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Every harried renter, driver, and suitor you see around you as you go through a typical week is essentially reinventing the wheel. They don’t need a therapist; they need an algorithm. The therapist tells them to find the right, comfortable balance between impulsivity and overthinking. The algorithm tells them the balance is thirty-seven percent.
Long before algorithms were ever used by machines, they were used by people.
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.
For many sellers, turning down a good offer or two can be a nerve-racking proposition, especially if the ones that immediately follow are no better.
But in house selling and job hunting, even if it’s possible to reconsider an earlier offer, and even if that offer is guaranteed to still be on the table, you should nonetheless never do so. If it wasn’t above your threshold then, it won’t be above your threshold now. What you’ve paid to keep searching is a sunk cost. Don’t compromise, don’t second-guess. And don’t look back.
The ideal parking space, as Shoup models it, is one that optimizes a precise balance between the “sticker price” of the space, the time and inconvenience of walking, the time taken seeking the space (which varies wildly with destination, time of day, etc.), and the gas burned in doing so.
So explore when you will have time to use the resulting knowledge, exploit when you’re ready to cash in.
Exploration in itself has value, since trying new things increases our chances of finding the best. So taking the future into account, rather than focusing just on the present, drives us toward novelty.
Computer science can’t offer you a life with no regret. But it can, potentially, offer you just what Bezos was looking for: a life with minimal regret.
In the long run, optimism is the best prevention for regret.
In general, it seems that people tend to over-explore—to favor the new disproportionately over the best.
the size of people’s social networks (that is, the number of social relationships they engage in) almost invariably decreases over time.
Sorted lists are so ubiquitous that—like the fish who asks, “What is water?”—we must consciously work to perceive them at all. And then we perceive them everywhere.
We refer to things like Google and Bing as “search engines,” but that is something of a misnomer: they’re really sort engines.
Computer science, however, almost never cares about the best case.
Computer science has developed a shorthand specifically for measuring algorithmic worst-case scenarios: it’s called “Big-O” notation.
the effort expended on sorting materials is just a preemptive strike against the effort it’ll take to search through them later.
Sorting something that you will never search is a complete waste; searching something you never sorted is merely inefficient.
The dominant performance of the LRU algorithm in most tests that computer scientists have thrown at it leads to a simple suggestion: turn the library inside out. Put acquisitions in the back, for those who want to find them. And put the most recently returned items in the lobby, where they are ripe for the browsing.
Anticipating the purchases of individuals is challenging, but when predicting the purchases of a few thousand people, the law of large numbers kicks in.
And because nothing benefits quite so much from local caching as the enormous files that comprise full-length HD video, it’s certain that Netflix has arranged it so the files for, say, L.A. Story live right in Los Angeles, just like its characters—and, more importantly, its fans.
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.
“Some things that might seem frustrating as we grow older (like remembering names!) are a function of the amount of stuff we have to sift through … and are not necessarily a sign of a failing mind.” As he puts it, “A lot of what is currently called decline is simply learning.”
Caching gives us the language to understand what’s happening. We say “brain fart” when we should really say “cache miss.”
the scheduling problem that matters the most involves just one machine: ourselves.
If you have only a single machine, and you’re going to do all of your tasks, then any ordering of the tasks will take you the same amount of time.
This is something of a theme in computer science: before you can have a plan, you must first choose a metric.
Staying focused not just on getting things done but on getting weighty things done
When the future is foggy, it turns out you don’t need a calendar—just a to-do list.
anyone you interrupt more than a few times an hour is in danger of doing no work at all.
the best strategy for getting things done might be, paradoxically, to slow down.
Whatever their drawbacks, regularly scheduled meetings are one of our best defenses against the spontaneous interruption and the unplanned context switch.
Gott saw the Berlin Wall eight years after it was built, so his best guess was that it would stand for eight years more. (It ended up being twenty.)
The richer the prior information we bring to Bayes’s Rule, the more useful the predictions we can get out of it.
The reason we can often make good predictions from a small number of observations—or just a single one—is that our priors are so rich.
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.
the murder rate in the United States declined by 20% over the course of the 1990s, yet during that time period the presence of gun violence on American news increased by 600%.
If you want to be a good intuitive Bayesian—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. Co...
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you have to be very careful of what you incent people to do, because various incentive structures create all sorts of consequences that you can’t anticipate.
When it comes to portfolio management, it turns out that unless you’re highly confident in the information you have about the markets, you may actually be better off ignoring that information altogether.
As a species, being constrained by the past makes us less perfectly adjusted to the present we know but helps keep us robust for the future we don’t.
even when you don’t commit the infraction, simply imagining it can be illuminating.
when we want to know something about a complex quantity, we can estimate its value by sampling from it.
A statistic can only tell us part of the story, obscuring any underlying heterogeneity. And often we don’t even know which statistic we need.
“Once you got somewhere you were happy,” he told the Guardian, “you’d be stupid to shake it up any further.”
Communication is one of those delightful things that work only in practice; in theory it’s impossible.
A friend of ours recently mused about a childhood companion who had a disconcerting habit of flaking on social plans. What to do? Deciding once and for all that she’d finally had enough and giving up entirely on the relationship seemed arbitrary and severe, but continuing to persist in perpetual rescheduling seemed naïve, liable to lead to an endless amount of disappointment and wasted time. Solution: Exponential Backoff on the invitation rate. Try to reschedule in a week, then two, then four, then eight. The rate of “retransmission” goes toward zero—yet you never have to completely give up.
A recent study showed that the average worker takes only half of the vacation days granted them, and a stunning 15% take no vacation at all.
All employees want, in theory, to take as much vacation as possible. But they also all want to take just slightly less vacation than each other, to be perceived as more loyal, more committed, and more dedicated (hence more promotion-worthy). Everyone looks to the others for a baseline, and will take just slightly less than that.
