So far I’ve described the adaptive cycles only in very elementary terms. Time to add a level of detail so that this concept can gain more contour and depth. Let’s take the lying eight as the starting point. Without a doubt that is the most common depiction of adaptive cycles that you can find on the web. And it is two-dimensional, illustrating the concept along the axes of connectedness and potential.
Before going straight to the lying eight, it’s worth to spend a moment to think about those two dimensions themselves.
- Connectedness is quite straightforward. This dimension is a characteristic of the relations between the components that make up the system: how many links are there, how well established, how strong, and how intensely are they used? These connections are a function of system size, but they also indicate the degree of control and dependencies within the system, which gives us a hint at the overall elasticity –or rigidity– of the system we consider.
- Potential is a bit tricky, as this term has many interpretations. Gunderson and Holling initially phrased it as “potential for change”, i.e., a system’s available means to respond to its environment. This dimension of the adaptive cycle is often simply called “potential”, and that is the term I’ll use mostly. Others call it “wealth” or “capital”, you’ll find “accumulated capital” or “accumulated resources”, while yet others prefer the term “capacity”. These terms are very similar, but we’ll have to remain mindful that they are similar only as long as we use them in their most abstract meaning, while their specific definition within various scientific disciplines can deviate substantially.
But now, without further ado, the lying eight:
At first glance, there are many similarities with the simple circle I’ve sketched before. There are still four phases, and the right-hand side remained unchanged (the conservation phase at the top, the release phase at the bottom). As the most striking difference, the left-hand side is literally turned upside down, now with the reorganisation phase at the top and the growth phase at the bottom. In addition, there are three new ideas that each deserve our attention: front–loop, back–loop, and exit.
Moving from the growth phase all the way through the conservation phase, the front–loop is where everybody wants to be. In the beginning, the system grows by binding and accumulating more resources (thus growing the potential), and by building and integrating new components (thus increasing internal connectedness as well). This initial period of rapid growth comes to a natural end when all easily accessible resources are absorbed by the evolving system. Up to that point, the system grows within the rules and constraints defined by its environment. From then on, the system seeks to gain influence and ultimately control over the environment in order to impose the conditions that are most favourable for the system itself.
As a consequence of these efforts, the further accumulation of resources is increasingly costly: that is what economists call diminishing marginal returns. In partial compensation, the system seeks to improve its efficiency through increased internal control. But these growing interdependencies within the system bind more and more resources, thus decrease the system’s potential. In the end, the loss of elasticity in the system leads to a level of rigidity that makes the system susceptible to external events that can then shake its foundation with surprising ease.
Eventually, the transition to the back–loop comes very quickly; it is triggered by a seemingly minor event and often perceived as shock. Running through the release and reorganisation phases, this is the dark side of the adaptive cycle where all the former structure and predictability are lost. In release, the system literally disintegrates: first it loses internal connections, the dependencies between the different components fall apart. Then it loses control over the resources that were previously tied up in the processes within the system, and after a relatively short time it even loses components. In the words of Gunderson and Holling, the system becomes leaky. While the connectedness is on a steady decline, the potential for change behaves differently: it hits a low very early, when the system is already too weak to fight the external changes, yet still strong enough to control many resources. As long as the external trigger keeps working, the decay of the system will continue. And as a consequence, the resources and components released by the system become available as fodder for other actors in the environment to drive further change. Hence the potential for change goes up again.
In the reorganisation phase (almost) everything is possible. This is a period of intense experimentation, of trial and error. Novel ideas and approaches can be tested easily as resources are available in abundance, regulatory control is fairly limited, and interdependencies hardly exist. In this situation, the total cost of failure is low, and the entrepreneur is encouraged to pursue alternative routes off the beaten track. As the French economist Jean-Baptiste Say remarked already in the 19th century:
The entrepreneur shifts economic resources out of an area of lower and into an area of higher productivity and greater yield.
The back–loop of the adaptive cycle is the place where this reallocation of resources occurs: the release phase sets them free, and they are directed to new uses in the reorganisation phase. It is the place where the hostility of innovation takes shape in all its destructive and creative facets.
However, the outcome of reorganisation is not predetermined: it might give rise to a new system very similar to the former system (that’s the black curve in the chart above); we might see a novel system with different functions, components, and boundaries (the grey curve); or the entire environment might deteriorate when the resources actually leak to other environments (as indicated by the exit curve). There is simply no guarantee that a complex adaptive system will always go through the exactly same incarnations every time it passes through the adaptive cycle.
This is the right moment to give the word to Gunderson and Holling again, who observe the different characteristics of the two stages of the adaptive cycle:
The front-loop stage … is a slow, incremental phase of growth and accumulation. The back-loop stage … is the rapid phase of reorganisation leading to renewal. The first stage is predictable with higher degrees of certainty. The outcomes following destruction and reorganisation in the back loop can be highly unpredictable and uncertain.
Even more importantly, they reflect on the objectives of those two different stages, and on their sequential interaction:
It is as if two separate objectives are functioning, but in sequence. The first maximises production and accumulation; the second maximises invention and reassortment. … The two different objectives cannot be maximised simultaneously; they can only occur sequentially. And success in achieving one tends to set the stage for its opposite. The adaptive cycle therefore embraces the opposites of growth and stability on one hand, change and variety on the other.
This idea of two different, yet sequential objectives does not come to mind intuitively when thinking about human systems, their optimisation, or innovation within them. All the more, it provides rich food for further thought. So stay tuned …
understanding innovation 