The SiD Sustainability Definition

Where This Fits

The previous unit asked "what is sustainability?" and showed why the word is so confused. This unit delivers SiD's answer: a formal definition built on complex systems theory, time boundaries, and the three RAH indicators. Everything in SiD method, process, and tools flows from this definition.

Systems do not only have a spatial boundary, but also one in time and in context. Something is ‘sustainable’ for a certain, relevant, time frame. We are clear to say ‘relevant time frame’ because, to take a simple example, it is irrelevant for us to be concerned about the sustainability of the Earth and its life forms beyond the expiration of our sun, which is scheduled to happen in 100 million or so years.

Often a ‘relevant time frame’ can be taken as several generations. Since these succeed each other while the definition is still active, as time goes by the essence should be maintained over many generations. In reality, looking much further than 50 years ahead is often not feasible. Yet, 50 years is insignificant compared to human history.

This is also where resilience comes in again. Resilience is a long term property encompassing patterns of change, while growth is just a part of the ebb and flood of the system in motion. In the end, nothing is eternal, and nothing should be.

Sustainability is not about endlessness or development for eternal preservation. There’s always a natural end to a system’s life span. And so it should be. Sustainable, explicitly, does not mean immortal, or eternal. Things come and go, and a sustainable system ‘pulses’ like all natural systems do, aiming to adapt itself to be better-fitted each time around.

A sustainable system also inherently accepts there is an end to things, and gracefully resolves itself when that moment is there. Like dead trees in a forest become new habitats for other organisms, leaving room for the cycle of life to continue. This rise and fall of things is also present on the system level.

It is about being able to continue to flourish, remain doing so as long as possible while being relevant, without endangering the system that it’s a part of. Complex Systems The word ‘complex’ appears in the definition to underline that we are talking about systems from the understanding of non-linear, infinitely complex entities, almost like biological entities, and not from a predictable, finite, mechanistic understanding of systems.

Let’s look at that further, and why it is important. We differentiate between ‘complex’ systems and ‘non-complex’ systems. These terms are analogous with terms used in various fields such as respectively ‘nonlinear’ and ‘linear’, and “complex” vs. “complicated”.

Non-complex systems are systems of which the objects and relations can be fully indexed and understood, they are finite in their composition, and in some cases, can be fully modeled using physics, mathematics, or other science tools. They’re called non-complex, rather than ‘simple’’, because non-complex systems can still be very complicated and far from simple.

For example, the electrical system of a house, is a non-complex system. You can model it using mechanical system analysis and predict its behavior. But it can still be pretty complicated to figure it all out. Working with these non-complex systems has been the prevalent mode of systems analysis, design, and innovation for the last century.

We know how to accurately model them, and what kind of behavior they can exhibit, which can mostly be explained from a mechanical perspective. Complex systems, however, are an entirely different ballgame. A complex system is a system which consists of a number of objects and relations so numerous that we can’t keep track of all of them.

Complex systems are also called chaotic systems or non-linear systems (to further confuse it all). Complex systems exhibit behavior that cannot be predicted using normal mechanical (non-complex) systems behavior or computer simulation. Hence, the ‘non-linear’ name. An easy example is the weather.

Nobody can accurately predict the weather

Nobody can accurately predict the weather more than a week into the future using mathematical models - there are too many factors involved, and the events that generate the governing future patterns may not yet have happened. As we try to predict further ahead, the complexities multiply so fast, any attempt at doing so grinds to a halt. Some complex systems may appear like a non-complex system, or have been treated as such in the past.

This is a dangerous move and continues to cause issues in society. Ever had a bad weather forecast ruin a party? This also happens to our economy, for example, by introducing policy measures that are well-intended but do not take into account the complexity of the system. One can blame the poor policy performance on ‘unexpected’ events, but really, only the expected is unexpected in complex systems.

An economic policy that does not account for complex dynamics is simply not resilient, which means it was plain bad policy. Thinking you can predict a complex system is a thinking error. Just as our brains are more than a sum of molecules, so are complex systems more than the sum of their parts.

They tend to behave less like machines and more like creatures displaying ‘emergent’ behavior. Acting on one aspect of a system in isolation will always have side effects on the rest of the system. Expecting complex systems to respond like machines is a one way road to disaster.

In SiD, we focus on complex systems over non-complex ones, because we feel they are the determinants of the future of our world. In sustainability literature, non-complex systems modeling is often used to explain certain economic or social patterns.

While useful as an exercise, and to use for insight, we should exercise caution when encountering this sort of argumentation. It’s tempting to want to simplify complex systems to try to understand their behavior in mechanical terms, in order to move forward, but it’s also dangerous. It is in the quality of ‘complexity’ that systems do their special thing. Because of this importance, we find the word ‘complex’ essential in SiD’s sustainability definition.

