The dynamic ups and downs of human systems

No matter how hard we try to keep things simple and under control, many man-made systems turn out to be – or to become over time – quite complex and unpredictable. Despite our effort. Against our intentions. In stark contrast to our preference for simple, linear, static, controllable, predictable systems.

Computer software, stock markets and tax codes are prominent examples, but they are far from isolated. If you look around, you’ll easily find that the human endeavour is steeped in such kinds of large-scale, complex, evolving systems (let’s simply call those human systems): just consider the economy as a whole or the individual business enterprises therein, take a look at scientific disciplines or technology domains. Or, from yet a different venture point, think about the political and economic institutions that shape many of the other systems: they are human systems as well.

And all these systems are related to innovation in two different ways: individually, human systems are subject to innovation as we keep trying to shape, to control, or to improve them; at the same time, collectively they shape the environment and context for innovation to occur in. Innovation doesn’t work without or outside human systems. So maybe there are some common characteristics of these systems that can help us to systematically understand and explain the role that innovation plays.

Searching for a definition of such kinds of systems, you’ll inevitably come across the notion of complex adaptive systems. Such systems are comprised of a large numbers of components that can react to external stimulus and are dynamically interconnected. These systems behave as macro-structures that respond to their environment and co-evolve with it. The macro-behaviour of such a system is not subject to any centralised control; rather, the system adapts by reconfiguring its internal organisation. As a result, such systems can take us by surprise as they display macro-behaviours that deviate from and exceed the behavioural spectrum of their component parts. Furthermore, this macro-behaviour is often non-linear, i.e., a complex adaptive system might respond mildly to a major event, or could react violently to a minor trigger. In fact, while we can explain the evolving macro-behaviour in hindsight, it is impossible to predict it in detail, even with perfect knowledge of the constituent parts of the system.

For a more thorough introduction to complex adaptive systems, I’d suggest reading The Quark and the Jaguar: Adventures in the Simple and the Complex by Murray Gell-Mann. A Nobel laureate in physics, he is one of the architects of the emerging science of simplicity and complexity, focusing his attention on the question of how complexity arises from a set of simple rules and conditions. He will take you on an inspiring journey from the fundamental laws of physics to the astonishing diversity of the biosphere, with a narrative that is as solid in its scientific foundation as it is full of tangible and entertaining examples.

It is quite obvious that human systems are complex adaptive systems, and that insight gives us an additional idea of the effects of innovation in human systems. First, there is the intended effect: innovation serves as a means to adapt an existing human system to its evolving environment (a.k.a. “solving a problem”). But secondly, there is the undesired outcome: innovation increases complexity. In short: adaptation is the purpose of innovation, while complexity is the price to pay.

This sounds pretty familiar, as I arrived at a similar considerations a few months ago in a post on the complex interdependencies between energy and society. Back then I employed and built on Joseph Tainter’s idea that societies use complexity as a strategy for solving problems, as well as Ian Morris’ Paradox of Development, i.e., the observation that “success creates new problems, solving them creates still newer problems.” While those considerations are still consistent with the ideas on innovation in the context of human systems, they also kept an almost mono-disciplinary perspective by focusing on the behaviour of societies and macro-economics.

In The Upside of Down: Catastrophe, Creativity, and the Renewal of Civilization, Thomas Homer-Dixon presents a truly multi-disciplinary approach, masterfully crossing the boundaries between life sciences and social sciences in order to emphasise the commonalities they share. He collects stories of rise and fall, examples of the natural dynamics in biological systems and in human systems. And all these stories share the narrative of a development process that is mainly steady, with only rare bursts of dramatic change: a novel system germinates somewhere in a niche of its environment, grows slowly as a fledgling, absorbs more and more resources to rise and gain strength, flourishes to reign its environment, before it peaks and begins to fade, goes through decay and sometimes collapse, until it eventually disappears. You might conclude that there’s no Phoenix without ashes, neither in the biosphere nor in human systems. And regardless of any specific discipline, all those systems undergo essentially the same development process.

In his thinking, Homer-Dixon builds on Joseph Tainter’s ideas about the dynamic changes in societies, and he complements them with the concept of adaptive cycles, which was originally presented by Lance Gunderson and Crawford Holling in Panarchy: Understanding Transformations in Human and Natural Systems. In this landmark book, Gunderson and Holling edited and integrated the works of the Resilience Alliance, a group of scientists committed to research on resilience on socio-ecological systems in order to promote sustainability. As a results of their efforts, they delivered a unifying description and explanation of the dynamic ups and downs in, as the title indicates, human and natural systems.

Granted, those more than 500 pages might seem too much for a quick read. If you’d like a shorter introduction to the concept of adaptive cycles, you could turn to the 100 pages Synopsis that Gunderson and Holling offered as an excerpt of the full volume. Or yet shorter, you might prefer the very compact overview Holling provided in a 16-page article on Understanding the Complexity of Economic, Ecological, and Social Systems. Either way, you’ll find the main characteristics of adaptive cycles explained with illustrative examples.

As a very initial primer to the concept, the adaptive cycle falls into the four phases of growth, conservation, release, and reorganisation. It is described along three dimensions: the accumulated wealth (or potential) in the systems, the connectedness within the system, and the system’s resilience to external shock. Adaptive cycles exist across a variety of scales (both of space and of time), with smaller cycles turning faster and bigger cycles moving more slowly. And they are nested, working as interconnected cycles within cycles, so that fast events in smaller cycles can cascade upwards into the slower and bigger cycles, and vice versa. Finally,  the concept applies across disciplines, which implies that human systems and natural systems can mutually influence each other.

I’m curious to investigate this concept of adaptive cycles further, especially how it could be applied to innovation in human systems, and maybe become an element for innovation literacy. I can imagine potential use of the concept as an alternative framework to complement the innovation landscape; it could offer a new perspective on innovation fitness; it could help express the risk of continued resistance to change and describe the increasing vulnerability to shock; and it might help to draw lessons that could shape future innovation policy; and these are only some initial ideas. So there’s a lot to explore.

That exploration is what I intend to dedicate the upcoming posts to as yet another exciting journey is unfolding before us …