Chaotic versus random

From John D. Barrow’s chapter in Design and Disorder:

The standard folklore about chaotic systems is that they are unpredictable. They lead to out-of-control dinosaur parks and out-of-work meteorologists. …

Classical … chaotic systems are not in any sense intrinsically random or unpredictable. They merely possess extreme sensitivity to ignorance. Any initial uncertainty in our knowledge of a chaotic system’s state is rapidly amplified in time.

… although they become unpredictable when you try to determine the future from a particular uncertain starting value, there may be a particular stable statistical spread of outcomes after a long time, regardless of how you started out.

Emphasis added.

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Eat, drink, and be merry

Almost every bit of health advice I’ve heard has been contradicted. Should you eat more carbs or fewer carbs? More fat or less fat? Take vitamin supplements or not? It reminds me of this clip from Sleeper in which Woody Allen wakes up after 200 years of suspended animation.

Offhand I can only think of a couple things on which there seems to be near unanimous agreement: smoking is bad for you, and moderate exercise is good for you.

Here are a couple suggestions for evaluating health studies.

Be suspicious of linear extrapolation. It does not follow that because moderate exercise is good for you, extreme exercise is extremely good for you. Nor does it follow that because extreme alcohol consumption is harmful, moderate alcohol consumption is moderately harmful.

Start from a default assumption that something natural or traditional is probably OK. This should not be dogmatic, only a starting point. In statistical terms, it’s a prior distribution informed by historical experience. The more a claim is at odds with nature and tradition, the more evidence it requires. If someone says fresh fruit is bad for you, for example, they need to present more evidence than someone who says an newly synthesized chemical compound is harmful. Extraordinary claims require extraordinary evidence.

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Approximating galaxies as spheres

A couple days ago I compared different ways of approximating Earth and other ellipsoids by spheres. The earth is so nearly spherical that the difference in the approximations would only matter when you need fairly high accuracy. Elliptical galaxies, however, can be much more eccentric than Earth and so the difference in approximation approaches can matter more.

The Hubble classification of elliptical galaxies uses a scale E0 through E7 where the number following ‘E’ is 10(1 – b/a) where a is the major semi-axis and b is the minor semi-axis. An E0 galaxy is essentially spherical. The most common classification is near E3. The limit is believed to be around E7.

Hubble photo of galaxy M49

The image above is a photo of Messier 49, an E4 galaxy, taken by the Hubble telescope.

For an E3 galaxy, the minor and major axes are around 7 and 10 in some unit. The average of these is 8.5. A sphere with the same volume would have radius 8.88 and a sphere with the same surface area would have radius 8.98, about 5.7% larger than the average of the axes.

For an E7 galaxy, the minor and major axes would have a ratio of 3 to 10. This gives an average of 6.5. Matching volumes gives a radius of 6.69 and matching surface area gives a ratio of 7.67, about 18% larger than the average of the axes.

It’s not what you cover

Walter Lewin on teaching physics:

What counts, I found, is not what you cover but what you uncover. Covering subjects in a class can be a boring exercise, and students feel it. Uncovering the laws of physics and making them see through the equations, on the other hand, demonstrates the process of discovery, with all its newness and excitement, and students love being part of it.

From For the Love of Physics

Irreproducible research on 60 Minutes

If your research cannot be reproduced, you might end up on 60 Minutes. Two days ago the new show ran a story about irreproducible research at Duke. You can find the video clip here.

I believe the 60 Minutes piece was somewhat misleading. It focused on data manipulation and implied that the controversial results followed from the manipulated data. As Keith Baggerly explains here, that is not the case. The conclusions do not follow from the (erroneous) data. The analysis itself was irreproducible. That discovery started the whole saga.

Update: Here’s some footage that 60 Minutes recorded but did not include on Sunday. “The systems we have in academia, especially with something this complicated, shield sloppy science and fraud.”

Update: Guess someone took that video down. Sorry.

More posts on reproducibility

Hard science, soft science, hardware, software

The hard sciences—physics, chemistry, astronomy, etc.—boasted remarkable achievements in the 20th century. The credibility and prestige of all science went up as a result. Academic disciplines outside the sciences rushed to append “science” to their names to share in the glory.

Science has an image of infallibility based on the success of the hard sciences. When someone says “You can’t argue with science,” I’d rather they said “It’s difficult to argue with hard science.”

The soft sciences get things wrong more often. Sciences such as biology and epidemiology — soft compared to physics, but hard compared to sociology — often get things wrong. In softer sciences, research results might be not even wrong.

I’m not saying that the softer sciences are not valuable; they certainly are. Nor am I saying they’re easier; in some sense they’re harder than the so-called hard sciences. The soft sciences are hard in the sense of being difficult, but not hard in the sense of studying indisputably measurable effects and making sharp quantitative predictions. I am saying that the soft sciences do not deserve the presumption of certainty they enjoy by association with the hard sciences.

There’s a similar phenomena in computing. Computing hardware has made astonishing progress. Software has not, but it enjoys some perception of progress by association. Software development has improved over the last 60 years, but has made nowhere near the progress of hardware (with a few exceptions). Software development has gotten easier more than it has gotten better. (Old tasks have gotten easier to do, but software is expected to do new things, so it’s debatable whether all told software development has gotten easier or harder.)

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Einstein on radio

From Albert Einstein’s address to the Seventh German Radio Exhibition at Berlin (1930):

One ought to be ashamed to make use of the wonders of science embodied in a radio set, the while appreciating them as little as a cow appreciates the botanic marvels in the plants she munches.

Source: The Science of Radio by Paul Nahin, first edition

Feynman on imagining electromagnetic waves

Richard Feynman on imagining electromagnetic waves:

I’ll tell you what I see. I see some kind of vague showy, wiggling lines  — here and there an E and a B written on them somehow, and perhaps some of the lines have arrows on them — an arrow here or there which disappears when I look too closely at it. When I talk about the fields swishing through space, I have a terrible confusion between the symbols I use to describe the objects and the objects themselves. I cannot really make a picture that is even nearly like the true waves. So if you have difficulty making such a picture, you should not be worried that your difficulty is unusual.

From The Feynman Lectures on Physics, volume II.

Other Feynman posts

Grokking electricity

After I finished an electromagnetism course in college, I said that one day I’d go back and really understand the subject. Now I’m starting to do that. I want to understand theory and practical applications, from Maxwell’s equations to Radio Shack.

I’m starting by reading the Feynman lectures on E&M. After that I plan to read something on electronics. If you have resources you recommend, please let me know.

I’ve started new Twitter account, @GrokEM. I figure that tweeting about E&M will help me stick to my goal. My other Twitter accounts post on a regular schedule (plus a few extras) and are scheduled weeks in advance. GrokEM will be more erratic, at least for now. (In case you’re not familiar with grok, it’s a slang for knowing something thoroughly and intuitively.)

[Update: GrokEM has become ScienceTip and is about more than E&M.]

Here’s what Feynman said about mathematicians learning physics, particularly E&M.

Mathematicians, or people who have very mathematical minds, are often led astray when “studying” physics because they loose sight of the physics. They say: “Look, these differential equations — the Maxwell equations — are all there is to electrodynamics … if I understand them mathematically inside out, I will understand the physics inside out.” Only it doesn’t work that way. … They fail because the actual physical situations in the real world are so complicated that it is necessary to have a much broader understanding of the equations. … A physical understanding is a completely unmathematical, imprecise, and inexact thing, but absolutely necessary for a physicist.

Heinlein coined grok around the same time that Feynman made the above remarks. Otherwise, Feynman might have said that only studying differential equations is not the way to grok electrodynamics.

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