sethist's comments

I don't think the author is proposing an "A<-B<-C" system. She is stating that the models are really blameless in the situation when the information provided to the models are wrong. The cause and correlation issue arises there.

Silver assumes that the systems fail because the models are bad. O'Neil is instead claiming those are just correlations and not a cause and effect relationship. Basically the models are bad and the systems failed because the people providing the data were corrupt. Using your example: "A<-C" and "B<-C"

I don't think blunt and inefficient are necessarily the same in this instance. The hiring managers are basically outsourcing the prescreening to the college admission boards. That method certainly allows a huge number of great candidates to slip through the cracks. However,it also is an incredibly quick and efficient way to narrow down applications to a group that is on average "better" than the general population.

Another firm can try to capitalize on the market inefficiencies and hire tons of Brown grads. Although they will likely need to spend more time and money on the hiring process since the candidates aren't as thoroughly prescreened. None of us know if that will really pay off in the end.

Just for argument's sake, think of it like a college application process. Google might be MIT and Amazon somewhere like RPI.

Why try so hard to into a school that everyone is applying to when you can instead much more easily get a position at a lower profile school?"

RPI is easier to get into and could still give you challenging and rewarding work, but that doesn't mean it isn't worth the extra effort to get into the more difficult and sought-after position.