Level Graph Quotes

What the ethnographer is in fact faced with—except when (as, of course, he must do) he is pursuing the more automatized routines of data collection—is a multiplicity of complex conceptual structures, many of them superimposed upon or knotted into one another, which are at once strange, irregular, and inexplicit, and which he must contrive somehow first to grasp and then to render. And this is true at the most down-to-earth, jungle field work levels of his activity; interviewing informants, observing rituals, eliciting kin terms, tracing property lines, censusing households … writing his journal. Doing ethnography is like trying to read (in the sense of “construct a reading of”) a manuscript—foreign, faded, full of ellipses, incoherencies, suspicious emendations, and tendentious commentaries, but written not in conventionalized graphs of sound but in transient examples of shaped behavior.

a Croatian seismologist named Andrija Mohorovičić was studying graphs from an earthquake in Zagreb when he noticed a similar odd deflection, but at a shallower level. He had discovered the boundary between the crust and the layer immediately below, the mantle; this zone has been known ever since as the Mohorovičić discontinuity, or Moho for short.

People changed and they didn’t. People evolved and they didn’t. Alice imagined a graph that showed how much people’s personalities shifted after high school on one axis and on the other, how many miles away from home they had moved. It was easy to stay the same when you were looking at the same walls. Layered on top would be how easy your life was along the way, how many levels of privilege surrounded you like a tiny glass object in a sea of packing peanuts.

Listen, Google,’ I will say, ‘both John and Paul are courting me. I like both of them, but in a different way, and it’s so hard to make up my mind. Given everything you know, what do you advise me to do?’ And Google will answer: ‘Well, I know you from the day you were born. I have read all your emails, recorded all your phone calls, and know your favourite films, your DNA and the entire history of your heart. I have exact data about each date you went on, and if you want, I can show you second-by-second graphs of your heart rate, blood pressure and sugar levels whenever you went on a date with John or Paul. If necessary, I can even provide you with accurate mathematical ranking of every sexual encounter you had with either of them. And naturally enough, I know them as well as I know you. Based on all this information, on my superb algorithms, and on decades’ worth of statistics about millions of relationships – I advise you to go with John, with an 87 per cent probability of being more satisfied with him in the long run. Indeed, I know you so well that I also know you don’t like this answer. Paul is much more handsome than John, and because you give external appearances too much weight, you secretly wanted me to say “Paul”. Looks matter, of course; but not as much as you think. Your biochemical algorithms – which evolved tens of thousands of years ago in the African savannah – give looks a weight of 35 per cent in their overall rating of potential mates. My algorithms – which are based on the most up-to-date studies and statistics – say that looks have only a 14 per cent impact on the long-term success of romantic relationships. So, even though I took Paul’s looks into account, I still tell you that you would be better off with John.

I had a firm policy never to charge up my team on an emotional level. I believe that for every artificial peak you may create there is a valley, and i don't like valleys. Games can be lost in valleys. The ideal is an ever-mounting graph line that peaks with your final performance. There will be difficulty and adversity to overcome, but that is necessary to become stronger. Other coaches believe in charging a team up. I never did and never will. I sought a calm assurances in our dressing room, and a calm assurance warming up on the floor, and ad calm assurance in my final remarks before going out to play.

One day, Carmona had an idea. Axcom had been employing various approaches to using their pricing data to trade, including relying on breakout signals. They also used simple linear regressions, a basic forecasting tool relied upon by many investors that analyzes the relationships between two sets of data or variables under the assumption those relationships will remain linear. Plot crude-oil prices on the x-axis and the price of gasoline on the y-axis, place a straight regression line through the points on the graph, extend that line, and you usually can do a pretty good job predicting prices at the pump for a given level of oil price.

Temperatures, petrol prices, the price of the dollar: the golden triangle of our summer. These are facts beyond our control and all we hope now is to see them all rising indefinitely. Sometimes the figures are mixed up in a prophetic confusion, as in 1980 in the US deserts. There, the price per gallon: 51.18, 51.20, 51 .25, varied from one place to another as an exact reflection of the temperature graphs: 100, 110 and 120 degrees Fahrenheit. With the question of confidence always lurking just beneath the surface: what price would you accept petrol rising to? What point do you think the dollar could go up to (with the implication: before causing a crash in world economies)? What record level can the heat reach (before causing a volatilization of energy and the beginnings of a worldwide insomnia)? Our artificial destiny is written in these asymptotic curves.

