Look-Then-Leap Rule: You set a predetermined amount of time for “looking”—that is, exploring your options, gathering data—in which you categorically don’t choose anyone, no matter how impressive. After that point, you enter the “leap” phase, prepared to instantly commit to anyone who outshines the best applicant you saw in the look phase.
37% Rule: look at the first 37% of the applicants,* choosing none, then be ready to leap for anyone better than all those you’ve seen so far.
Intuitively, we think that rational decision-making means exhaustively enumerating our options, weighing each one carefully, and then selecting the best. But in practice, when the clock—or the ticker—is ticking, few aspects of decision-making (or of thinking more generally) are as important as this one: when to stop.
Perhaps the deepest insight that comes from thinking about later life as a chance to exploit knowledge acquired over decades is this: life should get better over time. What an explorer trades off for knowledge is pleasure
minimizing our pain and suffering when it comes to sorting is all about minimizing the number of things we have to sort
Caching gives us the language to understand what’s happening. We say “brain fart” when we should really say “cache miss.” The disproportionate occasional lags in information retrieval are a reminder of just how much we benefit the rest of the time by having what we need at the front of our minds. So as you age, and begin to experience these sporadic latencies, take heart: the length of a delay is partly an indicator of the extent of your experience. The effort of retrieval is a testament to how much you know. And the rarity of those lags is a testament to how well you’ve arranged it: keeping the most important things closest to hand
The key to a good human memory then becomes the same as the key to a good computer cache: predicting which items are most likely to be wanted in the future
try to stay on a single task as long as possible without decreasing your responsiveness below the minimum acceptable limit. Decide how responsive you need to be—and then, if you want to get things done, be no more responsive than that. If you find yourself doing a lot of context switching because you’re tackling a heterogeneous collection of short tasks, you can also employ another idea from computer science: “interrupt coalescing.”
When the future is foggy, it turns out you don’t need a calendar—just a to-do list
Considering the costs of context switching, the silver lining to this should by now be obvious: you can only get interrupted by bills and letters at most once a day. What’s more, the twenty-four-hour postal rhythm demands minimal responsiveness from you: it doesn’t make any difference whether you mail your reply five minutes or five hours after receiving a letter
Our beeping and buzzing devices have “Do Not Disturb” modes, which we could manually toggle on and off throughout the day, but that is too blunt an instrument. Instead, we might agitate for settings that would provide an explicit option for interrupt coalescing—the same thing at a human timescale that the devices are doing internally. Alert me only once every ten minutes, say; then tell me everything.
Learning self-control is important, but it’s equally important to grow up in an environment where adults are consistently present and trustworthy.
the representation of events in the media does not track their frequency in the world.
if you want to naturally make good predictions, without having to think about what kind of prediction rule is appropriate—you need to protect your priors. Counterintuitively, that might mean turning off the news.
If you have high uncertainty and limited data, then do stop early by all means. If you don’t have a clear read on how your work will be evaluated, and by whom, then it’s not worth the extra time to make it perfect with respect to your own (or anyone else’s) idiosyncratic guess at what perfection might be. The greater the uncertainty, the bigger the gap between what you can measure and what matters, the more you should watch out for overfitting—that is, the more you should prefer simplicity, and the earlier you should stop. When you’re truly in the dark, the best-laid plans will be the simplest. When our expectations are uncertain and the data are noisy, the best bet is to paint with a broad brush, to think in broad strokes
As McGill’s Henry Mintzberg puts it, “What would happen if we started from the premise that we can’t measure what matters and go from there? Then instead of measurement we’d have to use something very scary: it’s called judgment.”
Unless we’re willing to spend eons striving for perfection every time we encounter a hitch, hard problems demand that instead of spinning our tires we imagine easier versions and tackle those first. When applied correctly, this is not just wishful thinking, not fantasy or idle daydreaming. It’s one of our best ways of making progress.
even if you’re in the habit of sometimes acting on bad ideas, you should always act on good ones
your likelihood of following a bad idea should be inversely proportional to how bad an idea it is
you should front-load randomness, rapidly cooling out of a totally random state, using ever less and less randomness as time goes on, lingering longest as you approach freezing
Seek out games where honesty is the dominant strategy. Then just be yourself.
In almost every domain we’ve considered, we have seen how the more real-world factors we include—whether it’s having incomplete information when interviewing job applicants, dealing with a changing world when trying to resolve the explore/exploit dilemma, or having certain tasks depend on others when we’re trying to get things done—the more likely we are to end up in a situation where finding the perfect solution takes unreasonably long. And indeed, people are almost always confronting what computer science regards as the hard cases. Up against such hard cases, effective algorithms make assumptions, show a bias toward simpler solutions, trade off the costs of error against the costs of delay, and take chances. These aren’t the concessions we make when we can’t be rational. They’re what being rational means.
people preferred receiving a constrained problem, even if the constraints were plucked out of thin air, than a wide-open one.