Even though complex systems can’t be predicted, they can be studied, learned from, their behaviors analyzed and intervened on in order to make them, for example, more resilient. We’ll get into more detail about this later on. To help get a feel for what a complex system is and isn’t, we’ve assembled 12 general rules that complex systems typically comply to on the next page.

We go further into system dynamics and complex systems further on in this chapter. Dynamic Systems The SiD definition defines sustainability not as a physical constant, but as a state of a dynamic system. This means that sustainability is an edge condition of something that always moves, changes, grows, shrinks and acts in accordance to changes in its environment and internal composition.

This means that a system can move and change while still remaining in the ‘state’ of sustainability, as long as it doesn’t cross the border of its state. Defining it as such allows us to evaluate and work towards sustainability without locking ourselves into static and rigid structures, which would inhibit resilience.

Because there’s always something that changes, be it due to smaller or larger changes in climate, the natural cycles of birth and decay, the laws of entropy or something else, a system needs to be able to adapt itself in order to be able to continue doing what it does if it is to survive (resilience).

Therefore, a sustainable system is always dynamic. Without dynamism there’s no capacity for adaptability, flexibility and therefore no resilience. A system without resilience is hard-pressed to be sustainable.

After all, if it can’t survive changes in its environment, how can a system be called sustainable? We can therefore expand our understanding of sustainability to be not just a state of a system, but explicitly that of a dynamic system. It puts sustainability in the realm of systems analysis and science, including network and complexity theory. This enables a myriad of new perspectives on how to achieve and work with sustainability, allowing many new innovative pathways to be explored.

Resilience and autonomy The second sentence

Resilience and autonomy The second sentence of the SiD sustainability definition is: “In this state, a system can continue to flourish resiliently, in harmony, and without requiring critical inputs from outside its system boundaries.” The first sentence identifies what we understand the word sustainability to truly be: a state of a complex dynamic system.

This second sentence identifies what this state actually is. This sentence can be broken down into three terms, defining that a sustainable system is one which is resilient, autonomous, and in harmony. As you can see, we captured the part “without requiring inputs from outside its system boundaries.” in the word ‘autonomy’.

Resilience, Autonomy, and Harmony are the main three system indicators for sustainability in the SiD system. Resilience determines the degree to which a system can survive unexpected occurrences, a critical part of continuing to exist. Autonomy determined to what degree a system can take care of its own needs, and its ability to continue doing so.

We’ll discuss Resilience and Autonomy in detail further on, but Harmony may seem a little odd here as a term. Let’s look at that for a minute. Harmony, Social justice and Ethics In the first part of the definition, we use the word ‘harmony’. In the second part of the definition, where we exemplify what a sustainable society may surmount to, we refer to just societies. These include the necessary elements of social justice and discussions about fairness.

Inharmonious systems (unjust, inequitable, large divisions of resource control, etc.) give rise to internal strife and even war, and thus, endanger the sustainability of a system. A system can be resilient and autonomous, but without harmony between its agents it will still collapse due to the eruption of internal tension.

One can even say that a system that is resilient and autonomous, but not harmonious, is the opposite of what we hope to achieve, conjuring up images of hardy evil empires impossible to overthrow.

Harmony finds much of its intelligence from agencies dealing with human rights, and ethics. Ethics discusses matters of equity, social justice, the perception of value, and how we determine ‘good’ and ‘bad’. You can read about some main ethical perspectives in the tools chapter, and we’ll discuss Harmony together with Resilience and Autonomy in detail in section 1.2.4.

We aim to flourish The word ‘flourish’ is also present in the definition. This gives us a way to positively regard difficult to quantify values such as quality of life, cultural and artistic value, etc. Resilient, self sustained, harmonious life is already great, but there’s value in excitement as well, which is where flourishing comes in.

How does it all add up? With the SiD sustainability definition, we determine that a sustainable system is one which is self-sufficient, resilient enough to continue operating under a wide range of expected and unexpected events, and is harmonious and just while it flourishes.

To translate that to our modern society, this means a society where all the energy and material loops are closed, we no longer make use of finite resources, and wealth and power are distributed in an ethical way.

It means that our ecosystems and fellow species are thriving, allowing us to benefit from their resources without breaking them down as we do so. It means an equitable society in which we all have a chance to lead a life with quality and create a meaningful existence for ourselves, our children and loved ones. And, our resources are more or less equitably distributed. This is what the last part of the definition describes.

And who doesn’t want that? Well, there are some. Discuss among yourselves... Now that we have a basis to align and agree on as far as what we hope to achieve on a system level, let’s try to see how this breaks down into the practical, real world, and how we can go about achieving this.

In the next sections, we start by creating a language to analyze systems and their components, how we can measure them, and how to create new solutions that achieve that goal.

Takeaway

SiD defines sustainability through the lens of complex systems: a sustainable system is one that maintains its Resilience, Autonomy, and Harmony over a relevant time frame, without undermining the larger system it belongs to. This is not about permanence. It is about adaptive capacity, self-sufficiency, and internal balance, sustained long enough to matter.