Could it be that, once a society achieves a certain high level of equality, beyond that point greater equality merely leads to diminishing happiness returns? As has been proved with measures of wealth, once people have enough equality to cover their basic needs, greater equality does not necessarily result in corresponding increases in happiness. Could this explain why the Danes were judged to be the happiest people on earth, even though they were not the most equal? “Colleagues at Harvard think there might be a leveling-off in terms of some health aspects,” said the professor. “But if you look at our graphs, where we put all our sources together in terms of all health and societal problems, there is no sign of it leveling off at the other end. It is a linear relationship. My view is that we don’t know what happens if you get more equal than Sweden.” Ultimately,

Or, stated in a familiar way, increasing cognitive load* should make people more conservative. This is precisely the case. The time pressure of snap judgments is a version of increased cognitive load. Likewise, people become more conservative when tired, in pain or distracted with a cognitive task, or when blood alcohol levels rise. Recall from chapter 3 that willpower takes metabolic power, thanks to the glucose demands of the frontal cortex. This was the finding that when people are hungry, they become less generous in economic games. A real-world example of this is startling (see graph on previous page)—in a study of more than 1,100 judicial rulings, prisoners were granted parole at about a 60 percent rate when judges had recently eaten, and at essentially a 0 percent rate just before judges ate (note also the overall decline over the course of a tiring day). Justice may be blind, but she’s sure sensitive to her stomach gurgling.

Fifteen of his clubs, dedicated to politics, music, and the performing arts, had all been developing strategic plans for the past two years. And the local branches of various societies--whose goals were to advance aviation, knowledge of chemistry, automotive transportation, equestrian sports, highway construction, as well as the prompt eradication of ethnic chauvinism--existed only in the sick imagination of the local union committee. As for the school of continuing education, of which Sardinevich was especially proud, it was constantly reorganizing itself, which, as anybody knows, means it wasn't undertaking any useful activity whatsoever. If Sardinevich were an honest man, he would probably have admitted that all these activities were essentially a mirage. But the local union committee used this mirage to concoct its reports, so at the next level up nobody doubted the existence of all those musico-political clubs. At that level, the school of continuing education was imagined as a large stone building filled with desks, where perky teachers draw graphs that show the rise of unemployment in the United States on their chalkboards, while mustachioed students develop political consciousness right in front of your eyes.

In short, the combined effects of lower infant mortality, higher longevity, and increased fertility have fueled an explosion in the world’s population, as figure 18 graphs. Since population growth is intrinsically exponential, even small increases in fertility or decreases in mortality spark rapid population growth. If an initial population of 1 million people grows at 3.5 percent per year, then it will roughly double every generation, growing to 2 million in twenty years, 4 million in forty years, and so on, reaching 32 million in a hundred years. In actual fact, the global growth rate peaked in 1963 at 2.2 percent per year and has since declined to about 1.1 percent per year,60 which translates into a doubling rate of every sixty-four years. In the fifty years between 1960 and 2010, the world’s population more than doubled, from 3 to 6.9 billion people. At current rates of growth, we can expect 14 billion people at the end of this century. FIGURE 21. The demographic transition model. Following economic development, death rates tend to fall before birth rates decrease, resulting in an initial population boom that eventually levels off. This controversial model, however, only applies to some countries. One major by-product of population growth plus the concentration of wealth in cities has been a shift to more urbanization. In 1800, only 25 million people lived in cities, about 3 percent of the world’s population. In 2010, about 3.3 billion people, half the world’s population, are city dwellers.

Listen, Google,’ I will say, ‘both John and Paul are courting me. I like both of them, but in different ways, and it’s so hard to make up my mind. Given everything you know, what do you advise me to do?’ And Google will answer: ‘Well, I’ve known you from the day you were born. I have read all your emails, recorded all your phone calls, and know your favourite films, your DNA and the entire biometric history of your heart. I have exact data about each date you went on and, if you want, I can show you second-by-second graphs of your heart rate, blood pressure and sugar levels whenever you went on a date with John or Paul. If necessary, I can even provide you with an accurate mathematical ranking of every sexual encounter you had with either of them. And naturally, I know them as well as I know you. Based on all this information, on my superb algorithms, and on decades’ worth of statistics about millions of relationships –I advise you to go with John, with an 87 per cent probability that you will be more satisfied with him in the long run. ‘Indeed, I know you so well that I also know you don’t like this answer. Paul is much more handsome than John, and because you give external appearances too much weight, you secretly wanted me to say “Paul”. Looks matter, of course; but not as much as you think. Your biochemical algorithms –which evolved tens of thousands of years ago on the African savannah –give looks a weight of 35 per cent in their overall rating of potential mates. My algorithms –which are based on the most up-to-date studies and statistics –say that looks have only a 14 per cent impact on the long-term success of romantic relationships. So, even though I took Paul’s looks into account, I still tell you that you would be better off with John.

The fact is that the estimate of fatalities, in terms of what was calculable at that time—even before the discovery of nuclear winter—was a fantastic underestimate. More than forty years later, Dr. Lynn Eden, a scholar at Stanford’s Center for International Security and Cooperation, revealed in Whole World on Fire71 the bizarre fact that the war planners of SAC and the Joint Chiefs—throughout the nuclear era to the present day—have deliberately omitted entirely from their estimates of the destructive effects of U.S. or Russian nuclear attacks the effects of fire. They have done so on the questionable grounds that these effects are harder to predict than the effects of blast or fallout, on which their estimates of fatalities are exclusively based, even though, as Eden found, experts including Hal Brode have disputed such conclusions for decades. (A better hypothesis for the tenacious lack of interest is that accounting for fire would reduce the number of USAF warheads and vehicles required to achieve the designated damage levels: which were themselves set high enough to preclude coverage by available Navy submarine-launched missiles.) Yet even in the sixties the firestorms caused by thermonuclear weapons were known to be predictably the largest producers of fatalities in a nuclear war. Given that for almost all strategic nuclear weapons, the damage radius of firestorms would be two to five times the radius destroyed by the blast, a more realistic estimate of the fatalities caused directly by the planned U.S. attacks on the Sino-Soviet bloc, even in 1961, would surely have been double the summary in the graph I held in my hand, for a total death toll of a billion or more: a third of the earth’s population, then three billion. Moreover, what no one would recognize for another twenty-two years were the indirect effects of our planned first strike that gravely threatened the other two thirds of humanity. These effects arose from another neglected consequence of our attacks on cities: smoke. In effect, in ignoring fire the Chiefs and their planners ignored that where there’s fire there’s smoke. But what is dangerous to our survival is not the smoke from ordinary fires, even very large ones—smoke that remained in the lower atmosphere and would soon be rained out—but smoke propelled into the upper atmosphere from the firestorms that our nuclear weapons were sure to create in the cities we targeted. (See chapter 16.) Ferocious updrafts from these multiple firestorms would loft millions of tons of smoke and soot into the stratosphere, where it would not be rained out and would quickly encircle the globe, forming a blanket blocking most sunlight around the earth for a decade or more. This would reduce sunlight and lower temperatures72 worldwide to a point that would eliminate all harvests and starve to death—not all but nearly all—humans (and other animals that depend on vegetation for food). The population of the southern hemisphere—spared nearly all direct effects from nuclear explosions, even from fallout—would be nearly annihilated, as would that of Eurasia (which the Joint Chiefs already foresaw, from direct effects), Africa, and North America. In a sense the Chiefs

How,” the Autumn King asked no one in particular. “How?” It was the ancient Prime of the wolves who answered, his withered voice rising above the pinging of the graph. “With the strength of the most powerful force in the world. The most powerful force in any realm.” He pointed to the screen. “What brings loyalty beyond death, undimming despite the years. What remains unwavering in the face of hopelessness.” The Autumn King twisted toward the ancient Prime, shaking his head. Still not understanding. Bryce was at the level of ordinary witches now. But still too far from life. Motion caught Declan’s eye, and he whirled toward the feed of the Old Square. Wreathed in lightning, healed and whole, Hunt Athalar was kneeling over Bryce’s dead body. Pumping her torso with his hands—chest compressions. Hunt hissed to Bryce through his gritted teeth, thunder cracking above him, “I heard what you said.” Pump, pump, pump went his powerful arms. “What you waited to admit until I was almost dead, you fucking coward.” His lightning surged into her, sending her body arcing off the ground as he tried to jump-start her heart. He snarled in her ear, “Now come say it to my face.” Sabine whispered a sentence to the room, to the Autumn King, and Declan’s heart rose, hearing it. It was the answer to the ancient Prime’s words. To the Autumn King’s question of how, against every statistic blaring on Declan’s computer, they were even witnessing Hunt Athalar fight like Hel to keep Bryce Quinlan’s heart beating. Through love, all is possible.

It was the ancient Prime of the wolves who answered, his withered voice rising above the pinging of the graph. “With the strength of the most powerful force in the world. The most powerful force in any realm.” He pointed to the screen. “What brings loyalty beyond death, undimming despite the years. What remains unwavering in the face of hopelessness.” The Autumn King twisted toward the ancient Prime, shaking his head. Still not understanding. Bryce was at the level of ordinary witches now. But still too far from life. Motion caught Declan’s eye, and he whirled toward the feed of the Old Square. Wreathed in lightning, healed and whole, Hunt Athalar was kneeling over Bryce’s dead body. Pumping her torso with his hands—chest compressions. Hunt hissed to Bryce through his gritted teeth, thunder cracking above him, “I heard what you said.” Pump, pump, pump went his powerful arms. “What you waited to admit until I was almost dead, you fucking coward.” His lightning surged into her, sending her body arcing off the ground as he tried to jump-start her heart. He snarled in her ear, “Now come say it to my face.” Sabine whispered a sentence to the room, to the Autumn King, and Declan’s heart rose, hearing it. It was the answer to the ancient Prime’s words. To the Autumn King’s question of how, against every statistic blaring on Declan’s computer, they were even witnessing Hunt Athalar fight like Hel to keep Bryce Quinlan’s heart beating. Through love, all is possible.

Stories can be incredibly powerful and beautiful devices that form and assist our perception and understanding of the world. However, according to twentieth-century American author Kurt Vonnegut, stories rarely tell the truth. After studying stories from an anthropological standpoint, examining the relationships with various cultures, Vonnegut found that stories and myths across many cultures share consistent similar shapes that can typically be broken down into just a few main categories. These shapes can be found graphing the course of a protagonist’s journey through a story along an axis of good and ill fortune. In all stories, someone or something starts somewhere, either in a good place, bad place, or neutral place. Then things happen related to that person which is conveyed as good or bad, bringing the character up and down the axis of fortune as they traverse forward through the story. Then, the story ends and its shape reveals itself. Vonnegut discovered that many popular stories follow common, consistent curves and spikes up and down the good/ill axis and that most end with the protagonist higher on the axis than where they started. However, what’s perhaps most interesting about Vonnegut’s analysis is this argument that these shapes, and consequently most stories, lie. Vonnegut proposed that a more honest, realistic story shape is simply a straight line. In a story of this shape, things still happen and characters still change, but the story maintains ambiguity around whether or not the events that occur are conclusively good or bad. According to Vonnegut, Hamlet is the closest literary representation of real life. “We are so seldom told the truth. In Hamlet-Shakespeare tells us that we don’t know enough about life to know what the good news is and the bad news is and we respond to that.” One story medium that seems to inadvertently coincide with this idea, is the medium of the television series. The goal of TV series is to keep viewers watching as long as possible. Each episode must be an engaging enough story to keep the viewer watching until the end, but each episode must also be left unresolved enough so the larger season-long and series-long stories continue and the viewer is interested in watching all the following episodes. In order to keep the whole thing going, none of the stories can reach a conclusion, and thus, the main characters can’t find ultimate peace or freedom from the uncertainty between good and ill-fortune. Of course, most shows don’t qualify as the straight-line shape in Vonnegut’s analysis, because most shows attempt to convey conclusively good and bad fortunes within them. However merely by the requirements of the medium TV series are forced to self-impose the same sort of universal truth that Vonnegut suggests. That neither the viewer nor the characters in a series can ever know what anything that’s so-called “good” or “bad” in one episode might cause in the next. And that on a fundamental level, the changes in each episode are futile because they are a part of a never-ending cycle of change through conflict and resolution, for the mere sake of its continuation, with no aim of a final resolution or reveal of what’s ultimately good or bad. Of course, eventually, a show reaches its series end when it stops working or runs its natural course. But the show fights its whole life to stay away from this moment. A good TV series, a series that we don’t want to end, is only a series that we don’t want to end because it can’t seem to resolve itself. In this, the format of Tv series also shows us that there is meaning, engagement, and entertainment within the endless cycle of change, regardless of its potential universal futility. And that perhaps change in life can exist not for the sake of some conclusion or ultimate state of peace, but a continuation of itself for the sake of itself. And perhaps the ability to be in this cycle of continued change for the sake of change is the actual good fortune.

The totalitarian regimes of the 20th century give us the starkest examples of such insanity. Stalin persecuted genetics researchers in the 1930s and ostentatiously praised the scientist Trofim Lysenko when he claimed that genetics was a “bourgeois perversion” and geneticists were “saboteurs”. The resulting crop failures killed millions. For an encore, Stalin ordered the killing of the statistician in charge of the 1937 census, Olimpiy Kvitkin. Kvitkin’s crime was that his census revealed a fall in population as a result of that famine. Telling that truth could not be forgiven. In May, the great crop scientist Yuan Longping died at the age of 90. He led the research effort to develop the hybrid rice crops that now feed billions of people. Yet in 1966, he too came very close to being killed as a counter-revolutionary during China’s cultural revolution. In western democracies we do things differently. Governments do not execute scientists; they sideline them. Late last year, Undark magazine interviewed eight former US government scientists who had left their posts in frustration or protest at the obstacles placed in their way under the presidency of Donald Trump. Then there are the random acts of hostility on the street and the death threats on social media. I have seen Twitter posts demanding that certain statisticians be silenced or hunted down and destroyed, sometimes for doing no more than publishing graphs of Covid-19 cases and hospitalisations. Even when this remains at the level of ugly intimidation, it is horrible to hear about and must be far worse to experience. It is not something we should expect a civil servant, a vaccine researcher or a journalist to have to endure. And it would be complacent to believe that the threats are always empty.

Over the years, Facebook has executed an effective playbook that does exactly this, at scale. Take Instagram as an example—in the early days, the core product tapped into Facebook’s network by making it easy to share photos from one product to the other. This creates a viral loop that drives new users, but engagement, too, when likes and comments appear on both services. Being able to sign up to Instagram using your Facebook account also increases conversion rate, which creates a frictionless experience while simultaneously setting up integrations later in the experience. A direct approach to tying together the networks relies on using the very established social graph of Facebook to create more engagement. Bangaly Kaba, formerly head of growth at Instagram, describes how Instagram built off the network of its larger parent: Tapping into Facebook’s social graph became very powerful when we realized that following your real friends and having an audience of real friends was the most important factor for long-term retention. Facebook has a very rich social graph with not only address books but also years of friend interaction data. Using that info supercharged our ability to recommend the most relevant, real-life friends within the Instagram app in a way we couldn’t before, which boosted retention in a big way. The previous theory had been that getting users to follow celebrities and influencers was the most impactful action, but this was much better—the influencers rarely followed back and engaged with a new user’s content. Your friends would do that, bringing you back to the app, and we wouldn’t have been able to create this feature without Facebook’s network. Rather than using Facebook only as a source of new users, Instagram was able to use its larger parent to build stronger, denser networks. This is the foundation for stronger network effects. Instagram is a great example of bundling done well, and why a networked product that launches another networked product is at a huge advantage. The goal is to compete not just on features or product, but to always be the “big guy” in a competitive situation—to bring your bigger network as a competitive weapon, which in turn unlocks benefits for acquisition, engagement, and monetization. Going back to Microsoft, part of their competitive magic came when they could bring their entire ecosystem—developers, customers, PC makers, and others—to compete at multiple levels, not just on building more features. And the most important part of this ecosystem was the developers.

Figure 2.1 Cortical connections over two years adapted from Conel The top row shows the baby’s cortex at birth, then at one month and at three months. They all look about the same, don’t they? But look what happens at six months (bottom left box): the number of cell bodies remains the same, but the number of connections has multiplied exponentially. The connections grow so quickly in the first three years of life that neuroscientists call it neural exuberance. Neural exuberance! The name is well earned: The baby’s brain makes 24 million new connections every minute, and this continues for the first three years of life. Each neuron may be connected to 1,000 other neurons — that multiplies out to 100 trillion possible connections between neurons, more than the number of stars in the universe. This high level of connectivity between brain cells leads to the cortex of a three-year-old being twice as thick as an adult’s! As connections are created, new abilities emerge. For example, when connections grow in Broca’s area — speech production — around six months, then children begin to speak. Around nine months of age, the frontal areas (behind the forehead) become more interconnected, and that’s when most children develop object permanence: knowing that objects continue to exist even when they are out of sight. Before object permanence develops, when Mom is out of sight she’s no longer in the baby’s universe. This is why young babies are inconsolable when Mom leaves. Once they start to develop object permanence, babies can hold on to an internal image of Mom. This is about the age that babies play peek-a-boo. Mom disappears when she puts the blanket over her head, but the nine-month-old knows Mom’s still there even if he can’t see her. The infant tests his “knowledge” when he pulls the blanket off and sees — sure enough! —Mom really is there! What is the use of so many brain connections in the first three years of life? These connections are ready-made highways for information to travel along. The toddlers’ ability to quickly adapt and learn is possible because they have a vast number of brain connections available for making sense of the world. Thanks to neural exuberance, the child does not need to create connections on the spur of the moment to make meaning of each new experience; myriad connections are already there. Pruning of connections The number of connections remains high from age 3 until age 10, when the process of neural pruning begins. Connections that are being used remain; others get absorbed back into the neuron. It’s similar to pruning a bush. After pruning, individual branches get thicker, fruit is more abundant, and the whole bush gets fuller. This seems a little counter-intuitive, but pruning works because it allows the plant’s limited resources to go to its strongest parts; water and nutrients are no longer wasted on spindly branches and dried-out roots. Similarly, when unused brain connections are pruned, neural resources are more available for brain areas that are being used. This results in a more useful and efficient brain that’s tailor-made to meet each individual’s needs. This process of pruning occurs in all brain areas. Figure 2.2 presents findings published by Sowell and associates. They measured Magnetic Resonance Imaging in 176 normal subjects from age 7 to 87 years. The x-axes in these graphs present years from 10 to 90 years. Notice there is a common pattern of decreasing connections in all brain areas. In some brain areas this change is steeper, such as in frontal areas, but is flatter in other areas such as temporal areas in the left hemisphere.

Moreover, these changes occurred when most American households actually found their real incomes stagnant or declining. Median household income for the last four decades is shown in the chart above. But this graph, disturbing as it is, conceals a far worse reality. The top 10 percent did much better than everyone else; if you remove them, the numbers change dramatically. Economic analysis has found that “only the top 10 percent of the income distribution had real compensation growth equal to or above . . . productivity growth.”14 In fact, most gains went to the top 1 percent, while people in the bottom 90 percent either had declining household incomes or were able to increase their family incomes only by working longer hours. The productivity of workers continued to grow, particularly with the Internet revolution that began in the mid-1990s. But the benefits of productivity growth went almost entirely into the incomes of the top 1 percent and into corporate profits, both of which have grown to record highs as a fraction of GNP. In 2010 and 2011 corporate profits accounted for over 14 percent of total GNP, a historical record. In contrast, the share of US GNP paid as wages and salaries is at a historical low and has not kept pace with inflation since 2006.15 As I was working on this manuscript in late 2011, the US Census Bureau published the income statistics for 2010, when the US recovery officially began. The national poverty rate rose to 15.1 percent, its highest level in nearly twenty years; median household income declined by 2.3 percent. This decline, however, was very unequally distributed. The top tenth experienced a 1 percent decline; the bottom tenth, already desperately poor, saw its income decline 12 percent. America’s median household income peaked in 1999; by 2010 it had declined 7 percent. Average hourly income, which corrects for the number of hours worked, has barely changed in the last thirty years. Ranked by income equality, the US is now ninety-fifth in the world, just behind Nigeria, Iran, Cameroon, and the Ivory Coast. The UK has mimicked the US; even countries with low levels of inequality—including Denmark and Sweden—have seen an increasing gap since the crisis. This is not a distinguished record. And it’s not a statistical fluke. There is now a true, increasingly permanent underclass living in near-subsistence conditions in many wealthy states. There are now tens of millions of people in the US alone whose condition is little better than many people in much poorer nations. If you add up lifetime urban ghetto residents, illegal immigrants, migrant farm-workers, those whose criminal convictions sharply limit their ability to find work, those actually in prison, those with chronic drug-abuse problems, crippled veterans of America’s recently botched wars, children in foster care, the homeless, the long-term unemployed, and other severely disadvantaged groups, you get to tens of millions of people trapped in very harsh, very unfair conditions, in what is supposedly the wealthiest, fairest society on earth. At any given time, there are over two million people in US prisons; over ten million Americans have felony records and have served prison time for non-traffic offences. Many millions more now must work very long hours, and very hard, at minimum-wage jobs in agriculture, retailing, cleaning, and other low-wage service industries. Several million have been unemployed for years, exhausting their savings and morale. Twenty or thirty years ago, many of these people would have had—and some did have—high-wage jobs in manufacturing or construction. No more. But in addition to growing inequalities in income and wealth, America exhibits

Of the various factors that make osteoporosis a modern mismatch disease, one of the biggest is physical activity, whose beneficial effects on bone health are difficult to exaggerate. First, because the skeleton mostly forms before one’s early twenties, lots of weight-bearing activity during youth—especially during puberty—leads to greater peak bone mass. As figure 26 graphs, people who are sedentary when they are young commence middle age with considerably less bone than those who were more active. Physical activity also continues to affect bone health as people age. Dozens of studies prove that high levels of weight-bearing activity considerably slow and sometimes even halt or modestly reverse the rate of bone loss in older individuals.

A graph of seal levels over the past five million years looks like a cross section through choppy surf. In yet deeper time, more than seventy million years ago, the height of this surf was magnificent. All of Florida and half of Georgia were shallow seas dotted with islands. The sabal palm’s ancestors likely grew on the sand of these beaches and islands with dinosaurs nibbling on their fruits. On the scale of thousands of years, sand behaves like water. A dune is a ripple. An island is a cresting wave. The sand water rolls, churns, and streams under the power of ocean and wind. Sabal palm is a surfer on those waves.

We assume familiarity with programming, a basic understanding of computational performance issues, complexity theory, introductory level calculus and some of the terminology of graph theory.

label maker plus extra cartridge tape and batteries pad of lined paper, pad of graph paper pens, pencils, felt-tip pens, Sharpies®, and highlighters office necessities like a stapler, tape, paper clips, scissors, labels, calculator, sticky notes, etc. box cutter, letter opener zip ties, cable ties, or cable clips tape measure and small tools (hammer, screw driver, level) assortment of nails and picture-hanging supplies Moving

Neither Duncan nor I could see how to solve that problem by pure mathematics, so we used a computer to simulate the morph on networks of large but manageable size, starting from pristine rings with 1,000 nodes and 10 links per node. To chart the structural changes in the middle ground, we graphed both the average path length and the clustering as functions of the proportion of links that were randomly rewired. What we found amazed us. The slightest bit of randomness contracted the network tremendously. The average path length plummeted at first—with only 1 percent rewiring (meaning that only 1 out of every 100 links was randomized), the graph dropped by 85 percent from its original level. Further rewiring had only a minimal effect; the curve leveled off onto a low-lying plateau, indicating that the network had already gotten about as small as it could possibly get, as if it were completely random. Meanwhile, the clustering barely budged. With 1 percent rewiring, the clustering dropped by only 3 percent. Connections were being yanked out of well-ordered neighborhoods, yet the clustering hardly noticed. Only much later in the morph, long after the crash in path length, did clustering begin to drop significantly.

When you eat food, you give your body a relatively large amount of energy (calories) in a short period. Glucose levels rise far above what is needed to maintain life, and instead of “throwing away” or burning off all excess energy, a portion is stored as body fat for later use. Scientifically speaking, when your body is absorbing nutrients eaten and storing fat, it’s in the “postprandial” state (post meaning “after” and prandial meaning “having to do with a meal”). This “fed” state is when the body is in “fat storage mode.” Once the body has finished absorbing the glucose and other nutrients from the food (amino acids and fatty acids), it then enters the “postabsorptive” state (“after absorption”), wherein it must turn to its fat stores for energy. This “fasted” state is when the body is in “fat burning mode.” Your body flips between “fed” and “fasted” states every day, storing fat from the food you eat and then burning it once there’s nothing left to use from the meals. Here’s a simple graph that depicts this cycle:

categorical and the dependent variable is continuous. The logic of this approach is shown graphically in Figure 13.1. The overall group mean is (the mean of means). The boxplots represent the scores of observations within each group. (As before, the horizontal lines indicate means, rather than medians.) Recall that variance is a measure of dispersion. In both parts of the figure, w is the within-group variance, and b is the between-group variance. Each graph has three within-group variances and three between-group variances, although only one of each is shown. Note in part A that the between-group variances are larger than the within-group variances, which results in a large F-test statistic using the above formula, making it easier to reject the null hypothesis. Conversely, in part B the within-group variances are larger than the between-group variances, causing a smaller F-test statistic and making it more difficult to reject the null hypothesis. The hypotheses are written as follows: H0: No differences between any of the group means exist in the population. HA: At least one difference between group means exists in the population. Note how the alternate hypothesis is phrased, because the logical opposite of “no differences between any of the group means” is that at least one pair of means differs. H0 is also called the global F-test because it tests for differences among any means. The formulas for calculating the between-group variances and within-group variances are quite cumbersome for all but the simplest of designs.1 In any event, statistical software calculates the F-test statistic and reports the level at which it is significant.2 When the preceding null hypothesis is rejected, analysts will also want to know which differences are significant. For example, analysts will want to know which pairs of differences in watershed pollution are significant across regions. Although one approach might be to use the t-test to sequentially test each pair of differences, this should not be done. It would not only be a most tedious undertaking but would also inadvertently and adversely affect the level of significance: the chance of finding a significant pair by chance alone increases as more pairs are examined. Specifically, the probability of rejecting the null hypothesis in one of two tests is [1 – 0.952 =] .098, the probability of rejecting it in one of three tests is [1 – 0.953 =] .143, and so forth. Thus, sequential testing of differences does not reflect the true level of significance for such tests and should not be used. Post-hoc tests test all possible group differences and yet maintain the true level of significance. Post-hoc tests vary in their methods of calculating test statistics and holding experiment-wide error rates constant. Three popular post-hoc tests are the Tukey, Bonferroni, and Scheffe tests.

It was a graph published by the WHO the previous year delineating key environmental issues deemed by the WHO to have the greatest impact on global health. The list included, among others: Demand for clean water, global surface temperatures, ozone depletion, consumption of ocean resources, species extinction, CO2 concentration, deforestation, and global sea levels. All of these negative indicators had been on the rise over the last century. Now, however, they were all accelerating at terrifying rates. Elizabeth had the same reaction that she always had when she saw this graph—a sense of helplessness. She was a scientist and believed in the usefulness of statistics, and this graph painted a chilling picture not of the distant future … but of the very near future.

politicians who bring out charts and graphs tend to fail, and those who use anecdotes tend to win. Stories make sense on an emotional level, so anything that conjures fear, empathy, or pride will trump confusing statistics. It causes you to buy a security system for your house but neglect to purchase radon detectors. It makes you carry pepper spray while you clog your arteries with burritos. It installs metal detectors in schools but leaves french fries on the menu. It creates vegetarian smokers

Graphs showing levels of income, or tourism, or democracy, or access to education, health care, or electricity would all tell the same story: that the world used to be divided into two but isn’t any longer. Today, most people are in the middle. There is no gap between the West and the rest, between developed and developing, between rich and poor.

Thomson’s tidal solution was something like the inverse of Bush’s lawnmower. The surveying machine would read the land’s data of hills and dips and even manhole covers and output a graph; the tide machine invented by Thomson and his brother, which they christened the harmonic analyzer, took a graph as input. The operator stood before a long, open wooden box resting on eight legs, a steel pointer and a hand crank protruding from its innards. With his right hand, he took hold of the pointer and traced a graph of water levels, months’ data on high tides and low; with his left, he steadily turned the crank that turned the oiled gears in the casket. Inside, eleven little cranks rotated at their own speeds, each isolating one of the simple functions that added up to the chaotic tide. At the end, their gauges displayed eleven little numbers—the average water level, the pull of the moon, the pull of the sun, and so on—that together filled in the equation to state the tides. All of it, in principle, could be ground out by human hands on a notepad—but, said Thomson, this was “calculation of so methodical a kind that a machine ought to be found to do it.

Let me take you back in time a little,” says Anumita Roychowdhury, an elegant woman in a beige and pale blue wrap. She’s the director of the Center for Science and Environment, a group that’s played a leading role in the years of battles over air quality. In the 1990s, she tells me, Delhi’s air was so bad “you couldn’t go out in the city without your eyes watering.” India had no regulations on vehicles or fuel, so despite advances elsewhere in the world, engines here hadn’t improved for 40 years, and fuel quality was abysmal. It was the activist Supreme Court that changed that. Its judges started issuing orders, and from 1998 to about 2003, a series of important new rules came into force. Polluting industries were pushed out of the city, auto-rickshaws and buses were converted to CNG, and emission limits for vehicles were introduced, then tightened. “These were pretty big steps,” Roychowdhury says, and they brought results. “If you plot the graph of particulate matter in Delhi, you will see after 2002 the levels actually coming down.” The public noticed. “I still remember the 2004 Assembly elections in Delhi, where the political parties were actually fighting with each other to take credit for the cleaner air. It had become an electoral issue.” So how did things go so wrong? The burst of activity petered out, and rapid growth in car ownership erased the improvements that had been won. “If you look at the pollution levels again from 2008 and ’09 onwards, you now see a steady increase,” Roychowdhury says. “We could not keep the momentum going.” Indeed, particulate levels jumped 75 percent in just a few years.14 Even the action that was taken, she believes, “was too little. We had to do a lot more, more aggressively.” Part of the reason government stopped pushing, Roychowdhury believes, is that the moves needed next would have had to address Delhiites’ growing fondness for cars, so would surely have prompted public anger. “There is a hidden subsidy for all of us who use cars today,” she says. “We barely pay anything in terms of parking charges, we barely pay anything in terms of road taxes. It is so easy to buy a car because of easy loans. So there is absolutely no disincentive.” About 80 percent of transportation spending is focused on drivers, even though they’re only about 15 percent of Delhiites. “The entire infrastructure of the city is getting redesigned to facilitate car movement, but not people’s movement.

What else should you watch for? Most fund buyers look at past performance first, then at the manager’s reputation, then at the riskiness of the fund, and finally (if ever) at the fund’s expenses.8 The intelligent investor looks at those same things—but in the opposite order. Since a fund’s expenses are far more predictable than its future risk or return, you should make them your first filter. There’s no good reason ever to pay more than these levels of annual operating expenses, by fund category: Taxable and municipal bonds: 0.75% U.S. equities (large and mid-sized stocks): 1.0% High-yield (junk) bonds: 1.0% U.S. equities (small stocks): 1.25% Foreign stocks: 1.50%9 Next, evaluate risk. In its prospectus (or buyer’s guide), every fund must show a bar graph displaying its worst loss over a calendar quarter. If you can’t stand losing at least that much money in three months, go elsewhere. It’s also worth checking a fund’s Morningstar rating. A leading investment research firm, Morningstar awards “star ratings” to funds, based on how much risk they took to earn their returns (one star is the worst, five is the best). But, just like past performance itself, these ratings look back in time; they tell you which funds were the best, not which are going to be. Five-star funds, in fact, have a disconcerting habit of going on to underperform one-star funds. So first find a low-cost fund whose managers are major shareholders, dare to be different, don’t hype their returns, and have shown a willingness to shut down before they get too big for their britches. Then, and only then, consult their Morningstar rating.10 Finally, look at past performance, remembering that it is only a pale predictor of future returns. As we’ve already seen, yesterday’s winners often become tomorrow’s losers. But researchers have shown that one thing is almost certain: Yesterday’s losers almost never become tomorrow’s winners. So avoid funds with consistently poor past returns—especially if they have above-average annual expenses.

The behavior of a system is its performance over time—its growth, stagnation, decline, oscillation, randomness, or evolution. If the news did a better job of putting events into historical context, we would have better behavior-level understanding, which is deeper than event-level understanding. When a systems thinker encounters a problem, the first thing he or she does is look for data, time graphs, the history of the system.

Exemplary texts can illustrate a number of features, including text structure; use of graphs, charts, and pictures; effective word choice; and varied sentence structure. Teachers should either read exemplary texts out loud or direct students to read and reread selected exemplary texts, paying close attention to the author’s word choice, overall structure, or other style elements. Teachers should explain and students should discuss how each text demonstrates characteristics of effective writing in that particular genre. Students will then be prepared to emulate characteristics of exemplary texts at the word, sentence and/or text level, or they can use the text as a springboard for writing.

Eighty-five percent of mankind are already inside the box that used to be named “developed world.” The remaining 15 percent are mostly in between the two boxes. Only 13 countries, representing 6 percent of the world population, are still inside the “developing” box. But while the world has changed, the worldview has not, at least in the heads of the “Westerners.” Most of us are stuck with a completely outdated idea about the rest of the world. The complete world makeover I’ve just shown is not unique to family size and child survival rates. The change looks very similar for pretty much any aspect of human lives. Graphs showing levels of income, or tourism, or democracy, or access to education, health care, or electricity would all tell the same story: that the world used to be divided into two but isn’t any longer. Today, most people are in the middle. There is no gap between the West and the rest, between developed and developing, between rich and poor. And we should all stop using the simple pairs of categories that suggest there is.

One morning, without prior notice, the railway level cross-ing opened and the sky- blue Plymouth with the sun in its tail fns sailed through. The architecture of the novel I was writing revealed itself to me. I actually drew it on the back of an enve-lope. Once I knew what I was doing, I wrote quickly. I developed the habit of taking deep, dreamy catnaps after writing a few para-graphs. After several hundred catnaps, over several hundred days, suddenly one ordinary summer morning that had nothing beyond the blazing heat to recommend itself, the story was told, the book was written. It had taken me more than four years. With the last of my money, I bought a printer. I printed the manuscript, and without pausing to think about it for even a moment, I printed the title: The God of Small Things . Pradip and I went out for a coffee. I sat across the table from him and recited the frst few paragraphs. ‘May in Ayemenem is a hot, brooding month. The days are long and humid. The river shrinks and black crows gorge on bright mangoes in the still, dustgreen trees . . .’ I knew the whole book by heart. ‘Finished?’ ‘Yes.

Lead levels in American children and adults are seen to be declining rapidly, beginning in 1992. At the same time, the crime rate falls, the largest plunge in recorded history. Epidemiologists superimpose graphs of lead and crime over each other, the lines rising and falling in tandem so closely that a theory is born: the lead–crime hypothesis.[