Algorithms to Live By Quotes

“It turns out there’s no Godfather quite like God the Father.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“The winner of that particular honor is an algorithm called Comparison Counting Sort. In this algorithm, each item is compared to all the others, generating a tally of how many items it is bigger than. This number can then be used directly as the item’s rank. Since it compares all pairs, Comparison Counting Sort is a quadratic-time algorithm, like Bubble Sort. Thus it’s not a popular choice in traditional computer science applications, but it’s exceptionally fault-tolerant. This algorithm’s workings should sound familiar. Comparison Counting Sort operates exactly like a Round-Robin tournament. In other words, it strongly resembles a sports team’s regular season—playing every other team in the division and building up a win-loss record by which they are ranked. That Comparison Counting Sort is the single most robust sorting algorithm known, quadratic or better, should offer something very specific to sports fans: if your team doesn’t make the playoffs, don’t whine. The Mergesort postseason is chancy, but the Comparison Counting regular season is not; championship rings aren’t robust, but divisional standings are literally as robust as it gets. Put differently, if your team is eliminated early in the postseason, it’s tough luck. But if your team fails to get to the postseason, it’s tough truth. You may get sports-bar sympathy from your fellow disappointed fans, but you won’t get any from a computer scientist.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“What the precise balance should be depends on the exact parameters of the situation, but thinking about sorting as valuable only to support future search tells us something surprising: Err on the side of messiness. Sorting something that you will never search is a complete waste; searching something you never sorted is merely inefficient. The question, of course, becomes how to estimate ahead of time what your future usage will be.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“Big-O notation has a particular quirk, which is that it’s inexact by design. That is, rather than expressing an algorithm’s performance in minutes and seconds, Big-O notation provides a way to talk about the kind of relationship that holds between the size of the problem and the program’s running time. Because Big-O notation deliberately sheds fine details, what emerges is a schema for dividing problems into different broad classes.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“From this we might infer that minimizing our pain and suffering when it comes to sorting is all about minimizing the number of things we have to sort. It’s true: one of the best preventives against the computational difficulty of sock sorting is just doing your laundry more often. Doing laundry three times as frequently, say, could reduce your sorting overhead by a factor of nine. Indeed, if Hillis’s roommate stuck with his peculiar procedure but went thirteen days between washes instead of fourteen, that alone would save him twenty-eight pulls from the hamper.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“The algorithm tells them the balance is thirty-seven percent.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“As Carl Sagan put it, “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

“Does arguably the world’s top expert on parking have some kind of secret weapon? He does: “I ride my bike.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“In packet switching, on the other hand, the proliferation of paths in a growing network becomes a virtue: there are now that many more ways for data to flow, so the reliability of the network increases exponentially with its size.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Even in cases where life is too messy for us to expect a strict numerical analysis or a ready answer, using intuitions and concepts honed on the simpler forms of these problems offers us a way to understand the key issues and make progress.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“The more information you gather, the better you’ll know the right opportunity when you see it—but the more likely you are to have already passed it by. So what do you do? How do you make an informed decision when the very act of informing it jeopardizes the outcome? It’s a cruel situation, bordering on paradox.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“But the whole story is subtler than that. 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

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

“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

“behavioral economics has told a very particular story about human beings: that we are irrational and error-prone, owing in large part to the buggy, idiosyncratic hardware of the brain.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“For example, consider restaurant seating policies. Some restaurants have an “open seating” policy, where waiting customers simply hover until a table opens up, and the first to sit down gets the table. Others will take your name, let you have a drink at the bar, and notify you when a table is ready. These approaches to the management of scarce shared resources mirror the distinction in computer science between “spinning” and “blocking.” When a processing thread requests a resource and can’t get it, the computer can either allow that thread to “spin”—to continue checking for the resource in a perpetual “Is it ready yet?” loop—or it can “block”: halt that thread, work on something else, and then come back around whenever the resource becomes free. To a computer scientist, this is a practical tradeoff: weighing the time lost to spinning against the time lost in context switching. But at a restaurant, not all of the resources being traded off are their own. A policy of “spinning” fills empty tables faster, but the CPUs being worn out in the meantime are the minds of their customers, trapped in a tedious but consuming vigilance.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“At the heart of TCP congestion control is an algorithm called Additive Increase, Multiplicative Decrease, or AIMD. Before AIMD kicks in, a new connection will ramp up its transmission rate aggressively: if the first packet is received successfully it sends out two more, if both of those get through it sends out a batch of four, and so on. But as soon as any packet’s ACK does not come back to the sender, the AIMD algorithm takes over. Under AIMD, any fully received batch of packets causes the number of packets in flight not to double but merely to increase by 1, and dropped packets cause the transmission rate to cut back by half (hence the name Additive Increase, Multiplicative Decrease). Essentially, AIMD takes the form of someone saying, “A little more, a little more, a little more, whoa, too much, cut way back, okay a little more, a little more…” Thus it leads to a characteristic bandwidth shape known as the “TCP sawtooth”—steady upward climbs punctuated by steep drops.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“And like computers on the Internet, ants must solve this shared problem without the benefit of a central decision maker, instead developing what Gordon calls “control without hierarchy.” It turns out the ants’ solution is similar, too: a feedback cycle where successful foragers prompt more to leave the nest, while unsuccessful returnees result in a diminishment of foraging activity.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Once you got somewhere you were happy,” he told the Guardian, “you’d be stupid to shake it up any further.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“First, from Hill Climbing: even if you’re in the habit of sometimes acting on bad ideas, you should always act on good ones. Second, from the Metropolis Algorithm: your likelihood of following a bad idea should be inversely proportional to how bad an idea it is. Third, from Simulated Annealing: you should front-load randomness, rapidly cooling out of a totally random state, using ever less and less randomness as time goes on, lingering longest as you approach freezing. Temper yourself—literally.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“In the physical world, you can randomize your vegetables by joining a Community-Supported Agriculture farm, which will deliver a box of produce to you every week. As we saw earlier, a CSA subscription does potentially pose a scheduling problem, but being sent fruits and vegetables you wouldn’t normally buy is a great way to get knocked out of a local maximum in your recipe rotation. Likewise, book-, wine-, and chocolate-of-the-month clubs are a way to get exposed to intellectual, oenophilic, and gustatory possibilities that you might never have encountered otherwise.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“A blind-variation-and-selective-retention process is fundamental to all inductive achievements, to all genuine increases in knowledge, to all increases in fit of system to environment.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Principles of Psychology, James wrote an article in the Atlantic Monthly called “Great Men, Great Thoughts, and the Environment.” The article opens with his thesis: A remarkable parallel, which to my knowledge has never been noticed, obtains between the facts of social evolution and the mental growth of the race, on the one hand, and of zoölogical evolution, as expounded by Mr. Darwin, on the other.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Taking a superior variation always makes sense, but we would only take inferior ones when the die shows, say, a 2 or more. After a while, we’d cool it further by only taking a higher-price change if the die shows a 3 or greater—then 4, then 5. Eventually we’d be mostly hill climbing, making the inferior move just occasionally when the die shows a 6. Finally we’d start going only uphill, and stop when we reached the next local max. 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

“Scott Fitzgerald once wrote that “the test of a first-rate intelligence is the ability to hold two opposing ideas in mind at the same time and still retain the ability to function.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“discrete optimization”—that is, there’s no smooth continuum among its solutions. The salesman goes either to this town or to that one; you’re either at table five or at table six. There are no shades of gray in between.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“For instance, you can relax the traveling salesman problem by letting the salesman visit the same town more than once, and letting him retrace his steps for free. Finding the shortest route under these looser rules produces what’s called the “minimum spanning tree.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Merrill Flood (who, you might recall from chapter 1, is also credited with circulating the first solution to the secretary problem).”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“The idea of keeping around pieces of information that you refer to frequently is so powerful that it is used in every aspect of computation. Processors have caches. Hard drives have caches. Operating systems have caches. Web browsers have caches.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“In some cases, the difference between a model and the real world is literally a matter of life and death. In the military and in law enforcement, for example, repetitive, rote training is considered a key means for instilling line-of-fire skills. The goal is to drill certain motions and tactics to the point that they become totally automatic. But when overfitting creeps in, it can prove disastrous. There are stories of police officers who find themselves, for instance, taking time out during a gunfight to put their spent casings in their”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions