Complex Adaptive Systems

Many systems are complex and adaptive rather than deterministic.

You want your car to be a deterministic system, and the same can be said for many other critical systems in your day-to-day life. But it’s critical to realize that most of what you see around you now is not deterministic. Instead, many of the systems you encounter out in the world are known as complex adaptive systems (CAS).

John H. Holland, one of the key figures in complexity science, said that complex adaptive systems "are systems that have a large numbers of components, often called agents, that interact and adapt or learn."

To understand what his means, consider a car and a brain. Both are made up of different parts that work together to do something—that's why they're both considered systems. But that's where the similarities end.

Building Cars

If your car isn't designed to deal with rough terrain, the parts won't recognize said terrain and transform your car into an off-roading truck. If you make the decision to drive into that environment, the car will just start to break. Likewise, the parts of your car don't have anything we'd recognize as "autonomy." It's a system designed to exist within certain boundaries, and if you cross those boundaries it's bad news for the car.

Your car's lifecycle begins in the minds of executives at a car company, who, driven by motives like profitability and market expansion, decide to introduce a new model. This decision isn't spontaneous but rather the result of a complex analysis of market trends, financial forecasts, and competitive strategies.

Once the green light is given, teams of designers and engineers take over. These professionals operate within a web of constraints – physical laws, budget limitations, and manufacturing capabilities – to transform a concept into a tangible set of technical specifications.

After all that, the company builds the car on an assembly line, ships it out and sells it to drivers like you. This whole process is built around centralized decisions that are made in a top-down manner.

Deterministic systems tend to operate like this, and in this case it's for good reason. You wouldn't want a car that isn't deterministic—that would make your day-to-day life more chaotic than it needs to be. The car's performance is predictable; when you turn the key, you expect it to start, and when you press the accelerator, you expect it to move.

Growing Brains

Contrast this with a brain, which is a complex adaptive system. Unlike a car, the brain isn't built in a factory; it's the product of billions of years of evolution, each step the product of genetic and environmental interactions that shape its structure and function. This process is decentralized and bottom-up, with no central authority orchestrating the development.

The brain's development begins with a single fertilized cell. This cell divides, and its descendants differentiate into various types of cells. As the brain grows, it's shaped by both genetic information and environmental stimuli. This interaction is crucial: just as the terrain shapes the flow of a river, experiences shape the brain. Neural pathways form and strengthen with use, or weaken and disappear with neglect.

The brain's adaptability, or neuroplasticity, is its defining feature. Unlike a car that deteriorates with misuse or under challenging conditions, a brain can adapt and even thrive in unforeseen circumstances. Expose it to new languages, and it rewires itself to accommodate them. Subject it to puzzles and problems, and it strengthens its problem-solving abilities. This adaptability isn't just limited to youth, either—while it diminishes with age, the brain retains a remarkable capacity for change throughout life.

While cars of a specific model are nearly identical, no two brains are the same, even those of identical twins. Every brain reflects a unique history of interactions, decisions, and experiences. The brain's structure and function are constantly evolving in response to its environment, making it a dynamic, living system.

In terms of autonomy, each component of the brain—from individual neurons to larger structures like the hippocampus or prefrontal cortex—has a degree of autonomy, responding to and influencing its local environment. This autonomy allows for emergent phenomena: the brain's capabilities, such as consciousness or creativity, aren't directly coded in its genes but emerge from the complex interactions of its myriad parts.

The brain's functioning is not deterministic in the way a car's is. You can't predict exactly how a brain will react in a given situation, as you might predict a car's response to turning the steering wheel. The brain's responses are influenced by past experiences, current context, and even random fluctuations in neuronal activity. This unpredictability is a hallmark of complex adaptive systems.

Complexity is Everywhere

One way to think in broad strokes about the differences here can be found in the phrasing used: you build a car, and you grow a brain. They sound like synonyms, but the approaches are quite different. One involves a predictable, defined set of steps, while the other is a process with many probabilistic features.

While we all prefer to deal with deterministic systems, the reality is that complex adaptive systems tend to be the most impactful across multiple scales. Consider this list of CAS examples from Wikipedia:

Climate; cities; firms; markets; governments; industries; ecosystems; social networks; power grids; animal swarms; traffic flows; social insect (e.g. ant) colonies; the brain and the immune system; and the cell and the developing embryo.

Human social group-based endeavors, such as political parties, communities, geopolitical organizations, war, and terrorist networks are also considered CAS. The internet and cyberspace—composed, collaborated, and managed by a complex mix of human–computer interactions, is also regarded as a complex adaptive system.

In other words: you're surrounded by complex adaptive systems. Furthermore, it's axiomatic in evolutionary biology and ecology that evolutionary systems (which CAS tend to be) become more complex over time: If you assume variation and heredity, then diversity and complexity will on average increase, unless other forces, e.g. natural selection, constrain or override this tendency.

Now that you know this, it's worthwhile to reflect on your current set of problems that need to be solved:

• Are you dealing primarily with deterministic systems or CAS?

• Given the answer to that question, do you think your current methods are appropriate?

• How can you shift what you're doing and thinking in order to more accurately reflect the kind of systems you're working with?

These are questions you need to ask yourself on a regular basis, if only as a gut check to stop yourself from drifting into the trap of deterministic thinking.