Algorithms to Live By Quotes

“And at a factory, focusing on production metrics led supervisors to neglect maintenance and repairs, setting up future catastrophe. Such problems can’t simply be dismissed as a failure to achieve management goals. Rather, they are the opposite: the ruthless and clever optimization of the wrong thing.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“For instance, someone living in a desert climate might overestimate the amount of sand in the world, and someone living at the poles might overestimate the amount of snow. Both are well tuned to their own ecological niche.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Small data is big data in disguise. The reason we can often make good predictions from a small number of observations—or just a single one—is that our priors are so rich. Whether we know it or not, we appear to carry around in our heads surprisingly accurate priors about movie grosses and running times, poem lengths, and political terms of office, not to mention human life spans. We don’t need to gather them explicitly; we absorb them from the world.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“In a power-law distribution, the longer something has gone on, the longer we expect it to continue going on. So a power-law event is more surprising the longer we’ve been waiting for it—and maximally surprising right before it happens. A nation, corporation, or institution only grows more venerable with each passing year, so it’s always stunning when it collapses. In a normal distribution, events are surprising when they’re early—since we expected them to reach the average—but not when they’re late. Indeed, by that point they seem overdue to happen, so the longer we wait, the more we expect them. And in an Erlang distribution, events by definition are never any more or less surprising no matter when they occur. Any state of affairs is always equally likely to end regardless of how long it’s lasted. No wonder politicians are always thinking about their next election.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“The Erlang distribution gives us a third kind of prediction rule, the Additive Rule: always predict that things will go on just a constant amount longer. The familiar refrain of “Just five more minutes!… [five minutes later] Five more minutes!” that so often characterizes human claims regarding, say, one’s readiness to leave the house or office, or the time until the completion of some task, may seem indicative of some chronic failure to make realistic estimates”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Erlang himself, working for the Copenhagen Telephone Company in the early twentieth century, used it to model how much time could be expected to pass between successive calls on a phone network. Since then, the Erlang distribution has also been used by urban planners and architects to model car and pedestrian traffic, and by networking engineers designing infrastructure for the Internet. There are a number of domains in the natural world, too, where events are completely independent from one another and the intervals between them thus fall on an Erlang curve. Radioactive decay is one example, which means that the Erlang distribution perfectly models when to expect the next ticks of a Geiger counter. It also turns out to do a pretty good job of describing certain human endeavors—such as the amount of time politicians stay in the House of Representatives.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“So when it comes to poetry, make sure you’ve got a comfortable seat. Something normally distributed that’s gone on seemingly too long is bound to end shortly; but the longer something in a power-law distribution has gone on, the longer you can expect it to keep going.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“the rich get richer,” and indeed the process of “preferential attachment” is one of the surest ways to produce a power-law distribution. The most popular websites are the most likely to get incoming links; the most followed online celebrities are the ones most likely to gain new fans; the most prestigious firms are the ones most likely to attract new clients; the biggest cities are the ones most likely to draw new residents. In every case, a power-law distribution will result.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“In fact, money in general is a domain full of power laws. Power-law distributions characterize both people’s wealth and people’s incomes. The mean income in America, for instance, is $55,688—but because income is roughly power-law distributed, we know, again, that many more people will be below this mean than above it, while those who are above might be practically off the charts. So it is: two-thirds of the US population make less than the mean income, but the top 1% make almost ten times the mean. And the top 1% of the 1% make ten times more than that.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Real-World Priors … In the broadest sense, there are two types of things in the world: things that tend toward (or cluster around) some kind of “natural” value, and things that don’t.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“All these suppositions are consistent and conceivable. Why should we give the preference to one, which is no more consistent or conceivable than the rest? —DAVID HUME”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“we be, therefore, engaged by arguments to put trust in past experience, and make it the standard of our future judgement, these arguments must be probable only. —DAVID HUME”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Knuth has not had an email address since 1990. “Email is a wonderful thing for people whose role in life is to be on top of things. But not for me; my role is to be on the bottom of things. What I do takes long hours of studying and uninterruptible concentration.” He reviews all his postal mail every three months, and all his faxes every six.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“And again, this is a principle that can be transferred to human lives. The moral is that you should try to stay on a single task as long as possible without decreasing your responsiveness below the minimum acceptable limit. Decide how responsive you need to be—and then, if you want to get things done, be no more responsive than that.