists discovered nonequilibrium thermodynamics, chemists dissected quantum chemistry, and economists examined spontaneous order in markets. Importantly, most of these academics branched outside of their disciplines to make these observations, leading to the interdisciplinary approach characteristic of complex systems studies today.
While the realization of complex systems began from that effort, this discovery of chaos theory solidified it as a legitimate academic field. In 1963, meteorologist and mathematician Edward Lorenz stumbled upon the concept of chaos when he tried and failed to use initial weather patterns to predict future weather conditions. Also known as the butterfly effect, chaos is what throws predictability out of the window for the majority of systems.
The first formal research institute that focused on complex systems was the Santa Fe Institute, which was established in 1984. This institute brought together Nobel laureate physicists, economists, and multiple other esteemed scientists to formalize a general understanding of the complexity and complex systems. Today, a number of similar institutions examine how complexity, chaos, and systems theory can be applied to a variety of different fields and issues.4
From our early years, we’re trained to think linearly. Math and science classes teach us important things like how correct equations produce correct results or mixing two ingredients together results in a predictable chemical reaction. While learning this method of thinking is important, it’s often overused. On one hand, using linear thinking makes sense: life is complicated, so we try to simplify it. Unfortunately, simply ignoring complexity doesn’t fix the problems it can create in our ecological, technological, or social systems.
It is no secret that the world is becoming increasingly complex. There seems to be a new crisis that we will rapidly need to deal with to ensure our continued existence every week. Just over the past twenty years alone, we have seen a concerning acceleration of income inequality, global warming, economic crashes, and pandemics. Despite our desire to defeat these evils, these issues still persist. If cause-and-effect explained these issues, we easily could have solved them long ago. Unfortunately, this is not the reality.
To tackle these big problems, we need a new mental model. Given that the majority of the world’s injustices are propelled by complex social, economic, and environmental systems, adjusting our thinking to understand how complex systems operate can help bring us towards holistic solutions.5 The unique multidisciplinary problem-solving powers found in systems thinking can help us massively, as it hones in on the root cause of most issues, preventing us from getting lost in the minutia. By identifying what triggers systems towards failure, we can attempt to find solutions.
While complex systems may not be 100% predictable, they do begin to elicit patterns of behavior over time. With the growing availability of data, mathematical models, and holistic understandings of human behavior, we may be on our way to understanding how many of these systems operate and therefore find solutions to these large issues. However, this understanding will only come once we extinguish concepts of linearity, easy solutions, and predictability when approaching these issues. Only by leaning into chaos and complexity will we even begin to understand how our world works.
Despite the prevalence of complex systems, we still don’t fully understand how to deal with their negative outcomes. When these systems fail us, as they sometimes do, our response essentially comes down to two options: do we fight the system or let it sort itself out? This question is both hotly debated and controversial in the field of economics: what do we do when complex economic systems fail us? Do we try to fix the system or trust in the decisions of others? Does the government step in or stay out? This simple question has divided the field and become the basis for a variety of political beliefs.
If we try to look at nature’s complex systems to solve these problems, we don’t find an adequate solution either. While ant colonies may be adaptive to a major crisis without central planning, we often intervene with our immune system’s natural functioning using protective medicines and steroids. Both the beauty and difficulty with complex systems lie in their unpredictability. While the nature of complex systems implies that we cannot predict the end results of any action, the only thing we can do is attempt to test these systems as rigorously as we can and in as many permutations as possible. As complex systems tend to reveal trends over time, our only chance is to increase our knowledge of them. For example, our immune systems are one of the most complex systems that we know of, but through years of scientific study, we have determined what interventions work and which do not. While there is no way to know if you will have a bad reaction after taking a new medication, you can at least be confident, due to significant testing, that the odds are in your favor.
Related TDL Content
Nassim Nicolas Talib: If you are looking for innovative thinkers who integrate complexity into all aspects of their analysis, Nassim Nicolas Talib is your man. While controversial, this academic, derivatives trader, mathematician, and philosopher have devised several theories about predicting the unpredictable in complex systems.
Crises shock our complex systems, which can lead to interesting results. In this piece, TDL staff writer Kaylee Somerfield digs into what happens to consumer behavior when the economy turns towards recession. Her interesting findings illuminate what is important to us when the going gets tough, as well as provide a great example of the unpredictable results of changes in complex systems.
- Sullivan, T. (2014, October 7). Embracing Complexity. Harvard Business Review. https://hbr.org/2011/09/embracing-complexity.
- Waterloo Institute for Complexity & Innovation. (2020, May 7). What are complex systems? Waterloo Institute for Complexity & Innovation. https://uwaterloo.ca/complexity-innovation/about/what-are-complex-systems.
- Bar-Yam, Y. (n.d.). Concepts: Reductionism. New England Complex Systems Institute. https://necsi.edu/reductionist#:~:text=Reductionism%20is%20an%20approach%20to,ignoring%20the%20relationships%20between%20them.&text=The%20statement%202(1%2F2,together%20restores%20the%20original%20system.
- van Rooyen, J. M. (2020, April 21). The History of Complexity Science. Complex Systems Theory. https://complexsystemstheory.net/complexity/the-history-of-complexity-science/.
- McSweeney, K. (2020, March 26). Complex Systems Theory: How Science Solves Social Issues. Now. https://now.northropgrumman.com/complex-systems-theory-how-science-solves-social-problems/.