Geek fatigue

I heard a great term the other day: geek fatigue. Being a geek often means doing things the hard way, at least in the short term. There’s usually some long-term advantage, real or imagined, to justify doing things the hard way. But even a die-hard geek gets tired and wants to take the easy way out.

Thomas Gideon — a self-described “die-hard technology geek” — used the term geek fatigue on his podcast to describe why he bought a Mac a few years ago. He was tired of using Linux and fighting driver issues. (Thomas has recently decided to move back to Linux.)

If geek fatigue is exhaustion from doing things the hard way, there needs to be a corresponding term for the relief that comes from joining the mainstream. Any suggestions?

Sometimes the geek approach is just extra work. There’s no advantage other than the personal satisfaction of doing something within self-imposed limitations. But sometimes the geek approach pays off, especially in the longer term. What has your experience been?

Four out of five dentists surveyed

Years ago, Dentyne chewing gum ran an advertising campaign with the line “four out of five dentists surveyed recommend sugarless gum for their patients who chew gum.” Of course there’s no mention of sample size. Maybe “four out of five” meant 80% of a large survey, or maybe they literally surveyed five dentists.

Even if they only talked to five dentists, you’d think that if four dentists out of five came to the same conclusion, it is quite likely that they have good advice. Individuals have their biases, but if a large majority comes to the same conclusion independently, maybe some underlying truth is responsible for the consensus rather than a coincidence of prejudices.

However, there is a fallacy in the preceding argument. It implicitly assumes that professionals make up their minds independently and that their prejudices are independent. That may be true on some small objective problem. Several scientists may conduct independent experiments and have independent errors. In that case, if most agree on a measurement, that measurement is likely to be accurate. But ask a group of scientists working in the same area if their area deserves more funding. Of course they’ll agree. Their financial interests are highly correlated.

James Surowiecki’s book The Wisdom of Crowds argues that crowds can be amazingly intelligent. Crowds can also be incredibly foolish. One of the necessary conditions for crowd wisdom is independence. The book gives examples of experiments in which the average independent estimates, such as the weight of a cow or the number of jelly beans in a jar, surprisingly accurate. But if there were an open debate rather than an anonymous poll, the estimates would no longer be independent.  If one influential persons offers a guess, other estimates will be anchored by that guess and tend to confirm it.

William Briggs has an excellent article this morning on scientific consensus. The context of his article is climate change, though I don’t want to open a debate here on climate change. For that matter, I don’t want to open a debate on the merits of sugarless chewing gum. I’m more interested in what the article says about how a consensus becomes self-reinforcing.

Endless preparation

In his book Made by Hand, Mark Frauenfelder quotes Peter Gray on what’s wrong with contemporary education. Gray says that school is about

always preparing for some future time when you will know enough to actually do something, instead of doing things now. And that’s such a tedious approach for anybody to take to life—always preparing.

Related post: “Just in case” versus “just in time”

Volumes of Lp unit balls

The unit ball in n dimensions under the Lp norm has volume

2^n \frac{\Gamma\left(1 + \frac{1}{p}\right)^n}{\Gamma\left( 1 + \frac{n}{p} \right)}

I ran across this formula via A nice formula for the volume of an L_p ball. That post gives an even more general result that allows different values of p along each axis.

There have been several blog posts lately on the volume of balls in higher dimensions that correspond to the case p = 2. The formula above is valid for all p > 0.

Note that as p goes to ∞ the volume goes to 2n because the terms involving gamma functions go to 1. This is as we’d expect since the unit “ball” in the infinity norm is a cube, two units wide on each side.

Related postMeans and inequalities

SciPy and NumPy for .NET

Travis Oliphant announced this morning at the SciPy 2010 conference that Microsoft is partnering with Enthought to produce a version of NumPy and SciPy for .NET. NumPy and SciPy are Python libraries for scientific computing. Oliphant is the president of Enthought and the original developer of NumPy.

It is possible to call NumPy and SciPy from IronPython now by using IronClad. However, going through IronClad can be inefficient.  The new libraries will enable efficient access to NumPy and SciPy from .NET languages and in particular from IronPython.

Here is the official press release from Enthought. [Update: press release no longer available.]