Algorithms to Live By Quotes

“The most prevalent critique of modern communications is that we are “always connected.” But the problem isn’t that we’re always connected; we’re not. The problem is that we’re always buffered. The difference is enormous.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“And it’s actually rational to emphasize exploration—the new rather than the best, the exciting rather than the safe, the random rather than the considered—for many of those choices, particularly earlier in life.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“To live in a restless world requires a certain restlessness in oneself. So long as things continue to change, you must never fully cease exploring.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“But any distrust regarding the analogy-based approach would soon vanish: at IBM, Kirkpatrick and Gelatt’s simulated annealing algorithms started making better chip layouts than the guru. Rather than keep mum about their secret weapon and become cryptic guru figures themselves, they published their method in a paper in Science, opening it up to others. Over the next few decades, that paper would be cited a whopping thirty-two thousand times. To this day, simulated annealing remains one of the most promising approaches to optimization problems known to the field.”
― 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 & 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”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Simply put, the representation of events in the media does not track their frequency in the world. As sociologist Barry Glassner notes, 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. Counterintuitively, that might mean turning off the news.”
― 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

“Though all Christians start a wedding invitation by solemnly declaring their marriage is due to special Divine arrangement, I, as a philosopher, would like to talk in greater detail about this … —JOHANNES KEPLER If you prefer Mr. Martin to every other person; if you think him the most agreeable man you have ever been in company with, why should you hesitate? —JANE AUSTEN, EMMA”
― 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

“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. It’s possible that a similar mechanism was in play during the enigmatic and controversial stock market “flash crash” of May 6, 2010, when, in a matter of minutes, the price of several seemingly random companies in the S&P 500 rose to more than $100,000 a share, while others dropped precipitously—sometimes to $0.01 a share. Almost $1 trillion of value instantaneously went up in smoke.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“I find that the three major administrative problems on a campus are sex for the students, athletics for the alumni, and parking for the faculty. —CLARK KERR, PRESIDENT OF UC BERKELEY, 1958–1967”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Stanford ecologist Deborah Gordon and computer scientist Balaji Prabhakar discovered that ants appear to have developed flow control algorithms millions of years before humans did.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“The economist Harry Markowitz won the 1990 Nobel Prize in Economics for developing modern portfolio theory: his groundbreaking “mean-variance portfolio optimization” showed how an investor could make an optimal allocation among various funds and assets to maximize returns at a given level of risk. So when it came time to invest his own retirement savings, it seems like Markowitz should have been the one person perfectly equipped for the job. What did he decide to do? I should have computed the historical covariances of the asset classes and drawn an efficient frontier. Instead, I visualized my grief if the stock market went way up and I wasn’t in it—or if it went way down and I was completely in it. My intention was to minimize my future regret. So I split my contributions fifty-fifty between bonds and equities. Why”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“The Gittins index, then, provides a formal, rigorous justification for preferring the unknown, provided we have some opportunity to exploit the results of what we learn from exploring. The old adage tells us that “the grass is always greener on the other side of the fence,” but the math tells us why: the unknown has a chance of being better, even if we actually expect it to be no different, or if it’s just as likely to be worse. The”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“The Gittins index, then, provides a formal, rigorous justification for preferring the unknown, provided we have some opportunity to exploit the results of what we learn from exploring. The”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“To live in a restless world requires a certain restlessness in oneself. So long as things continue to change, you must never fully cease exploring”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Journalists are martyrs, exploring so that others may exploit. In”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“In one particularly dramatic case, an officer instinctively grabbed the gun out of the hands of an assailant and then instinctively handed it right back—just as he had done time and time again with his trainers in practice. Detecting”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Minimizing the sum of completion times leads to a very simple optimal algorithm called Shortest Processing Time: always do the quickest task you can. Even”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“What’s more, sports are not, of course, always designed strictly to minimize the number of games. Without remembering this, some aspects of sports scheduling would otherwise seem mysterious to a computer scientist.”
― 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”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“They would randomly assign patients to either ECMO or the conventional treatment until a prespecified number of deaths was observed in one of the groups. Then they would switch all the patients in the study to the more effective treatment of the two.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“This procedure focuses on decisively resolving the question of which treatment is better, rather than on providing the best treatment to each patient in the trial itself.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Indeed, as Peter Whittle recounts, during World War II efforts to solve the question “so sapped the energies and minds of Allied analysts … that the suggestion was made that the problem be dropped over Germany, as the ultimate instrument of intellectual sabotage.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“After all, the whole time you’re searching for a secretary, you don’t have a secretary. What’s more, you’re spending the day conducting interviews instead of getting your own work done.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“The math shows that you should always keep playing. But if you follow this strategy, you will eventually lose everything. Some problems are better avoided than solved. Always”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“We asked Shoup if his research allows him to optimize his own commute, through the Los Angeles traffic to his office at UCLA. Does arguably the world’s top expert on parking have some kind of secret weapon? He does: “I ride my bike.” When”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions