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Author: Briggs

July 4, 2008 | 9 Comments

At least they’re admitting it

Here’s the problem. You are a scientist, working on measuring the levels of aragonite in ocean water. It’s not very sexy and nobody beyond a small cadre seems to care. But it’s grant time and you and your team are “figuring out how to make the issue more potent” so that you can bring in the bucks.

How do you do it?

The first thing you should immediately consider these days is “turning up the heat on the issue through the media.” However, convening a press conference on “The Importance of Aragonite in Ocean Water” is unlikely to interest even the New York Times.

You need to be clever. Your job in “expanding awareness” has to start with a snappier moniker. You need a term that is “easy to comprehend” and, if you’re lucky, sounds “alarming.”

Renaming is thus “a critical step.”

So you ponder. Then you recall that aragonite levels are related to the amount of diffused carbon dioxide in ocean water. Some chemistry helps: when CO2 dissolves in water it lowers that water’s pH. And what is lowering pH sometimes called? Acidification!

Success! Not only is this a fantastically frightening term, it drives “home the idea that carbon dioxide [i]s a pollutant.”

Your next step is to find a PR firm whose specialty is to “link researchers with policy-makers and the media.” The good news is that there are no shortage of such places.

Of course, you have to be honest about “the” science and the uncertainties (as you understand them). But if you’re lucky, even the possibility, no matter how small, of risk will be enough to frighten Congress into action.

I think we can agree “the acidification story provides a model of how to get science on the congressional agenda.”

A fuller account of this fascinating and inspirational story may be found here (Nature magazine, again leading the way).

July 2, 2008 | 21 Comments

Lizards all male climate change club

Nature magazine reports this headline: Condemned to single-sex life by climate change.

They are talking about a species of lizards called tuatara that live “on about 30 small islands in New Zealand?s north.” The disgusting, scaly creatures are in exile on those islands because they have everywhere else been “wiped out by predators.” No word on who or what these predators are or why the predators cannot follow the tuatara to the islands and thus continue their campaign of herpetological genocide.

Anyway, the lizards are about to go extinct and it’s all your fault. It seems that when the weather is hot, more male tuatara lizards are born than female lizards. And we all know what happens when there are more boy than girl lizards. It becomes impossible to get a date and procreate.

This “doomsday prediction”, we are told by researchers, is assured because of (what else?) global warming.

How do the researchers know this? Why, a computer told them so.

Previous computers did not tell them so, which forced the researchers to reprogram them, this time incorporating in their models “physics of heat transfer with terrain data.” Well, that is impressive. The researchers then “simulated climate change and then monitored its effect on specific points across the island.”

What they found was shocking: Rampant maleness, which naturally carries with it the consequence of enforced bachelorhood.

For those of you who are not as computer savvy as I, let me summarize. Researchers programmed a computer to show that when the temperature rises, fewer female lizards are born. They then told the computer that temperatures were in fact rising. The computer then said “fewer female lizards are born.”

The researchers pored over this result and came to the conclusion that “warmer temperatures caused by global warming imply fewer female lizards will be born.” They wrote this in a paper which was duly summarized at Nature. Science in action!

All might not be lost because, the researchers suggest, the lizards might be “translocated” ( = moved) to cooler climes. I just hope that those mysterious predators aren’t in the new translocations.

July 1, 2008 | 30 Comments

I wish I was making this up

Martin Creed

Another piece of data is in that shows money does not correlate with intelligence.

“Artist” Martin Creed (pictured above) created a “work” called 850, which he will exhibit at the well-known Tate Britain art gallery starting today.

The “work” consists of having joggers, once every thirty seconds, trot through the museum.

Yes, you read that right. Joggers, wearing shorts and looking like they came from the park, will run lightly through a hall or two in the name of “art.”

Guardian writer Adrian Searle claims that the wonderful thing about this “art” is “that it is gloriously pointless.” It’s not surprising the paper should feel that way, since much of its reporting falls into this category. Searle argues that people should not try to decide whether 850 is “art” but “whether the work captures the imagination, whether it gives pleasure and makes people think.”

