Algorithms to Live By Quotes

“certain flexibility in the 37% Rule: it can be applied to either the number of applicants or the time over which one is searching.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“A 63% failure rate, when following the best possible strategy, is a sobering fact. Even when we act optimally in the secretary problem, we will still fail most of the time—that is, we won’t end up with the single best applicant in the pool.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Today, algorithm design draws not only on computer science, math, and engineering but on kindred fields like statistics and operations research.”
― 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.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“The solutions to everyday problems that come from computer science tell a different story about the human mind. Life is full of problems that are, quite simply, hard. And the mistakes made by people often say more about the intrinsic difficulties of the problem than about the fallibility of human brains. Thinking algorithmically about the world, learning about the fundamental structures of the problems we face and about the properties of their solutions, can help us see how good we actually are, and better understand the errors that we make.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

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

“In Silicon Valley, for instance, there’s an adage about meetings: “You go to the money, the money doesn’t come to you.” Vendors go to founders, founders go to venture capitalists, venture capitalists go to their limited partners. It’s possible for the individuals to resent the basis of this hierarchy, but not really to contest its verdict.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“The studies also show that aggression appears to go away after a period of some weeks, unless new members are added to the flock—corroborating the idea that the group is sorting itself.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Shifting the bulk of one’s attention to one’s favorite things should increase quality of life. And it seems like it does: Carstensen has found that older people are generally more satisfied with their social networks, and often report levels of emotional well-being that are higher than those of younger adults. So there’s a lot to look forward to in being that late-afternoon restaurant regular, savoring the fruits of a life’s explorations. 3 Sorting Making Order Nowe if the word, which thou art desirous to finde, begin with (a) then looke in the beginning of this Table, but if with (v) looke towards the end.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Am I Wasting My Time Organizing Email?” Spoiler alert: the conclusion was an emphatic Yes. “It’s empirical, but it’s also experiential,”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“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

“This is the first and most fundamental insight of sorting theory. Scale hurts.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“Recognizing that old age is a time of exploitation helps provide new perspectives on some of the classic phenomena of aging. For example, while going to college—a new social environment filled with people you haven’t met—is typically a positive, exciting time, going to a retirement home—a new social environment filled with people you haven’t met—can be painful. And that difference is partly the result of where we are on the explore/exploit continuum at those stages of our lives.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“In the long run, optimism is the best prevention for regret.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Sometimes mess is more than just the easy choice. It’s the optimal choice.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“When we start designing something, we sketch out ideas with a big, thick Sharpie marker, instead of a ball-point pen. Why? Pen points are too fine. They’re too high-resolution. They encourage you to worry about things that you shouldn’t worry about yet, like perfecting the shading or whether to use a dotted or dashed line. You end up focusing on things that should still be out of focus. A Sharpie makes it impossible to drill down that deep. You can only draw shapes, lines, and boxes. That’s good. The big picture is all you should be worrying about in the beginning.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“The satirical “Peter Principle,” articulated in the 1960s by education professor Laurence J. Peter, states that “every employee tends to rise to his level of incompetence.” The idea is that in a hierarchical organization, anyone doing a job proficiently will be rewarded with a promotion into a new job that may involve more complex and/or different challenges. When the employee finally reaches a role in which they don’t perform well, their march up the ranks will stall, and they will remain in that role for the rest of their”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“The world’s most difficult word to translate has been identified as “ilunga,” from the Tshiluba language spoken in south-eastern DR Congo.… Ilunga means “a person who is ready to forgive any abuse for the first time, to tolerate it a second time, but never a third time.” —BBC”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“One day as a child, Brian was complaining to his mother about all the things he had to do: his homework, his chores.… “Technically, you don’t have to do anything,” his mother replied. “You don’t have to do what your teachers tell you. You don’t have to do what I tell you. You don’t even have to obey the law. There are consequences to everything, and you get to decide whether you want to face those consequences.” Brian’s kid-mind was blown. It was a powerful message, an awakening of a sense of agency, responsibility, moral judgment. It was something else, too: a powerful computational technique called Lagrangian Relaxation.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“When then senator Obama visited Google in 2007, CEO Eric Schmidt jokingly began the Q&A like a job interview, asking him, “What’s the best way to sort a million thirty-two-bit integers?” Without missing a beat, Obama cracked a wry smile and replied, “I think the Bubble Sort would be the wrong way to go.” The crowd of Google engineers erupted in cheers. “He had me at Bubble Sort,” one later recalled.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Carpe diem,” urges Robin Williams in one of the most memorable scenes of the 1989 film Dead Poets Society. “Seize the day, boys. Make your lives extraordinary.” It’s incredibly important advice. It’s also somewhat self-contradictory. Seizing a day and seizing a lifetime are two entirely different endeavors.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Information Cascades: The Tragic Rationality of Bubbles Whenever you find yourself on the side of the majority, it is time to pause and reflect. —MARK TWAIN”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“As the saying goes, “the most exciting phrase to hear in science, the one that heralds new discoveries, is not ‘Eureka!’ but ‘That’s funny.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“Exponential Backoff was a huge part of the successful functioning of the ALOHAnet beginning in 1971, and in the 1980s it was baked into TCP, becoming a critical part of the Internet. All these decades later, it still is. As one influential paper puts it, “For a transport endpoint embedded in a network of unknown topology and with an unknown, unknowable and constantly changing population of competing conversations, only one scheme has any hope of working—exponential backoff.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“Forgiveness The world’s most difficult word to translate has been identified as “ilunga,” from the Tshiluba language spoken in south-eastern DR Congo.… Ilunga means “a person who is ready to forgive any abuse for the first time, to tolerate it a second time, but never a third time.” —BBC NEWS If at first you don’t succeed, / Try, try again. —T. H. PALMER”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“This approach, called Simulated Annealing, seemed like an intriguing way to map physics onto problem solving. But would it work? The initial reaction among more traditional optimization researchers was that this whole approach just seemed a little too … metaphorical. “I couldn’t convince math people that this messy stuff with temperatures, all this analogy-based stuff, was real,” says Kirkpatrick, “because mathematicians are trained to really distrust intuition.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“Over a lifetime, you’re going to make a lot of 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

“You should be excited to meet new people and try new things—to assume the best about them, in the absence of evidence to the contrary. In the long run, optimism is the best prevention for regret.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“The Upside of Heuristics 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 in the world would he do that?”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“In 2010 and 2015, the FDA released a pair of draft “guidance” documents on “Adaptive Design” clinical trials for drugs and medical devices, which suggests—despite a long history of sticking to an option they trust—that they might at last be willing to explore alternatives.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions