Networks and Biology: Wiring ourselves into a bad theory
The one thing that can be said about networks is that they are easy to draw. Anyone who’s done “join the dots”, or who has looked at a map, or studied physiology or neuroanatomy understands networks in their essence: a set of points joined together with lines. The join-the-dots pattern permeates the natural world like a kind of fractal motif. But what we see and what things actually are, are not the same. How would we know if networks actually exist?
In order to know whether a network is real, we would have to be able to establish some kind of correlation between our observations of the network’s structure (which is “the network”), its behaviour, and any changes we might make to that structure. Obviously, if the network is human-made, then the relationship between an electronic network’s structure, how it behaves, and predictable outcomes in the light of changes to it would seem to be straight-forward. But in complex artificial networks, such as those defined by machine learning models, predictability in the light of network change is elusive. We are strangely unbothered by this, because we see the same type of unpredictability in natural networks.
continues in source:Improvisation Blog: Networks and Biology: Wiring ourselves into a bad theory