Black Swans

by Russ Roberts on April 30, 2007

The latest EconTalk is a conversation with Nassim Nicholas Taleb about the ideas in his two books, Fooled by Randomness and The Black Swan. He is a very interesting guy. Much of what he said is well known—we have trouble absorbing and thinking about low probability events. But he says it in a way that makes me think about it in a way I hadn’t before.

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Pasha

Good interview - he's much more palatable in person than in his books, which came off a bit heavy-handed for me.

Here's my question: why do so few talk about lognormal distributions with regard to issues like income or wealth? I believe that he (and/or mandelbrot) also implicate the power law distribution, which is similar except at the low end.

I'm not one for mathematical reductionism, but I'm not ready to throw out the practice of statistics either, just because we humans fall prone to cognitive errors.

Any of these log-based models would easily encompass high-tailed incomes, while recognizing the great preponderance of middle and lower incomes. Lognormal is commonly used in finance, for example, to represent asset prices - you can't go below zero because of bankruptcy. People are the same.

Ray G

I've only skimmed his newest book, but I like it so far. He uses Umberto Eco's large private library as an example from which he pulls the little gem that an unread book is far more valuable than the book one has already read.

Speedmaster

Fascinating discussion! I loved the concrete examples that the two of you discussed. But I must admit that much of the discussion was over my head. Mr. Taleb is clearly brilliant.

Russ Roberts

Speedmaster,

Read the books. They're very accessible. Try Fooled by Randomness first. If you like it, read The Black Swan. They're both listed in the right-hand margin.

Speedmaster

More thoughts on this. Mr. Taleb points out the problems associated with building models. What does this mean for all we read about global warming predictions that warn us of doom 100 years out?

Mesa EconoGuy

Having built several (pseudo) predictive statistical models myself, it probably means that these global warmers have misidentified several inputs and/or their relative weightings, interactions, and other anomalies, and have most likely failed to account for numerous other variables and their relative climatological importance.

In short, they’re looking at a very narrow set of conditions/interactions, and fudging the input numbers (especially for CO2). Don’t believe most of what they say.

I haven’t read Taleb’s books, yet. They’re next up.