The effects of parents choosing not to have their children vaccinated has been in the news again recently, after an outbreak of measles at Disneyland, apparently transmitted by an unvaccinated woman. This sort of thing seems to keep coming up. For a deep and personal reflection on the issue, have a look at this blog post. For me this paragraph is key:
What happened was that some parents decided not to immunize their children. As it is extremely contagious, measles does not need much of an opportunity to regain a foothold. That opportunity was provided by a false belief in some parents that immunization was unnecessary or even harmful. Parents who were too young to have experienced the disease became more fearful of the vaccine than the disease and their unvaccinated children became innocent victims.
It seems that sometimes we solve problems so well, we stop believing that they need to be solved.
Every generation want to think for themselves, and we’re not very good at reasoning about things we have no experience of, no matter what our grandparents tell us. This generational independence is good for freeing us from unnecessary traditions and the poor decisions of the past, but depressingly, it also seems to condemn us to repeating history.
As a physicist, I hope that we will be able to develop a general mathematical theory of complex systems. But complex systems don’t fit into traditional statistical physics theory. They are not in equilibrium, and are full of correlations and strong interactions. Complex systems can increase their complexity spontaneously over time. (Darwin’s theory of evolution is an explanation of one way that this can happen, for example.) This increase could be thought of as an increase in information content, since the more complex a system is, the more bits of information are needed to describe it. This is measured by Shannon entropy. But information theory doesn’t tell us how to relate this to other important quantities like energy. For that we should have been talking about thermodynamic entropy instead. Chris Adami has an interesting discussion on his blog about information entropy, thermodynamic entropy and how they’re related.
Nate Silver’s predictions for the US elections, on his fivethirtyeight blog, have received a lot of attention since the actual election results agreed extremely well with his predictions. Silver’s results have been held up as an example of the power of numbers as opposed to, for example pundit opinions, and rightly so. Silver’s results shouldn’t be a surprise. Much of the hard work was already done by the pollsters, and the aggregated predictions published by Silver and others mainly serve to underline the power of polling. Even much simpler analysis, such as a simple five-poll average gave very accurate results.
While they are not really the same thing, his analysis has something in common with what we try to do in Complex Systems: good data combined with reasonable assumptions and a careful application of statistical principles can give simple, but powerful, results.
On the topic of pundits, the vindification of numerical analysis, and the wildly innacurate predictions of some pundits might suggest that, come the next election, numerical analysis will supplant those pundits. But I doubt this will happen. This conclusion misunderstands the role of TV pundits. Their main role is not to give accurate predictions. Their task is to make TV interesting, by providing lively discussion, and to give arguments in support of a particular point of view. In fact, it might even be better for them to not give accurate predictions: if every network lined up pundits all repeating more or less the same careful estimates, there wouldn’t be as much entertainment value.
I’ve just returned from a week in Chicago. Well, technically Evanston, but Chicago looms large on the horizon. I was at NetSci’12, the biggest network science conference of the year, held at Northwestern University. NetSci is always a highlight for me. Representing things with networks can be an extremely powerful tool, and once you start to think about it, almost any complex system has a networky element to it. This means that NetSci has a very broad scope, and I heard fascinating talks on topics such as economics, epidemics, social networks, mobility, communications, ecology, gene interactions, …the list goes on and on. Another great thing about NetSci is that the first two days are dedicated to satellite symposia. These are independently organised workshops on more focussed topics. This year I went to LangNetSci, about all things networky related to language, and Netonets, about networks of networks and their importance to infrastructure and systemic risk. Both of these workshops were well worth the time, and there was hearty discussion and a real sense of collegiality in both, which continued through the main conference.
Paul Callaghan, one of New Zealand’s leading scientists, makes a forceful and compelling case for why the obvious avenues to economic growth won’t work, and why New Zealand should instead aim to grow its economy through niche, high tech businesses. Investment in research and development is one important ingredient in achieving this end. Well worth the 20 minutes viewing time.
I’ve just been home to New Zealand for a holiday. Choosing such a specific job has taken me literally to the opposite side of the planet.
Every scientist is a specialist in a very particular area, and this means there are very few work opportunities anywhere in the world. This often means they move far from home and the next job might not be in the same country or even the same continent.
This is of course a fantastic opportunity. I get to do a job I love, while living in an exotic country, learning about a new culture and language and working with people from all over the world. But of course there are difficulties too. Living far from home means being far from family and friends, and the comfort and enjoyment of living in a familiar culture and landscape. Visiting home reminds me of all the things that I miss.
This is not the first time I’ve lived ‘overseas’ and it might not be the last. Having made friends in several countries, wherever I go I’ll be far from some of them. It’s very easy to travel these days; the world has gotten a lot smaller, but it still feels pretty big sometimes.
Our ability to learn language never ceases to amaze me. We can not only communicate, we use language in amazingly subtle and nuanced ways. One of the clever things our brains are able to do is to tailor our language to the person we are speaking to. I’ve heard someone switching accents from sentence to sentence as she took part in a conversation involving people from her native country and her adopted one. When I write, I write differently for scientific articles, business emails, letters to friends and so on. I sometimes use particular slang and in-jokes with my friends that I don’t use with strangers.
This all seems to happen effortlessly and unconsciously, but it isn’t perfect. It sometimes breaks down. Language we use a lot in one situation can become such a habit that it bleeds over into other situations. Changes in language use that are conscious at first become automatic after a while. When I lived in England, I intentionally modified some of my strange New Zealand pronunciation and vocabulary so that people there could understand me better. (The way we say “ten” for example, is almost unintelligible to English people.) When I went back to New Zealand, I found some of these changes had stuck, and I couldn’t remember how to say some things in ‘New Zild’, while at other times some English-isms just slipped out by accident. This effect, where a conscious ‘accommodation’ of our language becomes a habit is a possible factor in language change. If people start to modify their language in some conscious way for whatever reason — be it playfulness, playing up to higher status people, purposefully exaggerating differences to reinforce their identity — this can become a habit and spread through all their language use, becoming their new ‘normal’ way to say something.
This kind of habit forming can also cause problems. In science (I suppose in any specialised job) we use a lot of jargon to make our communication more concise and precise. The problem is that we use the technical jargon so much, that for many scientists it becomes hard to describe their work without using these technical words. Sometimes the words we use in a technical way are quite common words which have quite different meanings in everyday life. This can lead to big misunderstandings about what a scientist really means when she or he is speaking to the public. One topical and unfortunate example is the way discussion about climate change can be heavily distorted simply by the words that are used. Callan Bentley has a table of such dangerous words in his blog, along with some less fraught alternatives.