4 out of 5
by
Trevor
from
Melbourne, Victoria, Australia | Apr 17, 2009
I liked this very much. The main thesis is that science up to fairly recently has been Platonic (which this book instead, and I think mistakenly, characterises as reductionist) and therefore fixated on describing things and their forms. This idea is that if you have a picture you want to study you will learn all that there is to learn about it by pulling all of the jigsaw pieces apart and studying these individual pieces in detail. As String Theory shows, we can always speculate on smaller and smaller component parts, but it is not clear that gaining a detailed knowledge of all of these parts will inevitably tell us all there is to know about how these parts work in unison.
The author makes it clear that he views that the path of science will be away from what he calls reductionism (and I would call a Platonic obsession with ‘things’) towards a deeper understanding of how these components already more or less described in detail work together in networks of relationships to bring about complex and emergent behaviours and phenomena. I have a fundamental faith that any view that turns our attention away from ‘things’ and towards relationships is pointing us in the right direction.
He uses a very broad palette here to make his point, taking examples from computer science, biology, economics and sociology to build a fascinating case for the role played by networks in assisting our understanding of how the world works. He also makes some fascinating points regarding the development of network theory and how that development has been away from notions of randomness towards much more highly structured and law driven networks.
Sorry, that wasn’t clear. He spends a lot of time in this book discussing in very clear prose the problems which have confronted mathematicians when they have sought to describe networks. The earliest models of networks assumed that the links between nodes on the network were more or less random. What has been found since is that networks follow power laws in which they tend to follow Matthew 13:12 “For whosoever hath, to him shall be given, and he shall have more abundance: but whosoever hath not, from him shall be taken away even that he hath,”
Much of the sociological implications of networks is much the same as is discussed in Malcolm Gladwell’s The Tipping Point. But you still may want to read this even if you have read that book, as this does give much more background to network theory and therefore helps to make more sense of some of the conclusions drawn in Gladwell’s book. Also, the examples drawn from other sciences, not least computer science, gives an interesting insight into the growing importance of network theory in understanding the world.
In a previous life I would have had a better understanding of power series and therefore a deeper understanding of how networks are shown to be less random and more law driven – but in this book such an understanding of mathematics is not assumed nor needed to follow the argument. (Was that a collective sigh of relief I could hear?) At no time did I feel like I was looking down over the abyss of my mathematical ignorance and thinking, “God, if only I’d stuck at it I might even be able to follow what this guy is on about”. He is always clear and makes no assumptions of the reader’s numeracy or intelligence, other than that the reader possessing some threshold level of literacy. And, to be honest, even this wasn’t set too high.
There was also a very interesting discussion and explanation of the Pareto Principle which I think in itself made the book worthwhile. This is the rule that one hears far too often from people who have an Masters of Business Administration (or a masters of bugger all as a friend of mine refers to them). The notion that we get 80% of our sales from 20% of our customers being the MBAs Pareto Relation of choice. He says that this rule is not as all pervasive as MBAs would have us believe. Rather, it only is the case in specific situations and this was the most interesting thing in the book, for me. Generally, we would expect things to be ordered around a normal distribution – with height, for example, there are lots of average height people, but far fewer very tall or very short people. The Pareto Principle instead follows a power rule and, as he points out, applies when a system is moving from randomness to an organised state. I would love to read more about this, but this was the first time I have heard someone talking about this relation and I didn’t think – Well, so what?
What was most interesting about this book, though, was what was not talked about. He talked about computer networks, he talked about the network relationships within plant and animal cells, but what wasn’t mentioned at all throughout the book (and I expected to hear about it at any moment) was a discussion of that most intriguing of networks, the neural networks in the brain. I wonder if this is because how we describe these neural networks is generally with reference to computer, highway or other human made networks and the metaphor doesn’t really work going the other way around.
There is lots to think about in this book – and like I said, given that it moves us some way from Plato’s world of forms towards notions that everything is connected to everything else makes this book worth reading. I think it is clear that these connections, impulses and directings and how they are played out when one set of a web of interactions impacts upon other parts of that web are worth both our notice and our study.
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