Algorithms to Live By

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. (Location 142)

This type of cost offers a potential explanation for why people stop early when solving a secretary problem in the lab. Seale and Rapoport showed that if the cost of seeing each applicant is imagined to be, for instance, 1% of the value of finding the best secretary, then the optimal strategy would perfectly align with where people actually switched from looking to leaping in their experiment. The mystery is that in Seale and Rapoport’s study, there wasn’t a cost for search. So why might people in the laboratory be acting like there was one? Because for people there’s always a time cost. It doesn’t come from the design of the experiment. It comes from people’s lives. (Location 525)

Tags: mental-models

Note: perhaps we over analyse Perhaps actually we are wired to make great decisions its just our tools for mesuring the quality of a decision are too simplistic

Neil Bearden puts it, “After searching for a while, we humans just tend to get bored. It’s not irrational to get bored, but it’s hard to model that rigorously.” (Location 532)

Intuitively, we think that rational decision-making means exhaustively enumerating our options, weighing each one carefully, and then selecting the best. But in practice, when the clock—or the ticker—is ticking, few aspects of decision-making (or of thinking more generally) are as important as one: when to stop. (Location 543)

When balancing favorite experiences and new ones, nothing matters as much as the interval over which we plan to enjoy them. (Location 620)

“I’m more likely to try a new restaurant when I move to a city than when I’m leaving it,” explains data scientist and blogger Chris Stucchio, a veteran of grappling with the explore/exploit tradeoff in both his work and his life. “I mostly go to restaurants I know and love now, because I know I’m going to be leaving New York fairly soon. Whereas a couple years ago I moved to Pune, India, and I just would eat friggin’ everywhere that didn’t look like it was gonna kill me. (Location 621)

A look into the economics of Hollywood confirms this hunch. Profits of the largest film studios declined by 40% between 2007 and 2011, and ticket sales have declined in seven of the past ten years. As the Economist puts it, “Squeezed between rising costs and falling revenues, the big studios have responded by trying to make more films they think will be hits: usually sequels, prequels, or anything featuring characters with name recognition.” In other words, they’re pulling the arms of the best machines they’ve got before the casino turns them out. (Location 644)

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. (Location 737)

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. (Location 741)

Third, and most specifically, the minimum possible regret—again assuming non-omniscience—is regret that increases at a logarithmic rate with every pull of the handle. Logarithmically (Location 784)

increasing regret means that we’ll make as many mistakes in our first ten pulls as in the following ninety, and as many in our first year as in the rest of the decade combined. (Location 785)

To live in a restless world requires a certain restlessness in oneself. So long as things continue to change, you must never fully cease exploring. (Location 979)

Hence childhood: “Childhood gives you a period in which you can just explore possibilities, and you don’t have to worry about payoffs because payoffs are being taken care of by the mamas and the papas and the grandmas and the babysitters.” (Location 998)

Note: If i get the chance I should explore like a child again

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. (Location 999)

So, should you alphabetize your bookshelves? For most domestic bookshelves, almost none of the conditions that make sorting worthwhile are true. It’s fairly rare that we find ourselves searching for a particular title. The costs of an unsorted search are pretty low: for every book, if we know roughly where it is we can put our hands on it quickly. And the difference between the two seconds it would take to find the book on a sorted shelf and the ten seconds it would take to scan for it on an unsorted one is hardly a deal breaker. We rarely need to find a title so urgently that it’s worth spending preparatory hours up front to shave off seconds later on. What’s more, we search with our quick eyes and sort with slow hands. (Location 1302)

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. Tossing things back on the top of the pile is the very best you can do, shy of knowing the future. In the previous chapter we examined cases where leaving something unsorted was more efficient than taking the time to sort everything; here, however, there’s a very different reason why you don’t need to organize it. You already have. (Location 1799)

“Many people hold the bias that human memory is anything but optimal,” wrote Anderson and Schooler. “They point to the many frustrating failures of memory. However, these criticisms fail to appreciate the task before human memory, which is to try to manage a huge stockpile of memories. In any system responsible for managing a vast data base there must be failures of retrieval. It is just too expensive to maintain access to an unbounded number of items.” This understanding has in turn led to a second revelation about human memory. If these tradeoffs really are unavoidable, and the brain appears to be optimally tuned to the world around it, then what we refer to as the inevitable “cognitive decline” that comes with age may in fact be something else. (Location 1844)

