Algorithms to Live By Quotes

“Algorithms have been a part of human technology ever since the Stone Age.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“We know this because finding an apartment belongs to a class of mathematical problems known as “optimal stopping” problems. The 37% rule defines a simple series of steps—what computer scientists call an “algorithm”—for solving these problems.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“The chance of ending up with the single best applicant in this full-information version of the secretary problem comes to 58%—still far from a guarantee, but considerably better than the 37% success rate offered by the 37% Rule in the no-information game. If you have all the facts, you can succeed more often than not, even as the applicant pool grows arbitrarily large.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“before you can have a plan, you must first choose a metric.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“About a dozen studies have produced the same result: people tend to stop early, leaving better applicants unseen.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Spend the afternoon. You can’t take it with you. —ANNIE DILLARD”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“The science of human memory is said to have begun in 1879, with a young psychologist at the University of Berlin named Hermann Ebbinghaus.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“If you know the sequence ahead of time,” says Tarjan, who splits his time between Princeton and Silicon Valley, “you can customize the data structure to minimize the total time for the entire sequence. That’s the optimum offline algorithm:”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“The twenty-first-century shift into real-time analytics has only made the danger of metrics more intense. Avinash Kaushik, digital marketing evangelist at Google, warns that trying to get website users to see as many ads as possible naturally devolves into trying to cram sites with ads: “When you are paid on a [cost per thousand impressions] basis the incentive is to figure out how to show the most possible ads on every page [and] ensure the visitor sees the most possible pages on the site.… That incentive removes a focus from the important entity, your customer, and places it on the secondary entity, your advertiser.” The website might gain a little more money in the short term, but ad-crammed articles, slow-loading multi-page slide shows, and sensationalist clickbait headlines will drive away readers in the long run. Kaushik’s conclusion: “Friends don’t let friends measure Page Views. Ever.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Love is like organized crime. It changes the structure of the marriage game so that the equilibrium becomes the outcome that works best for everybody.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“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

“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

“Thinking about children as simply being at the transitory exploration stage of a lifelong algorithm might provide some solace for parents of preschoolers. (Tom has two highly exploratory preschool-age daughters, and hopes they are following an algorithm that has minimal regret.) But it also provides new insights about the rationality of children. Gopnik points out that “if you look at the history of the way that people have thought about children, they have typically argued that children are cognitively deficient in various ways—because if you look at their exploit capacities, they look terrible. They can’t tie their shoes, they’re not good at long-term planning, they’re not good at focused attention. Those are all things that kids are really awful at.” But pressing buttons at random, being very interested in new toys, and jumping quickly from one thing to another are all things that kids are really great at. And those are exactly what they should be doing if their goal is exploration. If you’re a baby, putting every object in the house into your mouth is like studiously pulling all the handles at the casino.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“The body is its own flow control. We can’t be in more than one place at one time. At a crowded party we inevitably participate in less than 5% of the conversation, and cannot read up or catch up on the remainder. Photons that miss the retina aren’t queued for later viewing. In real life, packet loss is almost total.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“The lesson of the TCP sawtooth is that in an unpredictable and changing environment, pushing things to the point of failure is indeed sometimes the best (or the only) way to use all the resources to their fullest. What matters is making sure that the response to failure is both sharp and resilient. Under AIMD, every connection that isn’t dropping the ball is accelerated until it is—and then it’s cut in half, and immediately begins accelerating again. And though it would violate almost every norm of current corporate culture, one can imagine a corporation in which, annually, every employee is always either promoted a single step up the org chart or sent part of the way back down.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Is there any alternative, any middle path between the institutional stagnation of the Peter Principle and the draconian severity of the “up or out” system? The AIMD algorithm can offer just such an approach, since it is explicitly designed to handle the demands of a volatile environment. A computer network must manage its own maximum transmission capacity, plus the transmission rates of its clients, all of which may be fluctuating unpredictably. Likewise, in a business setting, a company has a limited pool of funds to pay for its operations, and each worker or vendor has a limited capacity for the amount of work they can do and the amount of responsibility they can handle. Everyone’s needs, capacities, and partnerships are always in flux.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“On what to keep, Martha Stewart says to ask yourself a few questions: “How long have I had it? Does it still function? Is it a duplicate of something I already own? When was the last time I wore it or used it?” On how to organize what you keep, she recommends “grouping like things together,” and her fellow experts agree.”
― 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. As a result, individual pairwise interactions take place with a minimum of jockeying for status. By and large, any pair of people can tell, without needing to negotiate, who is supposed to show what level of respect to whom. Everyone knows where to meet.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“I had reached a juncture in my reading life that is familiar to those who have been there: in the allotted time left to me on earth, should I read more and more new books, or should I cease with that vain consumption—vain because it is endless—and begin to reread those books that had given me the intensest pleasure in my past. —LYDIA DAVIS”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Data scientist Jeff Hammerbacher, former manager of the Data group at Facebook, once told Bloomberg Businessweek that “the best minds of my generation are thinking about how to make people click ads.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“When we choose what to eat, who to spend time with, or what city to live in, regret looms large—presented with a set of good options, it is easy to torture ourselves with the consequences of making the wrong choice. These regrets are often about the things we failed to do, the options we never tried. In the memorable words of management theorist Chester Barnard, “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

“In the 1974 classic Zen and the Art of Motorcycle Maintenance, Robert Pirsig decries the conversational opener “What’s new?”—arguing that the question, “if pursued exclusively, results only in an endless parade of trivia and fashion, the silt of tomorrow.” He endorses an alternative as vastly superior: “What’s best?” But the reality is not so simple. Remembering that every “best” song and restaurant among your favorites began humbly as something merely “new” to you is a reminder that there may be yet-unknown bests still out there—and thus that the new is indeed worthy of at least some of our attention.”
― 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%.”
― 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. Living by the wisdom of computer science doesn’t sound so bad after all. And unlike most advice, it’s backed up by proofs.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“The price of anarchy measures the gap between cooperation (a centrally designed or coordinated solution) and competition (where each participant is independently trying to maximize the outcome for themselves). 236”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“There are many ways to relax a problem, and we've seen 3 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, teach the art of bending the rules (or breaking them and accepting the consequences). 180-181”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“The best time to plant a tree is 20 years ago. The second best time is now. ~ Proverb p. 118”
― 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.”
― 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”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“For one, be wary of cases where public information seems to exceed private information, where you know more about what people are doing than why they’re doing it, where you’re more concerned with your judgments fitting the consensus than fitting the facts.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions