Insect swarm intelligence is impressive not because of what it achieves in an absolute sense, but because the building blocks are pre-programmed automatons with little more than simple firmware agency for behaviors like pheromone trail-following.

Innovation is a response to an ecological condition. A hypothesis about an environment. When a design is not in conversation with its environment, it dies. There’s no such thing as advantageous in a general sense. It’s advantageous in the circumstances you’re living in.
So, to survive, you might say a design has to discover an evolutionary path from the familiar to the new, from the present to the future, through a series of steps into the adjacent possible.

According to [Stafford] Beer, biological systems can solve these problems that are beyond our cognitive capacity. They can adapt to unforeseeable fluctuations and changes. The pond survives. Our bodies maintain our temperatures close to constant whatever we eat, whatever we do, in all sorts of physical environments. It seems more than likely that if we were given conscious control over all the parameters that bear on our internal milieu, our cognitive abilities would not prove equal to the task of maintaining our essential variables within bounds and we would quickly die. This, then, is the sense in which Beer thought that ecosystems are smarter than we are—not in their representational cognitive abilities, which one might think are nonexistent, but in their performative ability to solve problems that exceed our cognitive ones. (Pickering, 2010. The Cybernetic Brain)

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Evolution is the most decentralized thing that you can imagine. It is something that runs itself and is self-organizing at every level and at every scale. (Stewart Brand)

Perhaps there is a rule of thumb here? If you decentralize, the system will recentralize, but one layer up. Something new will be enabled by decentralization. That sounds like evolution through layering, like upward-spiraling complexity. That sounds like progress to me.

Networks also have a time dimension, and the shape of the network changes as it ages. Evolving networks exist in punctuated equilibrium, repeatedly evolving through distinct phases of randomness, growth, consolidation, and collapse.

(Phase 1) Random: The system is unstructured. Random events occur without particularly changing the structure.

(Phase 2) Growth: An innovation causes a major phase transition within the structure of the system. The innovation catalyzes other innovations in a positive feedback loop.

(Phase 3) Consolidation: Growth rates saturate. The ecosystem consolidates into a highly organized network, optimized for efficiency, as each agent seeks to eke out as much as it can from its position in the value chain. Hubs (keystone species) appear at critical points.

(Phase 4) Collapse: A random shock, or new innovation demolishes one of the keystone species, causing cascade failure within the highly structured network. The ecosystem collapses into a random...