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Algorithms to Live By: The Computer Science of Human Decisions Algorithms to Live By: The Computer Science of Human Decisions by Brian Christian
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“Seemingly innocuous language like 'Oh, I'm flexible' or 'What do you want to do tonight?' has a dark computational underbelly that should make you think twice. It has the veneer of kindness about it, but it does two deeply alarming things. First, it passes the cognitive buck: 'Here's a problem, you handle it.' Second, by not stating your preferences, it invites the others to simulate or imagine them. And as we have seen, the simulation of the minds of others is one of the biggest computational challenges a mind (or machine) can ever face.”
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. Make a mess on occasion. Travel light. Let things wait. Trust your instincts and don’t think too long. Relax. Toss a coin. Forgive, but don’t forget. To thine own self be true.”
Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions
“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. Counterintuitively, that might mean turning off the news.”
Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions
“Our judgments betray our expectations, and our expectations betray our experience. What we project about the future reveals a lot—about the world we live in, and about our own past.”
Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions
“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; how to do so with grace and confidence; and how to do so in a community with others who are all simultaneously trying to do the same.”
Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions
“Even the best strategy sometimes yields bad results—which is why computer scientists take care to distinguish between “process” and “outcome.” If you followed the best possible process, then you’ve done all you can, and you shouldn’t blame yourself if things didn’t go your way.”
Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions
“No choice recurs. We may get similar choices again, but never that exact one. Hesitation—inaction—is just as irrevocable as action. What the motorist, locked on the one-way road, is to space, we are to the fourth dimension: we truly pass this way but once.”
Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions
“If changing strategies doesn’t help, you can try to change the game. And if that’s not possible, you can at least exercise some control about which games you choose to play. The road to hell is paved with intractable recursions, bad equilibria, and information cascades. Seek out games where honesty is the dominant strategy. Then just be yourself.”
Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions
“When Charles Darwin was trying to decide whether he should propose to his cousin Emma Wedgwood, he got out a pencil and paper and weighed every possible consequence. In favor of marriage he listed children, companionship, and the 'charms of music and female chit-chat.' Against marriage he listed the 'terrible loss of time,' lack of freedom to go where he wished, the burden of visiting relatives, the expense and anxiety provoked by children, the concern that 'perhaps my wife won't like London,' and having less money to spend on books. Weighing one column against the other produced a narrow margin of victory, and at the bottom Darwin scrawled, 'Marry—Marry—Marry Q.E.D.' Quod erat demonstrandum, the mathematical sign-off that Darwin himself restated in English: 'It being proved necessary to Marry.”
Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions
“tackling real-world tasks requires being comfortable with chance, trading off time with accuracy, and using approximations. As”
Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions
“Everything starts to break down, however, when a species gains language. What we talk about isn’t what we experience—we speak chiefly of interesting things, and those tend to be things that are uncommon.”
Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions
“It’s fairly intuitive that never exploring is no way to live. But it’s also worth mentioning that never exploiting can be every bit as bad. In the computer science definition, exploitation actually comes to characterize many of what we consider to be life’s best moments. A family gathering together on the holidays is exploitation. So is a bookworm settling into a reading chair with a hot cup of coffee and a beloved favorite, or a band playing their greatest hits to a crowd of adoring fans, or a couple that has stood the test of time dancing to “their song.”
Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions
“Bayes’s Rule tells us that when it comes to making predictions based on limited evidence, few things are as important as having good priors—that is, a sense of the distribution from which we expect that evidence to have come. Good predictions thus begin with having good instincts about when we’re dealing with a normal distribution and when with a power-law distribution. As it turns out, Bayes’s Rule offers us a simple but dramatically different predictive rule of thumb for each.  …”
Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions
“Consider how many times you’ve seen either a crashed plane or a crashed car. It’s entirely possible you’ve seen roughly as many of each—yet many of those cars were on the road next to you, whereas the planes were probably on another continent, transmitted to you via the Internet or television. In the United States, for instance, the total number of people who have lost their lives in commercial plane crashes since the year 2000 would not be enough to fill Carnegie Hall even half full. In contrast, the number of people in the United States killed in car accidents over that same time is greater than the entire population of Wyoming. Simply”
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
“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
“To try and fail is at least to learn; to fail to try is to suffer the inestimable loss of what might have been.”
Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions
“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. We”
Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions
“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”
Tom Griffiths, Algorithms to Live By: The Computer Science of Human Decisions
“As Carl Sagan put it, “Science is a way of thinking much more than it is a body of knowledge.”
Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions
“Inspired by the punched railway tickets of the time, an inventor by the name of Herman Hollerith devised a system of punched manila cards to store information, and a machine, which he called the Hollerith Machine, to count and sort them. Hollerith was awarded a patent in 1889, and the government adopted the Hollerith Machine for the 1890 census. No one had ever seen anything like it. Wrote one awestruck observer, “The apparatus works as unerringly as the mills of the Gods, but beats them hollow as to speed.” Another, however, reasoned that the invention was of limited use: “As no one will ever use it but governments, the inventor will not likely get very rich.” This prediction, which Hollerith clipped and saved, would not prove entirely correct. Hollerith’s firm merged with several others in 1911 to become the Computing-Tabulating-Recording Company. A few years later it was renamed—to International Business Machines, or IBM.”
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
“Sorting something that you will never search is a complete waste; searching something you never sorted is merely inefficient.”
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
“Meanwhile, the kale market grew by 40% in 2013 alone. The biggest purchaser of kale the year before had been Pizza Hut, which put it in their salad bars—as decoration.”
Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions
“We can hope to be fortunate—but we should strive to be wise.”
Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions
“By the 2012 presidential election cycle, their company counted among its clients both the Obama re-election campaign and the campaign of Republican challenger Mitt Romney.”
Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions
“The same challenge also appears in an even more fraught setting: dating. Optimal stopping is the science of serial monogamy. Simple algorithms offer solutions not only to an apartment hunt but to all such situations in life where we confront the question of optimal stopping. People grapple with these issues every day—although surely poets have spilled more ink on the tribulations of courtship than of parking—and they do so with, in some cases, considerable anguish. But the anguish is unnecessary. Mathematically, at least, these are solved problems. 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.”
Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions
“There are many ways to relax a problem, and we’ve seen three of the most important. The first, Constraint Relaxation, simply removes some constraints altogether and makes progress on a looser form of the problem before coming back to reality. The second, Continuous Relaxation, turns discrete or binary choices into continua: when deciding between iced tea and lemonade, first imagine a 50–50 “Arnold Palmer” blend and then round it up or down. The third, Lagrangian Relaxation, turns impossibilities into mere penalties, teaching the art of bending the rules (or breaking them and accepting the consequences)”
Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions
“Though time management seems a problem as old as time itself, the science of scheduling began in the machine shops of the industrial revolution. In 1874, Frederick Taylor, the son of a wealthy lawyer, turned down his acceptance at Harvard to become an apprentice machinist at Enterprise Hydraulic Works in Philadelphia. Four years later, he completed his apprenticeship and began working at the Midvale Steel Works, where he rose through the ranks from lathe operator to machine shop foreman and ultimately to chief engineer. In the process, he came to believe that the time of the machines (and people) he oversaw was not being used very well, leading him to develop a discipline he called “Scientific Management.” Taylor created a planning office, at the heart of which was a bulletin board displaying the shop’s schedule for all to see. The board depicted every machine in the shop, showing the task currently being carried out by that machine and all the tasks waiting for it. This practice would be built upon by Taylor’s colleague Henry Gantt, who in the 1910s developed the Gantt charts that would help organize many of the twentieth century’s most ambitious construction projects, from the Hoover Dam to the Interstate Highway System. A century later, Gantt charts still adorn the walls and screens of project managers at firms like Amazon, IKEA, and SpaceX.”
Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

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