A small note before getting into the flow of this: I’m one of those people who is more and more prone to seeing the world from two distinct sides: as a vast network of fuzzy but meaningful human relationships, but also as a mathematical construct or network. And the more I learn, the less I feel I’m analogizing with that last bit — data doesn’t just describe this place we find ourselves, data is this place. In some ways, this changes nothing, but in some ways, it feels like a frame-shift. It feels to me like the realization that “I’m not stuck in the traffic jam; I am the traffic jam.” Anyhow, I’d love to share more about this perspective later, but for now, I wanted to add that texture to the thoughts below.
Back in May, I had the opportunity to attend and participate in RightsCon 2018, a digital human rights conference. It was a refreshingly interdisciplinary space — hey, it’s not often that you find a international event with sessions that thoughtfully tackle themes of inclusivity, human rights and AI.
I’ve been interested in consensus lately, and so a session on “multistakeholder internet governance” caught my attention. I don’t know about you, but a few years ago, this is one of those topics that would’ve been filed in my lizard brain under “boring as hell”. Or maybe a more lizard-friendly index of “neither warm rock nor delicious ants”. But anyhow, this particular internet governance consensus-building process was an inspiration for one of my current obsessions, vTaiwan, and so it seemed worth sitting in.
As I was listening, it became clear that there were some long-term characters in the internet governance world who were sitting on this panel. These people truly had wisdom and decades of experience. And they were also lamenting the lack of fresh participants in the ecosystem. This dynamic got me thinking in a very systems-biased way, as I’m prone to do — right or wrong. Before sharing, I should offer some context.
What is complexity science?
There’s a lot here that could be unpacked, but I’ll try to keep it simple. In a theoretical sense, complexity science is a network-oriented approach for seeing the complex systems in which we’re all participants: genetics, cities, communities, language, multicellular organisms, ecosystems. But for me, in a more practical sense, complexity science involves recognizing generalized “winning patterns” that emerge from various networks, that have been selected within different scales and contexts. By “selected”, I mean that very loosely. It’s not an individual that selects, but rather the environment which does. In fact, biological evolution is one of the forces the “selects” and validates successful patterns, but it’s not necessarily the only one. The complex environment of language, which includes interplay of culture and phonetics, could also be said to select certain words or phrases for amplification.
As one example, “specialization” is a pattern that has been discovered in many networks, including those of multicellular organisms and human societies. Could we more closely pattern human specialization after biological specialization, which has had orders of magnitude more iterations to stumble upon effective tactics?
Or alternatively, we could consider “meiosis”, the biological process of remixing and sharing genetic information with the next generation of organisms. There are some other processes of doing this, but this one has persisted among the vast majority of complex organisms. Could we use this process to model more effective ways to seed new communities from an origin community, and better share culture and norms?
These are the frames of consideration that complexity science offers.
So anyhow, as I’m sitting in on this session, I’m realizing that what we’re really trying to figure out with internet governance is how to govern a complex system. So it’s perhaps less internet governance, but rather network governance. And so what patterns have we seen in other systems, that deals with this challenge of creating space for new participants, while honouring the history of past participants?
And this lead to some interesting thoughts on the big pattern that often goes unspoken: Death.
Death is hegemonic.
It may sound funny to describe death as a pattern. We often talk about it like it’s this fact of existence. And it might as well be. It’s omnipresent. Pretty much every living thing we know of, dies. It has prevailed in all living networks, through countless iterations. Death is hegemonic.
But what about death might be selected for? How does it benefit the network? Perhaps it’s best to imagine this first at the scale of individual personal relationships. That’s simpler, and I believe the reasoning is portable up to larger social scales.
Imagine how we might have moved on from Newtonian physics if that intellectual heavy-weight Newton were still alive and influential within the network. One could imagine that it might be difficult. Newton’s thinking was clever, but we also understand it to be misguided by today’s measure. In a world without death, new ideas would have to grapple and contend not just with static ideas and information, but with the dynamic and increasingly stubborn minds that birthed them. (This stubbornness and confidence of age, is perhaps another selected pattern, with it’s own subtle network rationale, but I digress.)
Instead, the mind of an organism dies, and leaves information scattered throughout the human network in the form of static reference — in the minds of family and peers, in blog posts and newspaper articles, in books and distorted recollections.
So how would one describe the pattern of death? Through an unsentimental network lens, we could perhaps think about it as a data compression or noise reduction tactic of the larger evolving system. Or at a more human scale, we could imagine it as a process by which organisms go from the role of living dynamic actors to become static references.
We could perhaps think about death as a data compression or noise reduction tactic…
While the Newton example is on a worldly stage, there’s perhaps another example that’s more on the personal level. You can imagine this same dynamic playing in the relations between parents and children. The death of a parent, tragic as it is, is an opportunity for this complex character to move from actor to reference in the minds of their children. This memory is now open to healthy reinterpretation, which perhaps gives their ideas and actions more meaning than when they were alive. A static reference of a human, whether it’s a memory or a book or an article, becomes a bare skeleton picked clean, on which to hang new ideas and emotions and reflections.
After death, what remains is an abstraction — a simplification of the human who once was. But this abstraction can be re-imagined and built upon by the folks who follow.
And maybe this is healthy. Maybe this makes for healthy societies, with the right balance of new imaginings and old wisdoms on which to hang them.
So if this pattern, Death, has been selected in other systems over millions of iterations, then how can we implement death-like features in our designed governance processes? Are there ways that we could more intentionally cultivate this movement of members from active participant to static reference? In some ways, we get this already: People involved in organizational governance tend to slowly cycle out (or burn out), but remain available through informal social networks. Could we or should we make this more explicit, and bring it into the process itself? What form might that take?
I’m not sure of any answers, but they seem like worthwhile questions to ask :)