## Sunday, November 13, 2011

### Vanity Fair bungles probability example

There's an interesting article about Danny Kahneman in Vanity Fair, written by Michael Lewis. Kahneman's book Thinking: Fast And Slow is an interesting review of the state of decision psychology and well worth reading, as it the Vanity Fair article.

But the quiz attached to that article is an example of how not to popularize technical content.

This example, question 2, is wrong:
A team of psychologists performed personality tests on 100 professionals, of which 30 were engineers and 70 were lawyers. Brief descriptions were written for each subject. The following is a sample of one of the resulting descriptions:

Jack is a 45-year-old man. He is married and has four children. He is generally conservative, careful, and ambitious. He shows no interest in political and social issues and spends most of his free time on his many hobbies, which include home carpentry, sailing, and mathematics.
What is the probability that Jack is one of the 30 engineers?

A. 10–40 percent
B. 40–60 percent
C. 60–80 percent
D. 80–100 percent

If you answered anything but A (the correct response being precisely 30 percent), you have fallen victim to the representativeness heuristic again, despite having just read about it.
No. Most people have knowledge beyond what is in the description; so, starting from the appropriate prior probabilities, $p(law) = 0.7$ and $p(eng) = 0.3$, they update them with the fact that engineers like math more than lawyers, $p(math|eng) >> p(math|law)$. For illustration consider

$p(math|eng) = 0.5$; half the engineers have math as a hobby.
$p(math|law) = 0.001$; one in a thousand lawyers has math as a hobby.

Then the posterior probabilities (once the description is known) are given by
$p(eng|math) = \frac{ p(math|eng) \times p(eng)}{p(math)}$
$p(law|math) = \frac{ p(math|law) \times p(law)}{p(math)}$
with $p(math) = p(math|eng) \times p(eng) + p(math|law) \times p(law)$. In other words, with the conditional probabilities above,
$p(eng|math) = 0.995$
$p(law|math) = 0.005$
Note that even if engineers as a rule don't like math, only a small minority does, the probability is still much higher than 0.30 as long as the minority of engineers is larger than the minority of lawyers*:
$p(math|eng) = 0.25$ implies $p(eng|math) = 0.991$
$p(math|eng) = 0.10$ implies $p(eng|math) = 0.977$
$p(math|eng) = 0.05$ implies $p(eng|math) = 0.955$
$p(math|eng) = 0.01$ implies $p(eng|math) = 0.811$
$p(math|eng) = 0.005$ implies $p(eng|math) = 0.682$
$p(math|eng) = 0.002$ implies $p(eng|math) = 0.462$
Yes, that last case is a two-to-one ratio of engineers who like math to lawyers who like math; and it still falls out of the 10-40pct category.

I understand the representativeness heuristic, which mistakes $p(math|eng)/p(math|law)$ for $p(eng|math)/p(law|math)$, ignoring the base rates, but there's no reason to give up the inference process if some data in the description is actually informative.

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* This example shows the elucidative power of working through some numbers. One might be tempted to say "ok, there's some updating, but it will probably still fall under the 10-40pct category" or "you may get large numbers with a disproportionate example like one-half of the engineers and one-in-a-thousand lawyers, but that's just an extreme case." Once we get some numbers down, these two arguments fail miserably.

Numbers are like examples, personas, and prototypes: they force assumptions and definitions out in the open.