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I really enjoyed this whole post by Mark Burgess, founder of CFEngine, on the difference between the centralized brain model and the decentralized society model. It articulates a lot of core ideas about complexity theory and emergence by offering the simple brain/society analogy. As the article explains:
A brain model is a signalling model. Signals are transmitted from a common command-and-control centre out to the provinces of an organism, and messages are returned to allow feedback. ... The alternative to a brain model is what I'll call a society model. ... There can be cooperation between the parts (often called institutions or departments, communities or towns, depending on whether the clustering is logical or geographical).
As he concludes, "Societies scale better than brain models, because they can form local cells that interact weakly at the edges, trading or exchanging information. If one connection fails, it does not necessarily become cut off from the rest, and it has sufficient autonomy to reconfigure and adapt."
With all that said, though, what I found most interesting was this point about why big organizations get slower:
Our ability to form and maintain relationships (knowledge) with remote parts depends on them being local. Long distance relationships don't work as well as short distance ones! Certainly, this depends on the speed of responses. If messages take longer to send and receive, then an organism can react more slowly, so scaling up size means scaling down speed, and vice versa. This certainly fits with our knowledge of the animal kingdom (another centralized management expression!). Large animals like whales and elephants are slower than smaller creatures like insects. The speed of impulses in our bodies is some six orders of magnitude slower than the speed of light, so we could build a very big whale using photonic signalling.
It's a simple but vivid explanation. The answer, which many have realized, is to attempt to solve this by moving your organizational model closer to that of a society, where decisions can be made independently at the edges. Of course, as the metaphor illustrates, we're intimately familiar with the centralized (brain) model, so it's easier said than done.