A Primer on Emergent Complexity

—“Break it down to its component parts” reductionism doesn’t work for understanding some vastly interesting things about us. Instead, in such chaotic systems, minuscule differences in starting states amplify enormously in their consequences.
—This nonlinearity makes for fundamental unpredictability, suggesting to many that there is an essentialism that defies reductive determinism, meaning that the “there can’t be free will because the world is deterministic” stance goes down the drain.
—Nope. Unpredictable is not the same thing as undetermined; reductive determinism is not the only kind of determinism; chaotic systems are purely deterministic, shutting down that particular angle of proclaiming the existence of free will.
—These emergent properties are robust and resilient—a waterfall, for example, maintains consistent emergent features over time despite the fact that no water molecule participates in waterfall-ness more than once.
—Often the emergent properties can be breathtakingly adaptive and, despite that, there’s no blueprint or blueprint maker.[6]
No need to gather data about the length of every possible route and have a centralized authority compare them and then direct everyone to the best solution. Instead, something that comes close to the optimal solution emerges on its own.[*]
These are, respectively, called a Cantor set, a Koch snowflake, and a Menger sponge. These are mainstays of fractal geometry, where you iterate the same operation over and over, eventually producing something impossible in traditional geometry.[19]
Which helps explain something about your circulatory system. Each cell in your body is at most only a few cells away from a capillary, and the circulatory system accomplishes this by growing around forty-eight thousand miles of capillaries in an adult. Yet that ridiculously large number of miles takes up only about 3 percent of the volume of your body. From the perspective of real bodies in the real world, this begins to approach the circulatory system being everywhere, infinitely present, while taking up an infinitely small amount of space.
Branching patterns in capillary beds
What has this section provided us? The same themes as in the prior section about pathfinding ants, slime molds, and neurons—simple rules about how components of a system interact locally, repeated a huge number of times with huge numbers of those components, and out emerges optimized complexity. All without centralized authorities comparing the options and making freely chosen decisions.[*]
Most neurobiologists spend their time figuring out minutiae like, say, the structure of a particular receptor for a particular attractant signal. And then there are those marching superbly to their own drummer, like Robin Hiesinger, quoted earlier, who studies how brains develop with simple, emergent informational rules like we’ve been looking at. Hiesinger, whose review papers have puckish section titles like “The Simple Rules That Can,” has shown things like the three simple rules needed for neurons in the eye of a fly to wire up correctly. Simple rules about the duality of attraction and repulsion, and no blueprints.[*] Time now for one last style of emergent patterning.[28]
Then there are adaptive power-law relationships in the brain. What counts as adaptive or useful in how neuronal networks are wired? It depends on what kind of brain you want. Maybe one where every neuron synapses onto the maximal possible number of other neurons while minimizing the miles of axons needed. Maybe one that optimizes solving familiar, easy problems quickly or being creative in solving rare, difficult ones. Or maybe one that loses the minimal amount of function when the brain is damaged.
You can’t optimize more than one of those attributes. For example, if your brain cares only about solving familiar problems quickly, thanks to neurons being wired up in small, highly interconnected modules of similar neurons, you’re screwed the first time something unpredictable demands some creativity.
While you can’t optimize more than one attribute, you can optimize how differing demands are balanced, what trade-offs are made, to come up with the network that is ideal for the balance between predictability and novelty in a particular environment.[*] And this often turns out to have a power-law distribution where, say, the vast majority of neurons in cortical mini columns interact only with immediate neighbors, with an increasingly rare subset wandering out increasingly longer distances.[*] Writ large, this explains “brain-ness,” a place where the vast majority of neurons form a tight, local network—the “brain”—with a small percentage projecting all the way out to places like your toes.[33]
Thus, on scales ranging from single neurons to far-flung networks, brains have evolved patterns that balance local networks solving familiar problems with far-flung ones being creative, all the while keeping down the costs of construction and the space needed. And, as usual, without a central planning committee.
Now the awesome final example. As a tautology, studying the function of neurons in the brain tells you about the function of neurons in the brain. But sometimes more detailed information can be found by growing neurons in petri dishes. These are typically two-dimensional “monolayer” cultures, where a slurry of individual neurons is plated down randomly, then begin to connect with each other as a carpet. However, some fancy techniques make it possible to grow three-dimensional cultures, where the slurry of a few thousand neurons is suspended in a solution. And these neurons, each floating on its own, find and connect up with each other, forming clumps of brain “organoids.” And after months, these organoids, barely large enough to be visible without a microscope, self-organize into brain structures. A slurry of human cortical neurons starts making radiating scaffolding,[*] constructing a primitive cortex with the beginnings of separate layers, even the beginnings of cerebrospinal fluid. And these organoids eventually produce synchronized brain waves that mature similarly to the way they do in fetal and neonatal brains. A random bunch of neurons, perfect strangers floating in a beaker, spontaneously build themselves into the starts of our brains.[*] Self-organized Versailles is child’s play in comparison
What has this tour shown us? (A) From molecules to populations of organisms, biological systems generate complexity and optimization that match what computer scientists, mathematicians, and urban planners achieve (and where roboticists explicitly borrow swarm intelligence strategies of insects[37]). (B) These adaptive systems emerge from simple constituent parts having simple local interactions, all without centralized authority, overt comparisons followed by decision-making, a blueprint, or a blueprint maker.[*] (C) These systems have characteristics that exist only at the emergent level—a single neuron cannot have traits related to circuitry—and whose behavior can be predicted without having to resort to reductive knowledge about the component parts. (D) Not only does this explain emergent complexity in our brains, but our nervous systems use some of the same tricks used by the likes of individual proteins, ant colonies, and slime molds. All without magic.
Thus, in my view, emergent complexity, while being immeasurably cool, is nonetheless not where free will exists, for three reasons:
1.  Because of the lessons of chaoticism—you can’t just follow convention and say that two things are the same, when they are different, and in a way that matters, regardless of how seemingly minuscule that difference; unpredictable doesn’t mean undetermined.
2.  Even if a system is emergent, that doesn’t mean it can choose to do whatever it wants; it is still made up of and constrained by its constituent parts, with all their mortal limits and foibles.
3.  Emergent systems can’t make the bricks that built them stop being brick-ish.
These properties are all intrinsic to a deterministic world, whether chaotic, emergent, predictable, or unpredictable.