Algorithms to Live By Quotes

“Thus we encounter the first lesson in single-machine scheduling literally before we even begin: make your goals explicit. We can’t declare some schedule a winner until we know how we’re keeping”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“When you wash your clothes, they have to pass through the washer and the dryer in sequence, and different loads will take different amounts of time in each. A heavily soiled load might take longer to wash but the usual time to dry; a large load might take longer to dry but the usual time to wash. So, Johnson asked, if you have several loads of laundry to do on the same day, what’s the best way to do them? His answer was that you should begin by finding the single step that takes the least amount of time—the load that will wash or dry the quickest. If that shortest step involves the washer, plan to do that load first. If it involves the dryer, plan to do it last. Repeat this process for the remaining loads, working from the two ends of the schedule toward the middle.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“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

“In short, the mathematics of self-organizing lists suggests something radical: the big pile of papers on your desk, far from being a guilt-inducing fester of chaos, is actually one of the most well-designed and efficient structures available. What might appear to others to be an unorganized mess is, in fact, a self-organizing mess.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Depend upon it there comes a time when for every addition of knowledge you forget something that you knew before. It is of the highest importance, therefore, not to have useless facts elbowing out the useful ones.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“certain woman had a very sharp consciousness but almost no memory.… She remembered enough to work, and she worked hard.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Much as we bemoan the daily rat race, the fact that it’s a race rather than a fight is a key part of what sets us apart from the monkeys, the chickens—and, for that matter, the rats.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“What Carstensen and her colleagues found is that the shrinking of social networks with aging is due primarily to “pruning” peripheral relationships and focusing attention instead on a core of close friends and family members. This process seems to be a deliberate choice: as people approach the end of their lives, they want to focus more on the connections that are the most meaningful.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Economists refer to this idea, of valuing the present more highly than the future, as “discounting.” Unlike”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“A sobering property of trying new things is that the value of exploration, of finding a new favorite, can only go down over time, as the remaining opportunities to savor it dwindle.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Instead, 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

“The perfect is the enemy of the good. —VOLTAIRE”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“The success of Upper Confidence Bound algorithms offers a formal justification for the benefit of the doubt. Following the advice of these algorithms, 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

“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

“Exploration in itself has value, since trying new things increases our chances of finding the best. So taking the future into account, rather than focusing just on the present, drives us toward novelty.”
― 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”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“More significantly, Win-Stay, Lose-Shift doesn’t have any notion of the interval over which you are optimizing. If your favorite restaurant disappointed you the last time you ate there, that algorithm always says you should go to another place—even if it’s your last night in town.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“you’re a skilled burglar and have a 90% chance of pulling off each robbery (and a 10% chance of losing it all), then retire after 90/10 = 9 robberies. A ham-fisted amateur with a 50/50 chance of success? The first time you have nothing to lose, but don’t push your luck more than once.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“For instance, let’s say the range of offers we’re expecting runs from $400,000 to $500,000. First, if the cost of waiting is trivial, we’re able to be almost infinitely choosy. If the cost of getting another offer is only a dollar, we’ll maximize our earnings by waiting for someone willing to offer us $499,552.79 and not a dime less. If waiting costs $2,000 an offer, we should hold out for an even $480,000. In a slow market where waiting costs $10,000 an offer, we should take anything over $455,279. Finally, if waiting costs half or more of our expected range of offers—in this case, $50,000—then there’s no advantage whatsoever to holding out; we’ll do best by taking the very first offer that comes along and calling it done. Beggars can’t be choosers.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“Gold digging is more likely to succeed than a quest for love. If you’re evaluating your partners based on any kind of objective criterion—say, their income percentile—then you’ve got a lot more information at your disposal than if you’re after a nebulous emotional response (“love”) that might require both experience and comparison to calibrate.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“If you’re down to the last applicant, of course, you are necessarily forced to choose her. But when looking at the next-to-last applicant, the question becomes: is she above the 50th percentile? If yes, then hire her; if not, it’s worth rolling the dice on the last applicant instead, since her odds of being above the 50th percentile are 50/50 by definition. Likewise, you should choose the third-to-last applicant if she’s above the 69th percentile, the fourth-to-last applicant if she’s above the 78th, and so on, being more choosy the more applicants are left. No matter what, never hire someone who’s below average unless you’re totally out of options.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“We can instead use the Threshold Rule, where we immediately accept an applicant if she is above a certain percentile. We don’t need to look at an initial group of candidates to set this threshold—but we do, however, need to be keenly aware of how much looking remains available.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“He didn’t know how many women he could expect to meet in his lifetime, but there’s a certain flexibility in the 37% Rule: it can be applied to either the number of applicants or the time over which one is searching. Assuming that his search would run from ages eighteen to forty, the 37% Rule gave age 26.1 years as the point at which to switch from looking to leaping.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“Straightforward arithmetic, of course, isn’t particularly challenging for a modern computer. Rather, it’s tasks like conversing with people, fixing a corrupted file, or winning a game of Go—problems where the rules aren’t clear, some of the required information is missing, or finding exactly the right answer would require considering an astronomical number of possibilities—that now pose the biggest challenges in computer science. And the algorithms that researchers have developed to solve the hardest classes of problems have moved computers away from an extreme reliance on exhaustive calculation. Instead, 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

“In 1997, Forbes magazine identified Boris Berezovsky as the richest man in Russia, with a fortune of roughly $3 billion.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“By the 1960s, one study estimated that more than a quarter of the computing resources of the world were being spent on sorting.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“The heart has its reasons which reason knows nothing of. —BLAISE PASCAL”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“If you can’t solve the problem in front of you, solve an easier version of it—and then see if that solution offers you a starting point, or a beacon, in the full-blown problem. Maybe it does.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Programmers don’t talk because they must not be interrupted.… To synchronize with other people (or their representation in telephones, buzzers and doorbells) can only mean interrupting the thought train. Interruptions mean certain bugs. You must not get off the train. —ELLEN ULLMAN”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Rather than being signs of moral or psychological degeneracy, restlessness and doubtfulness actually turn out to be part of the best strategy for scenarios where second chances are possible.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions