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July 1 - July 4, 2016
The chance of ending up with the single best applicant in this full-information version of the secretary problem comes to 58%—still far from a guarantee, but considerably better than the 37% success rate offered by the 37% Rule in the no-information game.
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About a dozen studies have produced the same result: people tend to stop early, leaving better applicants unseen.
This is the first and most fundamental insight of sorting theory. Scale hurts.
Regardless of whatever other challenges aging brings, older brains—which must manage a greater store of memories—are literally solving harder computational problems with every passing day.
Every decision is a kind of prediction: about how much you’ll like something you haven’t tried yet, about where a certain trend is heading, about how the road less traveled (or more so) is likely to pan out.
In other words, overfitting poses a danger any time we’re dealing with noise or mismeasurement—and we almost always are.
“It really is true that the company will build whatever the CEO decides to measure.”
In an educational setting, how can we distinguish between a class of students excelling at the subject matter and a class merely being “taught to the test”?
Simply put, Cross-Validation means assessing not only how well a model fits the data it’s given, but how well it generalizes to data it hasn’t seen.
If you can’t explain it simply, you don’t understand it well enough.
One way to choose among several competing models is the Occam’s razor principle, which suggests that, all things being equal, the simplest possible hypothesis is probably the correct one.
If we introduce a complexity penalty, then more complex models need to do not merely a better job but a significantly better job of explaining the data to justify their greater complexity.
A heuristic that favors simpler answers—with fewer factors, or less computation—offers precisely these “less is more” effects.
Going with our first instinct can be the rational solution. The more complex, unstable, and uncertain the decision, the more rational an approach that is.
a “constrained optimization” problem: how to find the single best arrangement of a set of variables, given particular rules and a scorekeeping measure.
A close examination of random samples can be one of the most effective means of making sense of something too complex to be comprehended directly.
When a networking buffer fills up, what typically happens is called Tail Drop: an unceremonious way of saying that every packet arriving after that point is simply rejected, and effectively deleted.
Politely withholding your preferences puts the computational problem of inferring them on the rest of the group. In contrast, politely asserting your preferences (“Personally, I’m inclined toward x. What do you think?”) helps shoulder the cognitive load of moving the group toward resolution.
