There are two extraordinary science stories that I stumbled upon today. In both cases, a rigorous analysis on data gathered on a global scale reaches conclusions on hotly debated topics. These topics are hotly debated because they are notoriously difficult to comprehend, they affect our personal lives and therefore people develop strong opinions about them based on mostly incomplete pictures. Behold the topics of climate change and economy.
Climate change has been a controversial issue. There are two categories of people who don’t buy climate change: the skeptics and the deniers. The difference between the two is very well explained in Phil Plait’s post about how people and scientists use the same words (and how that leads to grandiose confusion), but also in the Guardian article I want to discuss. Phil already took a crack at the story, and I don’t have much to add, except that I find it staggering how good science is at backing up claims that are true.
The reason is simple. Science doesn’t choose a priori the claims that it is going to back up. Of course, all scientific investigation begin with something that needs testing: an hypothesis. The hypothesis allows you to make a prediction (if I suppose this, then, given the mechanics of the system I study, I should get that). Then, you go look at the data to see if the prediction is realized. If it is not, you have ruled out the hypothesis. If it is, you have added credibility to the hypothesis. You haven’t established that it is true. What you have established is that at the level of precision you looked at, you don’t see any disagreement between your hypothesis and reality. If you worry that this imply that you can never be 100% sure of anything in science, you are exactly right. But then, that is true of everything else too. A scientific investigation is the only way to gauge your level of certainty for any claim.
In the case of the the global warming story, there were several starting hypotheses. Those were the claims of climate change skeptics such as this. The rise in temperature in highly urbanized areas could skew the measurements of a global trend in temperature, since highly urbanized areas trap a lot of heat. More urbanisation would amount to warmer temperatures being measured, since temperature records exist mostly for towns where urbanisation has been expanding in the past century.
Such a claim can be tested in several ways, but the starting point is to get good temperature records. More importantly, for the results to have maximal credibility, everything must be open. Hence the Berkeley Earth Surface Temperature project, a compendium of more than a billion temperature records across the globe going as far back as 1800. With such a dataset, you can account correctly for the warming contribution of urbanized areas. The effect of urbanization on a global scale has been found to be negligible, and it is not the first study to reach that conclusion. Since warming is still observed, something else than urbanization must be responsible. However, the beauty of this study is, you can take it apart yourself if you have the patience to (and you can bet some people are already on it).
What the Berkeley Earth project is doing is that they are systematically going over legitimate concerns about the validity of climate change. So far, they have found that none of these concerns invalidate global warming. These results won’t shut up the climate change deniers, but these people won’t listen to anything besides their own ideology. They can accuse the people behind the Berkeley Earth project of having an agenda but they can also have a look at their methods and replicate their results. Data and methods don’t have agendas.
The other story is also quite fascinating and very timely, given the current Occupy movement. I hate discussing capitalism with anyone because you either find people who will despise the system no matter what, or people who will defend it at any cost. Nobody bothers to define contemporary capitalism. I think this is something we need to define very carefully since the market is never exactly free. There are always government regulations in place, and the strength of these regulations vary from country to country. I think to be correct, we should talk of categories like Canadian capitalism, American capitalism, French capitalism, global capitalism etc. There are degrees of capitalism, and it seems to me that some degrees are bad, and some are good. How do you quantify if capitalism has gone too far?
Complex system theorists from the Swiss Federal Institute of Technology in Zurich have pioneered a method to quantify just that. By looking at direct ownership relationships between 43060 transnational corporations, you can get a basic idea of the distribution of economic power among these corporations. Note that banks are included in the dataset.
The authors of the study don’t say it exactly like that in the New Scientist article, but it looks like wealth and influence among corporations is distributed like a power law. What this means is that you got a pattern like 10% of the corporations control 90% of the wealth. It is exactly like the distribution of wealth among citizens in a country. The more wealthy they are, the fewer they get. What makes this a power law is that it is scale independent. If you look in that 10% that control the 90% of the wealth, you will find that 10% of the 10% control 90% of the 90%.
What this means is that wealth is indeed concentrated among a few major players. The exact numbers quoted are that 147 corporations controls 40% of the wealth in the network of 40060 corporations. This is less than 1% of them. It is expected that the distribution of wealth follows a power law (so many things in nature and in our society do).
What is surprising is how interconnected that top 1% is. What opens up in this dataset is the possibility to study how the troubles of one major corporation will propagate through the network, and affect the global economy. The fact that the top 1% is so interconnected could potentially lead to unexpectedly far-reaching propagation of local economic instabilities. In any case, this study opens the door to a new world of economic simulations. These simulations have the potential to tell us what measures can be implemented to prevent the propagation of a local economic mistake.
A single corporation like Goldman Sachs shouldn’t be able to wreck the entire American economy, or affect economy on a global scale. The policies that may come out of work like this may go against the ideology of pro-capitalists or even against the ideology of anti-capitalists. At this point, who knows? It is too early to tell. This is the beauty of science. It goes beyond our opinions when it is well done. It can tell us what is true, and what works, irrespective of one’s ideology. As Feynman said:
Science is a way of trying not to fool yourself. The first principle is that you must not fool yourself, and you are the easiest person to fool.