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“The culprit is the hard responsiveness guarantee. So modern operating systems in fact set a minimum length for their slices and will refuse to subdivide the period any more finely. (In Linux, for instance, this minimum useful slice turns out to be about three-quarters of a millisecond, but in humans it might realistically be at least several minutes.) If more processes are added beyond that point, the period will simply get longer. This means that processes will have to wait longer to get their turn, but the turns they get will at least be long enough to do something.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“But preventive advice for thrashing doesn’t help you when you find yourself in the midst of it. Besides, when it comes to human attention, we’re stuck with what we’ve got. Another way to avert thrashing before it starts is to learn the art of saying no. Denning advocated, for instance, that a system should simply refuse to add a program to its workload if it didn’t have enough free memory to hold its working”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“thrashing” to refer to pretty much any situation where the system grinds to a halt because it’s entirely preoccupied with metawork. A thrashing computer’s performance doesn’t bog down gradually. It falls off a cliff. “Real work” has dropped to effectively zero, which also means it’s going to be nearly impossible to get out.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“thrashing” to refer to pretty much any situation where the system grinds to a halt because it’s entirely preoccupied with metawork.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“At the extreme, a program may run just long enough to swap its needed items into memory, before giving way to another program that runs just long enough to overwrite them in turn. This is thrashing: a system running full-tilt and accomplishing nothing at”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“At the extreme, a program may run just long enough to swap its needed items into memory, before giving way to another program that runs just long enough to overwrite them in turn. This is thrashing:”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“sixteen-hour days are more than twice as productive as eight-hour days. Brian, for his part, thinks of writing as a kind of blacksmithing, where it takes a while just to heat up the metal before it’s malleable. He finds it somewhat useless to block out anything less than ninety minutes for writing, as nothing much happens in the first half hour except loading a giant block of “Now, where was I?” into his head. Scheduling expert Kirk Pruhs, of the University of Pittsburgh, has had the same experience. “If it’s less than an hour I’ll just do errands instead, because it’ll take me the first thirty-five minutes to really figure out what I want to do and then I might not have time to do”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“The hurrieder I go / The behinder I get —NEEDLEPOINT SEEN IN BOONVILLE, CA 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

“the weighted version of Shortest Processing Time is a pretty good candidate for best general-purpose scheduling strategy in the face of uncertainty. It offers a simple prescription for time management: each time a new piece of work comes in, divide its importance by the amount of time it will take to complete. If that figure is higher than for the task you’re currently doing, switch to the new one;”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“If assignments get tossed on your desk at unpredictable moments, the optimal strategy for minimizing maximum lateness is still the preemptive version of Earliest Due Date—switching to the job that just came up if it’s due sooner than the one you’re currently doing, and otherwise ignoring it.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Minimizing maximum lateness (for serving customers in a coffee shop) or the sum of completion times (for rapidly shortening your to-do list) both cross the line into intractability if some tasks can’t be started until a particular time. But they return to having efficient solutions once preemption is allowed. In both cases, the classic strategies—Earliest Due Date and Shortest Processing Time, respectively—remain the best, with a fairly straightforward modification. When a task’s starting time comes, compare that task to the one currently under way. If you’re working by Earliest Due Date and the new task is due even sooner than the current one, switch gears; otherwise stay the course. Likewise, if you’re working by Shortest Processing Time, and the new task can be finished faster than the current one, pause to take care of it first; otherwise, continue with what you were doing.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“So far we have considered only factors that make scheduling harder. But there is one twist that can make it easier: being able to stop one task partway through and switch to another. This property, “preemption,” turns out to change the game dramatically.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“But in 1968, Lawler proved that this is no trouble as long as you build the schedule back to front: look only at the tasks that no other tasks depend on, and put the one with the latest due date at the end of the schedule. Then simply repeat this process, again considering at each step only those tasks that no other (as-yet unscheduled) tasks depend upon as a prerequisite.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Things which matter most must never be at the mercy of things which matter least,” Goethe allegedly proclaimed”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Do the difficult things while they are easy and do the great things while they are small.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Maybe instead we want to minimize the number of foods that spoil. Here a strategy called Moore’s Algorithm gives us our best plan. Moore’s Algorithm says that we start out just like with Earliest Due Date—by scheduling out our produce in order of spoilage date, earliest first, one item at a time. However, as soon as it looks like we won’t get to eating the next item in time, we pause, look back over the meals we’ve already planned, and throw out the biggest item (that is, the one that would take the most days to consume). For instance, that might mean forgoing the watermelon that would take a half dozen servings to eat; not even attempting it will mean getting to everything that follows a lot sooner. We then repeat this pattern, laying out the foods by spoilage date and tossing the largest already scheduled item any time we fall behind. Once everything that remains can be eaten in order of spoilage date without anything spoiling, we’ve got our plan.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions