Variables In Single Quotes

I, too, feel the need to reread the books I have already read," a third reader says, "but at every rereading I seem to be reading a new book, for the first time. Is it I who keep changing and seeing new things of which I was not previously aware? Or is reading a construction that assumes form, assembling a great number of variables, and therefore something that cannot be repeated twice according to the same pattern? Every time I seek to relive the emotion of a previous reading, I experience different and unexpected impressions, and do not find again those of before. At certain moments it seems to me that between one reading and the next there is a progression: in the sense, for example, of penetrating further into the spirit of the text, or of increasing my critical detachment. At other moments, on the contrary, I seem to retain the memory of the readings of a single book one next to another, enthusiastic or cold or hostile, scattered in time without a perspective, without a thread that ties them together. The conclusion I have reached is that reading is an operation without object; or that its true object is itself. The book is an accessory aid, or even a pretext.

I had not until then fully realized that I was odd, that there was anything strange about growing up with a single-parent genius. I thought all homes had equations scrawled with disc-marker across all the cabinets and walls, and clean laundry in the freezer, and defrosting chicken in the tool drawer. I thought everyone read a book a day and listened to hours of ancient music.

It’s important to remember that big data all comes from the same place – the past. A new campaigning style, a single rogue variable or a ‘black swan’ event can throw the most perfectly calibrated model into chaos.

it’s a theory advanced by two French philosophers who are also psychologists, they hold that we do not have a single soul but a confederation of souls guided by a ruling ego, and every now and then this ruling ego changes, so that although we establish a norm it isn’t a stable norm, but a variable one.

The researchers have found, in essence, that our advice to others tends to hinge on the single most important factor, while our own thinking flits among many variables. When we think of our friends, we see the forest. When we think of ourselves, we get stuck in the trees.§

If you take home one souvenir from this part of the tour, may I suggest that it be a suspicion of moral monists. Beware of anyone who insists that there is one true morality for all people, times, and places—particularly if that morality is founded upon a single moral foundation. Human societies are complex; their needs and challenges are variable.

Silver noticed that the areas where Trump performed best made for an odd map. Trump performed well in parts of the Northeast and industrial Midwest, as well as the South. He performed notably worse out West. Silver looked for variables to try to explain this map. Was it unemployment? Was it religion? Was it gun ownership? Was it rates of immigration? Was it opposition to Obama? Silver found that the single factor that best correlated with Donald Trump’s support in the Republican primaries was that measure I had discovered four years earlier. Areas that supported Trump in the largest numbers were those that made the most Google searches for “nigger.

Who I am is not the same minute to minute or day to day. There are far too many variables for that to ever be true, for me, or for anyone.

More than any other single trait, it is the apple’s genetic variability—its ineluctable wildness—that accounts for its ability to make itself at home in places as different from one another as New England and New Zealand, Kazakhstan and California. Wherever the apple tree goes, its offspring propose so many different variations on what it means to be an apple—at least five per apple, several thousand per tree—that a couple of these novelties are almost bound to have whatever qualities it takes to prosper in the tree’s adopted home.

History was full of divergence points nobody could get anywhere near—from Archduke Ferdinand’s assassination to the battle of Trafalgar. Events so critical and so volatile that the introduction of a single variable—such as a time traveler—could change the outcome. And alter the entire course of history.

Here’s Gall’s Law : all complex systems that work evolved from simpler systems that worked. Complex systems are full of variables and Interdependencies (discussed later) that must be arranged just right in order to function. Complex systems designed from scratch will never work in the real world, since they haven’t been subject to environmental selection forces while being designed.

the properties of an object become manifest when this object interacts with others. We cannot separate the properties from these other objects. We cannot attribute them just to a single object. All of the (variable) properties of an object, in the final analysis, are such and exist only with respect to other objects.

If a person cannot swim across the river, he must take the bridge. He is not afraid of the water or worried that he may drown. But he takes the bridge out of necessity. It is a fact. To fear or worry over necessities (over facts), stills the mind. The stagnant mind only focuses and refocuses on a single point, without peripheral vision—it ponders danger, but it does not prepare for it. The brave man is not bound immobile with caution or lost scouting the fog of hazard calculating insurmountable variables. His sight and sense is not hindered and he may confront danger upon its arrival. To be brave is not to be careless or headstrong, but to act without expectation.

based on a single variable! Every single line on this graph

Individual differences in general cognitive ability is the single most important variable for understanding how well students … learn academic material.

