Apr. 2nd, 2009


Apr. 2nd, 2009 11:29 pm
lederhosen: (Default)
Been meaning to post this one for a while (I didn't forget and post it already, did I?) It caught my attention because the end result is very similar to some of the stuff that came up in my work last year, though for somewhat different reasons.

Strong profiling is not mathematically optimal for discovering rare malfeasors.

The result, in a nutshell: suppose you're looking for Bad People via random screening, and you know that certain types of people (nationality, age, gender, whatever) are ten times more likely to be malfeasors than other people.

The natural response would be to screen the first type of person ten times as often ('strong profiling')... but it turns out that this is actually no more efficient that screening everybody at random with the same probability. When malfeasors do fit the stereotype, strong profiling lets you find them slightly faster; when they don't, strong profiling takes a lot longer to find them, because your efforts are concentrated too much on the wrong people. It turns out that the optimal solution here (ignoring moral considerations and the possibility that people will change behaviour in response to your strategy) is somewhere in between the two - 'weak profiling'.

(I should add that this result is for one specific formulation, as described in the paper; it could easily be argued that some scenarios don't fit this particular mathematical model. But it's still interesting as an illustration that putting too much faith in your information can be a bad idea.)


lederhosen: (Default)

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