Skip to main content

Small World – Mark Buchanan ***

It’s entirely possible for something to be both fascinating and intensely unsatisfying – and that is how I felt about Small World and the topic it covers.
The subject at the book’s heart is ‘small world networks’. This is the idea behind the famous (or infamous) concept of six degrees of separation. Based on an experiment by Stanley Milgram in 1970, the idea is that everyone in the world is connected to everyone else by no more than six links. The original experiment has been criticized for being limited to the US (hardly the whole world) and not taking in enough barriers of language, class and ethnicity – yet even when these are taken into account, there is a surprisingly small number of jumps required to get from most of us to most others.
What’s even more fascinating is that this type of network occurs widely in self-organizing systems, whether it’s the structure of the internet or biological food chains. What tends to crop up are networks where there are local clusters with a few long distance links, which drastically increase the chances of wide ranging connectivity. There isn’t a single style of these small world networks – some, for instance, have vast hubs with many spokes, while others are more democratic. (Interestingly, the internet, which was supposed to be democratic to avoid losing connectivity, as it was originally a military network that had to survive attack, has gone entirely the other way with huge hubs.)
What strikes me is the vagueness of it all. There seems to be an imprecision that’s most unusual for a mathematical discipline. This could be down to the way Buchanan is presenting things of course – his style is very readable but this does sometimes (not always!) bring a degree of smoothing over. Just as an example, we are told about Erdös in 1959 solving the puzzle of how many roads are required, placed randomly, to join 50 towns. Buchanan tells us ‘It turns out, the random placement of about 98 roads is adequate to ensure that the great majority of towns are linked.’ I’m sorry? What does about 98 mean? How about ensuring the vast majority are linked? That’s small consolation if you live in one of the towns that is isolated.
The other vagueness, in the ‘six degrees of separation’ model is what we really count as an acquaintance. It’s such a fuzzy concept, it’s hard to see just how it can be made to operate with the precision required by mathematics. I have nearly 1,000 people in my email address book. Are they all acquaintances? How about those lovely people on the Nature Network with whom I often exchange comments about blog entries, but none of whom have I ever met or spoken to, and only two have I ever emailed? For that matter, what about my ‘harvest’ emails? Is somebody an acquaintance because I’ve seen their email address? Probably not. How about when someone sends me an email and copies in lots of other people. Are those email addresses part of my contact circle? I don’t know – and I doubt if the people who play around with this interesting, but in some senses rather futile feeling, research do either.
Both these examples relate to why there’s an underlying lack of satisfaction. Like chaos theory, this is a concept where initially you feel ‘wow, this should give amazing insights’ because it’s so fascinating, but then it doesn’t. I’m reminded of Rutherford’s famous remark ‘All science is either physics or stamp collecting.’ Dare I say it – this feels a bit like stamp collecting.

Paperback:  
Using these links earns us commission at no cost to you
Review by Brian Clegg

Comments

Popular posts from this blog

David Spiegelhalter Five Way interview

Professor Sir David Spiegelhalter FRS OBE is Emeritus Professor of Statistics in the Centre for Mathematical Sciences at the University of Cambridge. He was previously Chair of the Winton Centre for Risk and Evidence Communication and has presented the BBC4 documentaries Tails you Win: the Science of Chance, the award-winning Climate Change by Numbers. His bestselling book, The Art of Statistics , was published in March 2019. He was knighted in 2014 for services to medical statistics, was President of the Royal Statistical Society (2017-2018), and became a Non-Executive Director of the UK Statistics Authority in 2020. His latest book is The Art of Uncertainty . Why probability? because I have been fascinated by the idea of probability, and what it might be, for over 50 years. Why is the ‘P’ word missing from the title? That's a good question.  Partly so as not to make it sound like a technical book, but also because I did not want to give the impression that it was yet another book

Vector - Robyn Arianrhod ****

This is a remarkable book for the right audience (more on that in a moment), but one that's hard to classify. It's part history of science/maths, part popular maths and even has a smidgen of textbook about it, as it has more full-on mathematical content that a typical title for the general public usually has. What Robyn Arianrhod does in painstaking detail is to record the development of the concept of vectors, vector calculus and their big cousin tensors. These are mathematical tools that would become crucial for physics, not to mention more recently, for example, in the more exotic aspects of computing. Let's get the audience thing out of the way. Early on in the book we get a sentence beginning ‘You likely first learned integral calculus by…’ The assumption is very much that the reader already knows the basics of maths at least to A-level (level to start an undergraduate degree in a 'hard' science or maths) and has no problem with practical use of calculus. Altho

Everything is Predictable - Tom Chivers *****

There's a stereotype of computer users: Mac users are creative and cool, while PC users are businesslike and unimaginative. Less well-known is that the world of statistics has an equivalent division. Bayesians are the Mac users of the stats world, where frequentists are the PC people. This book sets out to show why Bayesians are not just cool, but also mostly right. Tom Chivers does an excellent job of giving us some historical background, then dives into two key aspects of the use of statistics. These are in science, where the standard approach is frequentist and Bayes only creeps into a few specific applications, such as the accuracy of medical tests, and in decision theory where Bayes is dominant. If this all sounds very dry and unexciting, it's quite the reverse. I admit, I love probability and statistics, and I am something of a closet Bayesian*), but Chivers' light and entertaining style means that what could have been the mathematical equivalent of debating angels on