Why don’t they make the whole plane out of that black box stuff? —STEVEN WRIGHT (Location 1853)

Ramscar’s group demonstrated the impact of extra information on human memory by focusing on the case of language. Through a series of simulations, the researchers showed that simply knowing more makes things harder when it comes to recognizing words, names, and even letters. No matter how good your organization scheme is, having to search through more things will inevitably take longer. It’s not that we’re forgetting; it’s that we’re remembering. We’re becoming archives. (Location 1881)

How we spend our days is, of course, how we spend our lives. —ANNIE DILLARD (Location 1902)

Putting off work on a major project by attending instead to various trivial matters can likewise be seen as “the hastening of subgoal completion”—which is another way of saying that procrastinators are acting (optimally!) to reduce as quickly as possible the number of outstanding tasks on their minds. It’s not that they have a bad strategy for getting things done; they have a great strategy for the wrong metric. Working on a computer brings with it an additional hazard when it comes to being conscious and deliberate about our scheduling metrics: the user interface may subtly (or not so subtly) force its own metric upon us. A modern smartphone user, for instance, is accustomed to seeing “badges” hovering over application icons, ominous numbers in white-on-red signaling exactly how many tasks each particular app expects us to complete. If it’s an email inbox blaring the figure of unread messages, then all messages are implicitly being given equal weight. Can we be blamed, then, for applying the unweighted Shortest Processing Time algorithm to the problem—dealing with all of the easiest emails first and deferring the hardest ones till last—to lower this numeral as quickly as possible? Live by the metric, die by the metric. If all tasks are indeed of equal weight, then that’s exactly what we should be doing. But if we don’t want to become slaves to minutiae, then we need to take measures toward that end. This starts with making sure that the single-machine problem we’re solving is the one we want to be solving. (In the case of app badges, if we can’t get them to reflect our actual priorities, and can’t overcome the impulse to optimally reduce any numerical figure thrown in our face, then perhaps the next best thing is simply to turn the badges off.) (Location 2046)

Tags: notion-dark-side

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. (Location 2302)

Laplace was able to prove that this vast spectrum of possibilities could be distilled down to a single estimate, and a stunningly concise one at that. If we really know nothing about our raffle ahead of time, he showed, then after drawing a winning ticket on our first try we should expect that the proportion of winning tickets in the whole pool is exactly 2/3. If we buy three tickets and all of them are winners, the expected proportion of winning tickets is exactly 4/5. In fact, for any possible drawing of w winning tickets in n attempts, the expectation is simply the number of wins plus one, divided by the number of attempts plus two: (w+1)⁄(n+2). This incredibly simple scheme for estimating probabilities is known as Laplace’s Law, and it is easy to apply in any situation where you need to assess the chances of an event based on its history. If you make ten attempts at something and five of them succeed, Laplace’s Law estimates your overall chances to be 6/12 or 50%, consistent with our intuitions. (Location 2420)

Laplace’s Law offers us the first simple rule of thumb for confronting small data in the real world. Even when we’ve made only a few observations—or only one—it offers us practical guidance. Want to calculate the chance your bus is late? The chance your softball team will win? Count the number of times it has happened in the past plus one, then divide by the number of opportunities plus two. And the beauty of Laplace’s Law is that it works equally well whether we have a single data point or millions of them. (Location 2434)

He made the assumption that the moment when he encountered the Berlin Wall wasn’t special—that it was equally likely to be any moment in the wall’s total lifetime. And if any moment was equally likely, then on average his arrival should have come precisely at the halfway point (since it was 50% likely to fall before halfway and 50% likely to fall after). More generally, unless we know better we can expect to have shown up precisely halfway into the duration of any given phenomenon.* And if we assume that we’re arriving precisely halfway into something’s duration, the best guess we can make for how long it will last into the future becomes obvious: exactly as long as it’s lasted already. (Location 2479)

This straightforward reasoning, which Gott named the Copernican Principle, results in a simple algorithm that can be used to make predictions about all sorts of topics. (Location 2486)

In fact, Gott wasn’t even the first to propose something like the Copernican Principle. In the mid-twentieth century, the Bayesian statistician Harold Jeffreys had looked into determining the number of tramcars in a city given the serial number on just one tramcar, and came up with the same answer: double the serial number. And a similar problem had arisen even earlier, during World War II, when the Allies sought to estimate the number of tanks being produced by Germany. Purely mathematical estimates based on captured tanks’ serial numbers predicted that the Germans were producing 246 tanks every month, while estimates obtained by extensive (and highly risky) aerial reconnaissance suggested the figure was more like 1,400. After (Location 2520)

Tags: mental-models

Note: predicting the future

the war, German records revealed the true figure: 245. (Location 2525)

Predicting that a 90-year-old man will live to 180 years seems unreasonable precisely because we go into the problem already knowing a lot about human life spans—and so we can do better. The richer the prior information we bring to Bayes’s Rule, the more useful the predictions we can get out of it. (Location 2529)

Bayes’s Rule tells us that when it comes to making predictions based on limited evidence, few things are as important as having good priors—that is, a sense of the distribution from which we expect that evidence to have come. Good predictions thus begin with having good instincts about when we’re dealing with a normal distribution and when with a power-law distribution. As it turns out, Bayes’s Rule offers us a simple but dramatically different predictive rule of thumb for each. (Location 2556)

And for any power-law distribution, Bayes’s Rule indicates that the appropriate prediction strategy is a Multiplicative Rule: multiply the quantity observed so far by some constant factor. For an uninformative prior, that constant factor happens to be 2, hence the Copernican prediction; in other power-law cases, the multiplier will depend on the exact distribution you’re working with. (Location 2565)

For the grosses of movies, for instance, it happens to be about 1.4. (Location 2568)

When we apply Bayes’s Rule with a normal distribution as a prior, on the other hand, we obtain a very different kind of guidance. Instead of a multiplicative rule, we get an Average Rule: use the distribution’s “natural” average—its single, specific scale—as your guide. For instance, if somebody is younger than the average life span, then simply predict the average; as their age gets close to and then exceeds the average, predict that they’ll live a few years more. Following this rule gives reasonable predictions for the 90-year-old and the 6-year-old: 94 and 77, respectively. (Location 2575)

Between those two extremes, there’s actually a third category of things in life: those that are neither more nor less likely to end just because they’ve gone on for a while. Sometimes things are simply … invariant. The Danish mathematician Agner Krarup Erlang, who studied such phenomena, formalized the spread of intervals between independent events into the function that now carries his name: the Erlang distribution. The shape of this curve differs from both the normal and the power-law: it has a winglike contour, rising to a gentle hump, with a tail that falls off faster than a power-law but more slowly than a normal distribution. (Location 2587)

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. Well, in the cases where one’s up against an Erlang distribution, anyway, that refrain happens to be correct. If a casino card-playing enthusiast tells his impatient spouse, for example, that he’ll quit for the day after hitting one more blackjack (the odds of which are about 20 to 1), he might cheerily predict, “I’ll be done in about twenty more hands!” If, an unlucky twenty hands later, she returns, asking how long he’s going to make her wait now, his answer will be unchanged: “I’ll be done in about twenty more hands!” (Location 2598)

Consider how many times you’ve seen either a crashed plane or a crashed car. It’s entirely possible you’ve seen roughly as many of each—yet many of those cars were on the road next to you, whereas the planes were probably on another continent, transmitted to you via the Internet or television. In the United States, for instance, the total number of people who have lost their lives in commercial plane crashes since the year 2000 would not be enough to fill Carnegie Hall even half full. In contrast, the number of people in the United States killed in car accidents over that same time is greater than the entire population of Wyoming. (Location 2718)

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%. (Location 2722)

If you want to be a good intuitive Bayesian—if you want to naturally make good predictions, without having to think about what kind of prediction rule is appropriate—you need to protect your priors. Counterintuitively, that might mean turning off the news. (Location 2725)

Similarly, the FBI was forced to change its training after agents were found reflexively firing two shots and then holstering their weapon—a standard cadence in training—regardless of whether their shots had hit the target and whether there was still a threat. Mistakes like these are known in law enforcement and the military as “training scars,” and they reflect the fact that it’s possible to overfit one’s own preparation. In one particularly dramatic case, an officer instinctively grabbed the gun out of the hands of an assailant and then instinctively handed it right back—just as he had done time and time again with his trainers in practice. (Location 2873)

Language forms yet another natural Lasso: complexity is punished by the labor of speaking at greater length and the taxing of our listener’s attention span. Business plans get compressed to an elevator pitch; life advice becomes proverbial wisdom only if it is sufficiently concise and catchy. And anything that needs to be remembered has to pass through the inherent Lasso of memory. (Location 2935)

Why in the world would he do that? The story of the Nobel Prize winner and his investment strategy could be presented as an example of human irrationality: faced with the complexity of real life, he abandoned the rational model and followed a simple heuristic. But it’s precisely because of the complexity of real life that a simple heuristic might in fact be the rational solution. (Location 2945)

In contrast, if we look at the way organisms—including humans—evolve, we notice something intriguing: change happens slowly. This means that the properties of modern-day organisms are shaped not only by their present environments, but also by their history. For example, the oddly cross-wired arrangement of our nervous system (the left side of our body controlled by the right side of our brain and vice versa) reflects the evolutionary history of vertebrates. This phenomenon, called “decussation,” is theorized to have arisen at a point in evolution when early vertebrates’ bodies twisted 180 degrees with respect to their heads; whereas the nerve cords of invertebrates such as lobsters and earthworms run on the “belly” side of the animal, vertebrates have their nerve cords along the spine instead. (Location 2980)

Tom had exactly this experience when he became a professor. His first semester, teaching his first class ever, he spent a huge amount of time perfecting his lectures—more than ten hours of preparation for every hour of class. His second semester, teaching a different class, he wasn’t able to put in as much time, and worried that it would be a disaster. But a strange thing happened: the students liked the second class. In fact, they liked it more than the first one. Those extra hours, it turned out, had been spent nailing down nitty-gritty details that only confused the students, and wound up getting cut from the lectures the next time Tom taught the class. The underlying issue, Tom eventually realized, was that he’d been using his own taste and judgment as a kind of proxy metric for his students’. This proxy metric worked reasonably well as an approximation, but it wasn’t worth overfitting—which explained why spending extra hours painstakingly “perfecting” all the slides had been counterproductive. The effectiveness of regularization in all kinds of machine-learning tasks suggests that we can make better decisions by deliberately thinking and doing less. (Location 3011)

the factors we come up with first are likely to be the most important ones, then beyond a certain point thinking more about a problem is not only going to be a waste of time and effort—it will lead us to worse solutions. (Location 3020)

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. (Location 3034)

The idea behind such thought exercises is exactly that of Constraint Relaxation: to make the intractable tractable, to make progress in an idealized world that can be ported back to the real one. 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. (Location 3165)

F. 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.” (Location 3336)

And you don’t need to be Brian Eno to add a little random stimulation to your life. Wikipedia, for instance, offers a “Random article” link, and Tom has been using it as his browser’s default homepage for several years, seeing a randomly selected Wikipedia entry each time he opens a new window. While this hasn’t yet resulted in any striking discoveries, he now knows a lot about some obscure topics (such as the kind of knife used by the Chilean armed forces) and he feels that some of these have enriched his life. (For example, he’s learned that there is a word in Portuguese for a “vague and constant desire for something that does not and probably cannot exist,” a problem we still can’t solve with a search engine.) An interesting side effect is that he now also has a better sense not just of what sorts of topics are covered on Wikipedia, but also of what randomness really looks like. For example, pages that feel like they have some connection to him—articles about people or places he knows—show up with what seems like surprising frequency. (Location 3678)

Protocol is how we get on the same page; in fact, the word is rooted in the Greek protokollon, “first glue,” which referred to the outer page attached to a book or manuscript. (Location 3743)

The technology that ate circuit switching’s lunch would become known as packet switching. In a packet-switched network, rather than using a dedicated channel for each connection, senders and receivers atomize their messages into tiny shards known as “packets,” and merge them into the communal flow of data—a bit like postcards moving at the speed of light. (Location 3770)

In human society, we tend to adopt a policy of giving people some finite number of chances in a row, then giving up entirely. Three strikes, you’re out. This pattern prevails by default in almost any situation that requires forgiveness, lenience, or perseverance. Simply put, maybe we’re doing it wrong. (Location 3904)

Note: How does this link with the dip

We’ve all had the experience of talking to someone whose eyes drifted away—to their phone, perhaps—making us wonder whether our lackluster storytelling was to blame. In fact, it’s now clear that the cause and effect are often the reverse: a poor listener destroys the tale. (Location 4022)

The most prevalent critique of modern communications is that we are “always connected.” But the problem isn’t that we’re always connected; we’re not. The problem is that we’re always buffered. The difference is enormous. The feeling that one needs to look at everything on the Internet, or read all possible books, or see all possible shows, is bufferbloat. You miss an episode of your favorite series and watch it an hour, a day, a decade later. You go on vacation and come home to a mountain of correspondence. It used to be that people knocked on your door, got no response, and went away. Now they’re effectively waiting in line when you come home. (Location 4109)

Tags: mental-models

Note: will be a good video on bhfferbloat

Vacation email autoresponders explicitly tell senders to expect latency; a better one might instead tell senders to expect Tail Drop. Rather than warning senders of above-average queue times, it might warn them that it was simply rejecting all incoming messages. (Location 4124)

And this doesn’t need to be limited to vacations: one can imagine an email program set to auto-reject all incoming messages once the inbox reached, say, a hundred items. This is ill-advised for bills and the like, but not an unreasonable approach to, say, social invitations. (Location 4126)

Schoolchildren are taught to conceive of literary plots as belonging to one of several categories: man vs. nature, man vs. self, man vs. man, man vs. society. (Location 4165)

Simply put, any time a system—be it a machine or a mind—simulates the workings of something as complex as itself, it finds its resources totally maxed out, more or less by definition. (Location 4196)

In one of the seminal results in game theory, the mathematician John Nash proved in 1951 that every two-player game has at least one equilibrium. This major discovery would earn Nash the Nobel Prize in Economics in 1994 (and lead to the book and film A Beautiful Mind, about Nash’s life). (Location 4240)

Such an equilibrium is now often spoken of as the “Nash equilibrium”—the “Nash” that Dan Smith always tries to keep track (Location 4243)

But from a congestion standpoint, the fact that anarchy is only 4/3 as congested as perfect coordination means that perfectly coordinated commutes will only be 3/4 as congested as they are now. (Location 4313)

At the present moment, the Bay Area (where the two of us live) is attempting to remedy this sorry state of affairs by going through a radical paradigm shift when it comes to vacation policy—a shift that is very well meaning and completely, apocalyptically doomed. The premise sounds innocent enough: instead of metering out some fixed arbitrary number of days for each employee, then wasting HR man-hours making sure no one goes over their limit, why not just let your employees free? Why not simply allow them unlimited vacation? Anecdotal reports thus far are mixed—but from a game-theoretic perspective, this approach is a nightmare. All employees want, in theory, to take as much vacation as possible. But they also all want to take just slightly less vacation than each other, to be perceived as more loyal, more committed, and more dedicated (hence more promotion-worthy). Everyone looks to the others for a baseline, and will take just slightly less than that. The Nash equilibrium of this game is zero. As the CEO of software company Travis CI, Mathias Meyer, writes, “People will hesitate to take a vacation as they don’t want to seem like that person who’s taking the most vacation days. It’s a race to the bottom.” This is the tragedy of the commons in full effect. And it’s just as bad between firms as within them. Imagine two shopkeepers in a small town. Each of them can choose either to stay open seven days a week or to be open only six days a week, taking Sunday off to relax with their friends and family. If both of them take a day off, they’ll retain their existing market share and experience less stress. However, if one shopkeeper decides to open his shop seven days a week, he’ll draw extra customers—taking them away from his competitor and threatening his livelihood. The Nash equilibrium, again, is for everyone to work all the time. This exact issue became a flash point in the United States during the 2014 holid...

...ay season, as retailer after retailer, unwilling to cede market share to competitors who were getting ahead of the usual post-Thanksgiving shopping rush, caved in toward the lousy equilibrium. “Stores are opening earlier than ever before,” the International Business Times reported. Macy’s decided to open two hours earlier than the year before, as did Target. Kmart, for its part, opened at 6:00 a.m. on Thanksgiving morning, and was continuously open for forty-two hours. So what can we, as players, do when we find ourselves in such a situation—either the two-party prisoner’s dilemma, or the multi-party tragedy of the commons? In a sense, nothing. The very stability that these bad equilibria have, the thing that makes them equilibria, becomes damnable. By and large we cannot shift the dominant strategies from within. But this doesn’t mean that bad equilibria can’t be fixed. It just means that the solution is going to have to come from somewhere else. (Location 4341)

Tags: mental-models

Note: nash eqilibrium

On the other hand, a change to the game’s payoffs that doesn’t change the equilibrium will typically have a much smaller effect than desired. The CEO of the software firm Evernote, Phil Libin, made headlines with a policy of offering Evernote employees a thousand dollars cash for taking a vacation. This sounds like a reasonable approach to getting more employees to take vacation, but from a game-theoretic perspective it’s actually misguided. Increasing the cash on the table in the prisoner’s dilemma, for instance, misses the point: the change doesn’t do anything to alter the bad equilibrium. If a million-dollar heist ends up with both thieves in jail, so does a ten-million-dollar heist. The problem isn’t that vacations aren’t attractive; the problem is that everyone wants to take slightly less vacation than their peers, producing a game whose only equilibrium is no vacation at all. (Location 4387)

Does this mean that Libin needs to offer tens of thousands of dollars per employee per vacation? No. Mechanism design tells us that Libin can get the happy employees he wants with the stick, rather than the carrot; he can get a better equilibrium without spending a dime. For instance, he could simply make a certain minimal amount of vacation compulsory. If he can’t change the race, he can still change the bottom. Mechanism design makes a powerful argument for the need for a designer—be it a CEO, a contract binding all parties, or a don who enforces omertà by garroted carotid. (Location 4396)

league commissioner is this kind of a designer as well. Imagine how pathetic a sight the NBA would be if there were no games as such, and teams could simply score on each other at literally any time between the start and end of the season: 3:00 a.m. on a Sunday, noon on Christmas, you name it. What you’d see would be haggard, cadaverous players, in extreme sleep debt, forcing vigilance with chemical stimulants, almost losing their minds. War is like this. On the other hand, even Wall Street, ruthless cutthroat capitalists trading by the microsecond in the “city that never sleeps,” comes to a cease-fire every day at 4:00 p.m. sharp, so that brokers can sleep at predictable hours every night without getting too badly ambushed by competitors pushing toward a sleepless equilibrium. In this sense, the stock market is more a sport than a war. (Location 4400)

The canopy can be thought of as an aerial meadow, just like a rolling grassland prairie, but raised on stilts. The canopy is gathering solar energy at much the same rate as a grassland prairie would. But a substantial portion of the energy is “wasted” by being fed straight into the stilts, which do nothing more useful than loft the “meadow” high in the air, where it picks up exactly the same harvest of photons as it would—at (Location 4429)

far lower cost—if it were laid flat on the ground. (Location 4432)

“Morality is herd instinct in the individual,” wrote Nietzsche. (Location 4458)

French existentialist philosopher Jean-Paul Sartre famously wrote that “Hell is other people.” He didn’t mean that others are inherently malicious or unpleasant, but rather that they complicate our own thoughts and beliefs: When we think about ourselves, when we try to know ourselves … we use the knowledge of us which other people already have. We judge ourselves with the means other people have and have given us for judging ourselves. Into whatever I say about myself someone else’s judgment always enters. Into whatever I feel within myself someone else’s judgment enters.… But that does not at all mean that one cannot have relations with other people. It simply brings out the capital importance of all other people for each one of us. Perhaps, (Location 4635)

I firmly believe that the important things about humans are social in character and that relief by machines from many of our present demanding intellectual functions will finally give the human race time and incentive to learn how to live well together. —MERRILL FLOOD (Location 4656)

Note: But what if machines end up detracting from the social

Outcomes make news headlines—indeed, they make the world we live in—so it’s easy to become fixated on them. But processes are what we have control over. As Bertrand Russell put it, “it would seem we must take account of probability in judging of objective rightness.… (Location 4670)