So, on this theory, I could put a certain piece of Mr Searle’s anatomy in a vice and start to twist, an act which is certainly imaginative and would give me some pleasure. It would also cause Searle to do some serious thinking. But would he call it art?

People should not feel anger or despair over the sort of idiocy like 850, now common in the “art” world. They should instead view it as a chance to raise their income bracket. Since rich people—those people that run galleries and buy and sell “art”—are now utterly incapable of judging quality, and are dead scared of admitting their ignorance, the door is wide open for any “artist” to sell them anything. The only key seems to be that the “work” has to be completely asinine, childish, devoid of any value, and, of course, politically correct.

It also cannot be cheap. The more exhorbitantly priced your excrescense, the better chance it has to sell. For you must understand that the sole purpose of this “art” is to allow its owner to boast that he owns it. Or, in the case of the Tate, to claim that it is unique.


Wired’s theory: the end of theory

Chris Anderson, over at Wired magazine, has written an article called The End of Theory: The Data Deluge Makes the Scientific Method Obsolete.

Anderson, whose thesis is that we no longer need to think because computers filled with petabytes of data will do that for us, doesn’t appear to be arguing serious—he’s merely jerking people’s chains to see if he can get a rise out of them. It worked in my case.

Most of the paper was written, I am supposing, with the assistance of Google’s PR department. For example:

Google’s founding philosophy is that we don’t know why this page is better than that one: If the statistics of incoming links say it is, that’s good enough. No semantic or causal analysis is required.

He also quotes Peter Norvig, Google’s research director, who said, “All models are wrong, and increasingly you can succeed without them.”


The scientific method is built around testable hypotheses….The models are then tested, and experiments confirm or falsify theoretical models of how the world works…But faced with massive data, this approach to science ? hypothesize, model, test ? is becoming obsolete.

Part of what is wrong with this argument is a simple misconception of what the word “model” means. Google’s use of page links as indicators of popularity is a model. Somebody thought of it, tested it, found it made reasonable predictions (as judged by us visitors who repeatedly return to Google because we find its link suggestions useful), and thus became ensconced as the backbone of its rating model. It did not spring into existence simply by collecting a massive amount of data. A human still had to interact with that data and make sense of it.

Norvig’s statement, which is false, is typical of the sort of hyperbole commonly found among computer scientists. Whatever they are currently working on is just what is needed to save the world. For example, probability theory was relabeled “fuzzy logic” when computer scientists discovered that some things are more certain than others, and nonlinear regression were re-cast as mysterious “neural networks,” which aren’t merely “fit” with data, as happens in statistical models, instead they learn (cue the spooky music).

I will admit, though, that their marketing department is the best among the sciences. “Fuzzy logic” is absolutely a cool sounding name which beats the hell out of anything other fields have come up with. But maybe they do too well because computer scientists often fall into the trap of believing their own press. They seem to believe, along with most civilians, that because a prediction is made by a computer it is somehow better than if some guy made it. They are always forgetting that some guy had to first tell the computer what to say.

Telling the computer what to say, my dear readers, is called—drum roll—modeling. In other words, you cannot mix together data to find unknown relationships without creating some sort of scheme or algorithm, which are just fancy names for models.

Very well—there will always be models and some will be useful. But blind reliance on “sophisticated and powerful” algorithms is certain to lead to trouble. This is because these models are based upon classical statistical methods, like correlation (not always linear), where it is easy to show that it becomes certain to find spurious relationships in data as the size of that data grows. It is also true that the number of these false-signals grow at a fast clip. In other words, the more data you have, the easier it becomes to fool yourself.

Modern statistical methods, no matter how clever the algorithm, will not being salvation either. The simple fact is that increasing the size of the data increases the chance of making a mistake. No matter what, then, a human will always have to judge the result, not only in and of itself, but how it fits in with what is known in other areas.

Incidentally, Anderson begins his article with the hackneyed, and false, paraphrase from George Box “All models are wrong, but some are useful.” It is easy to see that this statement is false. If I give you only this evidence: I will throw a die which has six sides, and just one side labeled ‘6’, the probability I see a ‘6’ is 1/6. That probability is a model of the outcome. Further, it is the correct model.