Algorithms to Live By Quotes

“Win-Stay, Lose-Shift algorithm: choose an arm at random, and keep pulling it as long as it keeps paying off. If the arm doesn’t pay off after a particular pull, then switch to the other one. Although this simple strategy is far from a complete solution, Robbins proved in 1952 that it performs reliably better than chance.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“Communication is one of those delightful things that work only in practice; in theory it’s impossible.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Hesitation—inaction—is just as irrevocable as action.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“Unless we have good reason to think otherwise, it seems that our best guide to the future is a mirror image of the past. The nearest thing to clairvoyance is to assume that history repeats itself — backward.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“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.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“Thrashing is a very recognizable human state. If you've ever had a moment where you wanted to stop doing everything just to have the chance to write down everything you were supposed to be doing, but couldn't spare the time, you've thrashed.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“So explore when you will have time to use the resulting knowledge, exploit when you’re ready to cash in. The interval makes the strategy.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“When our expectations are uncertain and the data are noisy, the best bet is to paint with a broad brush”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Don’t always consider all your options. Don’t necessarily go for the outcome that seems best every time.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“To see what happens in the real world when an information cascade takes over, and the bidders have almost nothing but one another’s behavior to estimate an item’s value, look no further than Peter A. Lawrence’s developmental biology text The Making of a Fly, which in April 2011 was selling for $23,698,655.93 (plus $3.99 shipping) on Amazon’s third-party marketplace. How and why had this—admittedly respected—book reached a sale price of more than $23 million? It turns out that two of the sellers were setting their prices algorithmically as constant fractions of each other: one was always setting it to 0.99830 times the competitor’s price, while the competitor was automatically setting their own price to 1.27059 times the other’s. Neither seller apparently thought to set any limit on the resulting numbers, and eventually the process spiraled totally out of control.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Love is like organized crime. It”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“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.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“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.”
― Brian Christian & Tom Griffiths, Algorithms to Live By: The Computer Science of Human Decisions

“The conceptual vocabulary derived from the classical form of the [multi-armed bandit] problem—the tension between explore/exploit, the importance of the interval, the high value of the 0-0 option [Gittins Index], the minimization of regret—gives us a new way of making sense not only of specific problems that come before us, but of the entire arc of human life. 54”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Part of what makes real-time scheduling so complex and interesting is that it is fundamentally a negotiation between two principles that aren’t fully compatible. These two principles are called responsiveness and throughput: how quickly you can respond to things, and how much you can get done overall. Anyone who’s ever worked in an office environment can readily appreciate the tension between these two metrics. It’s part of the reason there are people whose job it is to answer the phone: they are responsive so that others may have throughput.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Getting Things Done advocates a policy of immediately doing any task of two minutes or less as soon as it comes to mind. Rival bestseller Eat That Frog! advises beginning with the most difficult task and moving toward easier and easier things. The Now Habit suggests first scheduling one’s social engagements and leisure time and then filling the gaps with work—rather than the other way around, as we so often do. William James, the “father of American psychology,” asserts that “there’s nothing so fatiguing as the eternal hanging on of an uncompleted task,” but Frank Partnoy, in Wait, makes the case for deliberately not doing things right away.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“The next pages begin our journey through some of the biggest challenges faced by computers and human minds alike: how to manage finite space, finite time, limited attention, unknown unknowns, incomplete information, and an unforeseeable future;”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Why not simply allow them unlimited vacation? Anecdotal reports thus far are mixed—but from a game-theoretic perspective, this approach is a nightmare. 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. The Nash equilibrium of this game is zero. As the CEO of software company Travis CI, Mathias Meyer, writes, “People will hesitate to take a vacation as they don’t want to seem like that person who’s taking the most vacation days. It’s a race to the bottom.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“The patent talks about “anticipatory package shipping,” which the press seized upon as though Amazon could somehow mail you something before you bought it.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Want to calculate the chance your bus is late? The chance your softball team will win? Count the number of times it has happened in the past plus one, then divide by the number of opportunities plus two. And the beauty of Laplace’s Law is that it works equally well whether we have a single data point or millions of them.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Do the difficult things while they are easy and do the great things while they are small. —LAO TZU”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“What horrified Hillis, unlike many a college undergraduate, wasn’t his roommate’s hygiene. It wasn’t that the roommate didn’t wash the socks; he did. The problem was what came next. The roommate pulled a sock out of the clean laundry hamper. Next he pulled another sock out at random. If it didn’t match the first one, he tossed it back in. Then he continued this process, pulling out socks one by one and tossing them back until he found a match for the first. With just 10 different pairs of socks, following this method will take on average 19 pulls merely to complete the first pair, and 17 more pulls to complete the second. In total, the roommate can expect to go fishing in the hamper 110 times just to pair 20 socks. It was enough to make any budding computer scientist request a room transfer.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“We say "brain fart" when we should really say "cache miss".”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Now is better than never. Although never is often better than right now. —THE ZEN OF PYTHON”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“And here, the children who had learned that the experimenter was unreliable were more likely to eat the marshmallow before she came back, losing the opportunity to earn a second treat. Failing the marshmallow test—and being less successful in later life—may not be about lacking willpower. It could be a result of believing that adults are not dependable: that they can’t be trusted to keep their word, that they disappear for intervals of arbitrary length. Learning self-control is important, but it’s equally important to grow up in an environment where adults are consistently present and trustworthy.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Learning the structure of the world around us and forming lasting social relationships are both lifelong tasks.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“If you want the best odds of getting the best apartment, spend 37% of your apartment hunt (eleven days, if you’ve given yourself a month for the search) noncommittally exploring options. Leave the checkbook at home; you’re just calibrating. But after that point, be prepared to immediately commit—deposit and all—to the very first place you see that beats whatever you’ve already seen. This is not merely an intuitively satisfying compromise between looking and leaping. It is the provably optimal solution.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“If you’re flammable and have legs, you are never blocking a fire exit.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“I expect to pass through this world but once. Any good therefore that I can do, or any kindness that I can show to any fellow creature, let me do it now. Let me not defer or neglect it, for I shall not pass this way again. —STEPHEN GRELLET”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“In fact, for any possible drawing of w winning tickets in n attempts, the expectation is simply the number of wins plus one, divided by the number of attempts plus two: (w+1)⁄(n+2).”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions