Yes, he's unfortunately worth reading, for the drama and stimulation and calling attention to things that are known but not always fully appreciated. But I wish he wasn't worth reading.

His hero Mandelbrot on the other hand? A real scholar with radical ideas, not a provocateur.

The important ideas in his book are well known by thinkers, if not all practitioners, in the fields he criticizes. But, oh, the insults! And so much of what he says is "Such and such models have flaws! Throw them away and do nothing at all until you have perfect models!" That's not terribly useful if you are, say, running an insurance firm. Or doing science.

Anyway, here's the issue of American Statistician duly taking him to task on the technicalities, though unfortunately without the bombast and mud-slinging.

http://pubs.amstat.org/toc/tas/61/3

Also could somebody explain to me why hacker types like to name-check Popper but never Kuhn? Is it because the Star Trek TNG episode with the Binars actually got it right?

Kuhn and Popper both make interesting observations about the development of science. However, Popper's observations are more directly applicable to the task of actually doing science. He gives a useful framework for developing and testing theories, where Kuhn's work has more to say about how theories become widely accepted. So Popper appeals to the pragmatism of most hackers.

Kuhn is also, sadly, very popular with crackpots. Almost any fringe scientist will eagerly explain to you that his ideas represent "a new paradigm", and that those who doubt him are simply trapped in old ways of thinking. So a desire to avoid guilt by association probably also plays into it.

That's my take on it anyway. (I know it's kind of a tangent, but it seemed like an interesting thing to think through).

The fact that those familiar with the subject matter do not think this is revolutionary is exactly the problem Taleb is pointing to. He is calling it "the great intellectual fraud" for a reason. Domain experts obviously do not believe "intellectual fraud" and "bell curve" should be in the same sentence

Issuu doesn't have a deep-linking feature.

http://www.cunymath.cuny.edu/images/sidebar/gauss_10_DM.jpg

As Eugene Fama points out on his website... http://www.dimensional.com/famafrench/2009/03/qa-confidence-...

"Half of my 1964 Ph.D. thesis is tests of market efficiency, and the other half is a detailed examination of the distribution of stock returns. Mandelbrot is right. The distribution is fat-tailed relative to the normal distribution. In other words, extreme returns occur much more often than would be expected if returns were normal. There was lots of interest in this issue for about ten years. Then academics lost interest. The reason is that most of what we do in terms of portfolio theory and models of risk and expected return works for Mandelbrot's stable distribution class, as well as for the normal distribution (which is in fact a member of the stable class)."

From the article you linked: "None of this implies, however, that the existence of outliers undermines modern portfolio theory or asset pricing theory."

In fact, that's exactly what it does. This is what happens when you build houses on top of sand.

Taleb pushes for a strategy that consists of buying a lot of very safe assets and blending them with bets on "extreme events" (like buying far out-of-the-money put options). Is that a viable long-term strategy? I have my doubts, since there are no evidence suggesting that 'uncertain' strategies have greater returns that more quantified ones.

Part of the reason they underestimated those risks is that they paid attention only to the middle of the distribution, where things are approximately normal. I wouldn't say the explicit Gaussian-ness of the models was the reason for the trouble, but it's hard to imagine a Gaussian model providing any sort of reasonable risk estimate for the type of thing that we saw happen. It was so far outside the "business as usual" range that no risk estimate based on what was happening on most days would have been legitimate.

Taleb pushes for a strategy that consists of buying a lot of very safe assets and blending them with bets on "extreme events" (like buying far out-of-the-money put options). Is that a viable long-term strategy? I have my doubts, since there are no evidence suggesting that 'uncertain' strategies have greater returns that more quantified ones.

I don't know about this; it's all a question of price. If far out of the money puts are really underpriced compared to how often they "hit", then he could be right. My immediate impression is that the crappy prices you tend to get due to low liquidity in the extreme tails might make a profitable strategy tough to come by.

One could certainly look at the historical data over the past several decades and see whether such a strategy might have been profitable (which wouldn't necessarily tell you whether it will be profitable in the future, but might shed at least some light on the matter), but I don't have options data going back very far, so I'm not the man for the job...

He's telling people that "common knowledge" in a particularly despised sector of our economy is not only wrong, but downright idiotic.

And he's presenting it in a way such that Joe the Plumber can feel like he groks it, even if he doesn't stand a snowball's chance in hell of really understanding what's been happening here.

Not that Taleb's altogether wrong - Eugene Fama may be very aware of the limitations and caveats implicit in risk measurements, but it wasn't Fama that got caught pants down screwing around for billions with an asset class that he didn't understand, was it?

What academics understand about the market often has very little to do with what real traders and banks will do in it. Taleb has valid criticisms against the real players, who were freaking idiots in a lot of ways, but he's presenting them as if they're criticisms against the establishment as a whole, which is a bit unfair, but makes for a good publicity play.

Smart move, if you ask me. Nobody would know or care who the hell he is if he hadn't made such a fuss over this stuff.

This is where the author loses me. Where is the randomness of the sample, when there are two items which are interdependent?

Later, he criticizes standard deviation as applied to stocks and bonds (decidedly non-random data), finding fault with the bell curve, rather than the misapplication.

Does this chapter make any more sense in the context of the entire book?

Given all pairs of people whose join income is $1M, select a pair at random. "All pairs" is an unusual population to select from, but mathematically it's a perfectly valid way to define a random variable.

Is that, then, exactly his point? That a random sample of a group can't be modeled like a group of random samples, yet this is what's being done?

He seemed to be complaining about assuming distributions were normal without checking, a simple mistakes that is warned against in any introductory statistics class.