Praxis and paradox

Even the best ideas “on paper” are no good if they cannot be practically applied in the real world. Theory is often differentiated from reality in this way, with the application or practice of ideas referred to as praxis.

We want to put these theories to work, but do we have a theory about how to put theory to work? What is the theory of praxis?

Our definition of sustainability has a broad scope, focusing on inclusivity and exhaustive consideration of many possibilities. Yet “scoping down” and narrowing considerations comes with benefits too. For reasons of pragmatism, political feasibility, comprehensibility, accessibility, and others, it can be useful to simplify things and focus in on one specific area, one specific need, one specific risk.

In the context of communicating sustainability too much information can lead to a range of negative outcomes such as information overload, decision paralysis. All of this leads to one basic question in practicing sustainability: what should our scope be? And can one like we are taking, with a broad and all-inclusive framework, still be useful in practical terms?

Operationalising sustainability requires a balancing act between broad and narrow perspectives.

The concept of “triage” is useful in highlighting how sustainability can be applied. In this model, prioritisation of one thing over another is based on evidence of urgency. A triage approach the impossible task of doing everything simultaneously, while still leaving room for all things to be considered –  allowing us to identify subtle threats and risks, to uncover hidden opportunities, and otherwise benefit from a broader approach that considers many perspectives.

Building a map – maybe even a data set?

The field of medicine has often struggled with a specific problem – managing the different interactions between drugs given to a patient. This is a complex problem made difficult by many factors, including accounting for the variables of a patient’s gender, ethnicity, personal medical history, and other factors.

It is a problem, in other words, often beyond the human mind’s ability to solve.

To get around these limitations, we’ve turned to Artificial Intelligence. AI like IBM’s Watson are learning how to excel at tasks like these and can offer far more comprehensive and accurate overviews of the complex interactions across hundreds of drugs, for any kind of patient[1] (IBM, 2019). The approach here; to collect, combine, and compile that data – and then feed it to Watson – is how something incredible will be achieved.

If the Earth and its inhabitants are the sick patient (and all indications suggest we are), then it’s worth noting that the area of sustainability has no Watson; no all-seeing Oracle we can look to for guidance.

Perhaps we will need something like this someday? Perhaps the challenge of persisting over time is a problem beyond the human mind’s ability to solve alone, without help. Already, we are putting artificial minds to use on singular, discrete sustainability projects, from climate modelling, to autonomous transport, to smart irrigation systems. Perhaps a time will come when these systems are supplemented by something like IBM’s Watson, a more generalized artificial intelligence, capable of insights between complex systems both natural and human-made. Some of these insights we can barely imagine right now. Like the discovery of the microscope, a whole new world – once invisible – could open to us.

What we are doing then with The Grass Ceiling, and what we encourage others to do, is help map this terrain for future travellers. Like cartographers of old, we are exploring a diverse and unfamiliar world, and capturing what we can of it to guide future people to come. And perhaps, appropriately for our STEM-driven era that promises profound technological progress, we are also building something of a data set – a resource that would help us build a “Watson for sustainability”. A catalogue of ideas and areas of investigation that any kind of holistic, integrative system would want to consider.

Embracing conflict

Lessons from “praxis at scale” in the coastline paradox

Mathematics and real-world situations can highlight how our epistemological approach – specifically, embracing paradox and competing truths – can make sense.

Fractal
An infinitely zooming fractal. From: Giphy | Loop Zoom GIF by Psyklon

Most of us know idea of the fractal; an infinitely-recurring, mathematically-defined structure that can be viewed in detail at any scale. Fractals are a good mascot for our definition and view of sustainability, for how we’re viewing knowledge: as something protean and shape-shifting, and “true” at all scales. Perhaps even more so, the Sierpinski Triangle should be our motif – a fractal that demonstrates a real-world paradox.

Fractal2
The Sierpiński triangle, also called the Sierpiński gasket or Sierpiński sieve, is a fractal attractive fixed set with the overall shape of an equilateral triangle, subdivided recursively into smaller equilateral triangles. Image from Wikimedia Commons.

The Coastline Paradox relates to the real-world problem of measuring the perimeter of a geographical area (for example, the coastline of Australia, some of which is shown below). As measurement accuracy increases, so too does the length of the coastline. Because accuracy can increase infinitely, it seems to suggest that therefore a coastline’s length can too. Theoretically, this means that Australia’s coastline is infinitely long – something that violates the laws of non-contradiction – it cannot be true that space is finite if it’s also true that coastlines have infinite length! Both are “true” however, but in different contexts.

CoastlineParadox
“Tending towards the infinite”: The coastline’s length grows as measurement accuracy increases.

Perhaps these kinds of seeming contradictions can illustrate a way to think about sustainability issues: often multiple conclusions or results are true (or at least have merit). What often matters is the context, the scale, and framework we’re applying.


Footnotes

[1] IBM. (2019). IBM Watson for Drug Discovery. Retrieved from IBM.com: https://www.ibm.com/products/watson-drug-discovery