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Cities = Complex systems (pt. 2)
Sharing two things that connected big dots for me
Alright, I’ve hammered it in more than a couple of times now: cities are complex systems.
And successful cities are therefore also successful complex systems.
What this means: To be good at city building you need at least a basic level of reverence and understanding for complexity. Right?
It just so happens to be that most humans are at a disadvantage when it comes to understanding complexity.
You see, for better or worse, humans have mastered many things. But we’ve not mastered complexity. Why is that? Well…
While complexity is not black and white, we prefer clearcut answers.
While complexity shows a diversity of relationships, we like to simplify and to generalize.
While complexity is open-ended, a big part of western modus operandi is to artificially make closed contexts to work within because they’re easier to manage.
While complexity can produce counter-intuitive results, we are biased to only believe and put weight to things we understand.
While complexity can produce results we don’t like, we are predispositioned to dispute them rather than let ourselves be inconvenienced by them.
And failing to heed the signals of a complex world is coming to bite us back hard. We are experiencing more severe climate events than ever before. Our cities, infrastructures and supply chains are under stress. Our bodies develop all kinds of new ailments in a modern world full of artificial stresses. While no individual is solely responsible for causing any of these things, yet we all somewhat bear co-responsibility for these human-induced events that are happening. We are both responsible in one sense and not in another, and this ambiguity is what makes complexity really hard for us to wrap our heads around as individuals and collectively.
Other than cities, another domain that is also ruled by hairy complexity is the stock market. As Investopedia wrote: “Academic studies and empirical evidence suggest that it is difficult to successfully pick stocks to outperform the markets over time.” Basically, a bunch of human curators fare no better in results than an index fund like the S&P500.
So, if we’re so shite at understanding complexity, perhaps it’s time to go back to basics. What can we learn more about in order to be successful in a complex world?
Fortunately, I found two practical ideas recently that can shed some light on this.
John Gall, who wrote General Systematics (1975) had this to say about complex systems:
A complex system that works is invariably found to have evolved from a simple system that worked. The inverse proposition also appears to be true: A complex system designed from scratch never works and cannot be made to work. You have to start over, beginning with a working simple system.
This characteristic of complex systems is called Gall’s Law, which I discovered in Dense Discovery / 202.
The takeaway: Start small and simple. Successful simple systems are a prerequisite to successful complex systems. This is how many a great city had its start: as a simple crossroad along trade routes. Over time, some crossroads grew organically into cities, while others disappeared. For all the successful cities we see today, there were countless attempts by simple crossroads that wanted to be more but never could. Those places are long forgotten, and in wanting to recreate the successes of big cities, many of us forget how big cities once were but a crossroads too.
So, according to Gall’s Law, to emulate the kind of processes that resulted in successful complex systems like cities, you need incremental effort to organically build complexity over time.
What if you don’t have time?
I recently learned about a guy called Akira Miyawaki (1928-2021), a Japanese arborist famous for his effort to reforest urban environments in Japan and elsewhere.
I used to think highly of any reforestation programs… yes even the gimmicky 20 million trees being done by Mr. Beast, of YouTube fame. After learning about Miyawaki-san and his work, I’ve learned that not all reforestation programs are created equal.
Miyawaki was not impressed by contemporary reforestation efforts based on modern forestry principles. They didn’t use species uniquely adapted to Japan, nor were they ideal for combating the effects of climate change in his opinion. On top of that, many reforestation efforts failed, with groves dying out or struggling to survive after a couple of years without human intervention.
It turns out, it’s not that easy to recreate a complex ecological web!
Miyawaki’s approach to the problem was different.
First, he looked for the oldest, untouched forests in Japan. Because Japan’s landscape has been so heavily modified by human intervention, very little original forest remains… except in temples, shrines and cemeteries. These sacred places have been mandated to keep their forests pristine since they were established hundreds of years ago, so this became the perfect place for Miyawaki to look for inspiration.
He would study these original Japanese forests, mapping out what arborists call pioneer and secondary species, indigenous to the area, to populate his planting charts with. They are called pioneer/secondary because it mimics how reforestation naturally happens. In simplified terms, the pioneer species (lichen, grasses) are the hardiest, able to establish root in a barren ecosystem. Then they are followed by secondary species (shrubs and trees) that are able to grow once the first batch is established. For each site, he would go to the nearest shrine to understand its unique ecological fingerprint.
Using his methodology of selecting plants, his reforestation efforts were a resounding success. In his lifetime, he helped reforest and restore local ecosystems in over 1700 sites all over the world!
So, what did we learn about dealing with complexity here?
If we got no time, study a successful complex system that works and copy that.
Not just a lazy copy though. The devil is in the details. Miyawaki copied forests as local to the site as possible to maximize climate and soil match between the two. When city builders aspire to learn from urban examples elsewhere, are we considering the (mis)match in things like history, culture, economics, geography, local materials and politics?
The complex interplay between desirable urban form and its social context is likely more important than we like to admit.
Bonus: Wolf Reintroductions
You have probably heard about the phenomenon of wolves being reintroduced to habitats they once lived in, and how this totally changes the posture of the ecosystem for the better. Everything from healthier forests to changed watercourses, wolves turned out to be more than just a keystone species. Removing them from the complex ecological equation had ramifications in totally unexpected areas. Read more about the full scale of their effect here.
These are but some ways that are inspiring me on how we can better create solutions for and within a complex world. Do you know of any interesting approaches that model an effective way of learning from complex systems, so we can navigate and operate within them better?
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Until next time, stay safe and stay curious.