In Seach of the Future Organization-II: The Power of Wolves

Pravir Malik
7 min readMar 9, 2024

Part I concluded that it is necessary to see things from a systems point of view to understand natural complexity. The story ‘How Wolves Change Rivers at Yellowstone National Park’ sheds practical insight into such complexity and introduces key system properties. Here is a word extract from the Sustainable Human YouTube video linked below:

“One of the most exciting scientific findings of the past half-century has been the discovery of widespread trophic cascades. A trophic cascade is an ecological process that starts at the top of the food chain and tumbles all the way down to the bottom. The classic example is what happened at Yellowstone National Park in the United States when wolves were reintroduced in 1995. We all know that wolves kill various species of animals and perhaps are slightly less aware that they give life to many others. Before the wolves turned up, they’d been absent for 70 years, and as a result, the number of deer had built up in Yellowstone Park despite efforts by humans to control them. The deer managed to reduce much of the vegetation there to almost nothing.

However, as soon as the wolves arrived, even though there were a few in number, they started to have the most remarkable effects. First, they killed some deer, but that wasn’t the major thing. Much more significantly, they radically changed the behavior of the deer. The deer started avoiding certain parts of the park, where they could be trapped most easily, particularly the valleys and the gorges. And immediately, those places started to regenerate. In some areas, the height of the trees quintupled in just six years. The number of migratory birds started to increase. The number of beavers starts to increase because beavers like to eat parts and produce from trees. But beavers are also ecosystem engineers who create niches for other species to thrive. The dams they built in the rivers provide habitats for otters, muskrats, ducks, fish, reptiles, and amphibians.

The wolves killed coyotes, and as a result, the number of rabbits and mice began to rise, which meant more weasels, foxes, and badgers. Ravens and bald eagles came down to feed on the carrion that the wolves had left. The bear population also began to rise, partly because of more regenerating shrubs. The bears reinforced the impact of the wolves by killing some of the deer calves.

But here is where it gets really interesting. The wolves changed the behaviors of rivers. The rivers changed in response to the wolves. The reason is that the regenerating forests stabilize the banks, so they collapse less often. Then, the rivers became more fixed in that course. Similarly, by driving the deer out of some places and the vegetation recovering on the valley sides, there was less soil erosion because the vegetation stabilized that as well.

So the wolves, small in number, transformed not just the ecosystem of the Yellowstone National Park, but also its physical geography.”

The story of introducing wolves is interesting because many components are fundamental to thinking about a system canvas. There is what is called sensitive dependence to initial conditions. None of those changes would have happened if the wolves were not introduced. Yellowstone would have continued down a destructive trajectory if the deer had been prevalent. Further, there was a lot of co-dependence between different species. When the wolves came in, coevolution between the different species and plant species was positively affected.

Yellowstone National Park began to thrive when the wolf was introduced into it. But it existed as a system, albeit increasingly dysfunctional, even when the deer dominated. What must also be considered is the kind of system Yellowstone National Park is. Is it a complex adaptive system, considered the most sustainable, or another? In constructing a systems canvas, this is the kind of system — a complex adaptive system — we want to be able to represent. The ‘complex’ means you can’t predict what will emerge through the interaction of parts. For example, we couldn’t predict, on the face of it, that the course of rivers will change. That is unexpected unless you’ve researched ecosystems deeply and understand this. It’s ‘adaptive’: the system will adapt to whatever challenge is coming to it. And here, for example, if we think about Yellowstone National Park, then the question is, how adaptive was it in the absence of the wolves? There would have been another trajectory in play where its destruction was inevitable. So, that begs the question of what we mean by adaptive and what a complex adaptive system is. And then finally, a ‘system’ is something that contains many independent parts that operate as one entity.

When we think about Yellowstone National Park, we may say that it became a complex adaptive system with the introduction of wolves. This is an important insight. It means that a system can potentially be changed by injecting new forms or new forms of energy: the destiny of a system can be changed by introducing a different type of energy.

The destiny of a system can be changed by introducing a different type of energy.

Historically, the most robust complex adaptive systems have been in cities and civilizations. If we think about a city, no one party usually is in control. If problems occur, then often different populations within the city will rise to address it if it’s a well-operating city. So, there are different ways in which it will operate depending on the type of challenge or whether things continue normally. That makes it a good example of a complex adaptive system. Again, we are trying to understand the ideal system canvas we want to work with when considering the future enterprise. So now the question is, what drives the sustainability of complex adaptive systems? So, whether it’s a thriving civilization or a city that withstood the test of time and remade itself again and again without falling into destructive habits, what is driving that sustainability?

From a high-level point of view, we can say there are two factors. There’s something that happens when a well-functioning city grows, whereby economies of scale kick in, and there’s also something that happens that drives innovation. So, imagine that you have a city that isn’t a complex system, and you double the size. You increase the population from 1 million to 2 million people. Since it is not a complex adaptive system then, you would expect that there are no economies of scale advantage, and as a result, you would need to double the number of grocery stores to service that increased population, or you would need to double the number of petrol pumps to service the population. However, if it’s a complex system that’s working well, then in reality, you don’t need to double the grocery stores and petrol pumps, but you need less than double — say one and a half times even though the population is double.

Similarly, on the flip side, if it is a well-functioning complex adaptive system, a well-functioning city, then you would see that the kind of innovation that happens, or the number of patents per population, or the inventions that become real, that doesn’t scale linearly either, but it scales more than linearly. As the population doubles, these innovation indicators may increase one-and-a-half times. Some have attributed this to the random meetings in coffee shops, restaurants, or parks, where people come in and out in an unplanned manner, have unplanned conversations that result in sparking new ideas and connections, and so on. So, ultimately, when you talk about the sustainability of a complex adaptive system, it’s this differential between the two scales that really would drive what’s responsible for the sustainability of a complex adaptive system. This differential can be considered a multiplier relative to the unitary or linear scaling of a city that is not a complex adaptive system.

However, that is still a surface view that gives us concrete insight into a system functioning as a complex adaptive system. What are the properties of complex adaptive systems when you look under the hood? As I mentioned, these were highlighted using the Yellowstone National Park example. We know that no one entity is in control. It’s a system of distributed control. There are many different species: animal species, plant species, rivers, and soil, and the high degree of interaction of unplanned connectivity contributes to it being a complex adaptive system. As already mentioned, co-evolution happens — as the behavior of one species changes, it impacts other species, and there is sensitivity to initial conditions. There is non-linearity: you would never expect, on the face of it, that just introducing a relatively small number of wolves would change things so radically. And so, when you look under the hood, you get a sense of what you need the system canvas to be able to reflect, display, or mimic.

However, there is still more; we can gain insight by examining the system from the top down.

(To be continued…)

Part — I: The Wizard of Oz

Part — II: The Power of Wolves

Part — III: The Necessity of Poetry

Part — IV: The Other Side of the Coin

Part — V: EQ & Managing at the Margin

Part — VI: The X-Factor

Part — VII: Power, Jedi Power & Light

Part — VIII: The Mathematics of Organization

Part — IX: Imperative of a Quantum-Like Core

Part — X: The Secret of Nataraja