Canadian happiness researcher John Helliwell believes that quality of government is the single most important variable that explains why some countries are happier than others.

Why focus on cash flows? Because a share of stock is a share of a company’s future cash flows, and, as a result, cash flows more than any other single variable seem to do the best job of explaining a company’s stock price over the long term.

My own choice of a single-variable measure for rapid and revealing comparisons of quality of life is infant mortality: the number of deaths during the first year of life that take place per 1,000 live births. Infant mortality is such a powerful indicator because low rates are impossible to achieve without having a combination of several critical conditions that define good quality of life—good healthcare in general, and appropriate prenatal, perinatal, and neonatal care in particular; proper maternal and infant nutrition; adequate and sanitary living conditions; and access to social support for disadvantaged families—and that are also predicated on relevant government and private spending, and on infrastructures and incomes that can maintain usage and access. A single variable thus captures a number of prerequisites for the near-universal survival of the most critical period of life: the first year.

Financial markets are far too complex to isolate any single variable with ease, as if conducting a scientific experiment. The record is utterly bereft of evidence that definitive predictions of short-term fluctuations in stock prices can be made with consistent accuracy. The prices of common stocks are evanescent and illusory.

Understanding reduces the complexity of data by collapsing the dimensionality of information to a lower set of known variables. " "There you have it: a generalizable principle. What was once a massive, high-dimensional dataset has now collapsed to a single dimension, a simple principle that comes from using the data but is not the data itself. Understanding transcends context, since the different contexts collapse according to their previously unknown similarity, which the principle contains. That is what understanding does. And you actually feel it in your brain when it happens. Your “cognitive load” decreases, your level of stress and anxiety decrease, and your emotional state improves.

They somehow managed to persuade themselves that computer models constitute data. That very complicated guesses become facts. They made themselves believe they had the power to accurately model, not merely something as inconceivably complex as, say, a single zygote…but a national economy, a weather system, a planetary ecosphere, a multiplanet society—even a universe.

It is important to note that the design of an entire brain region is simpler than the design of a single neuron. As discussed earlier, models often get simpler at a higher level—consider an analogy with a computer. We do need to understand the detailed physics ofsemiconductors to model a transistor, and the equations underlying a single real transistor are complex. A digital circuit that multiples two numbers requires hundreds of them. Yet we can model this multiplication circuit very simply with one or two formulas. An entire computer with billions of transistors can be modeled through its instruction set and register description, which can be described on a handful of written pages of text and formulas. The software programs for an operating system, language compilers, and assemblers are reasonably complex, but modeling a particular program—for example, a speech recognition programbased on hierarchical hidden Markov modeling—may likewise be described in only a few pages of equations. Nowhere in such a description would be found the details ofsemiconductor physics or even of computer architecture. A similar observation holds true for the brain. A particular neocortical pattern recognizer that detects a particular invariant visualfeature (such as a face) or that performs a bandpass filtering (restricting input to a specific frequency range) on sound or that evaluates the temporal proximity of two events can be described with far fewer specific details than the actual physics and chemicalrelations controlling the neurotransmitters, ion channels, and other synaptic and dendritic variables involved in the neural processes. Although all of this complexity needs to be carefully considered before advancing to the next higher conceptual level, much of it can be simplified as the operating principles of the brain are revealed.

Some of the variable words, say lunch and supper and dinner, may be highlighted but the differences are not particularly important. When we come to say `we just don't speak the same language' we mean something more general: that we have different immediate values or different kinds of valuation, or that we are aware, often intangibly, of different formations and distributions of energy and interest. In such a case, each group is speaking its native language, but its uses are significantly different, and especially when strong feelings or important ideas are in question. No single group is `wrong' by any linguistic criterion, though a temporarily dominant group may try to enforce its own uses as `correct'. What is really happening through these critical encounters, which may be very conscious or may be felt only as a certain strangeness and unease, is a process quite central in the development of a language when, in certain words, tones and rhythms, meanings are offered, felt for, tested, confirmed, asserted, qualified, changed.

From predator-prey models (the so-called Lotka-Volterra type of population dynamics), I knew that populations will experience Extremistan-style variability, hence predators will necessarily go through periods of feast and famine. That's us, humans-we had to have been designed to experience extreme hunger and extreme abundance. So our food intake had to have been fractal. Not a single one of those promoting the "three meals a day," "eat in moderation" idea has tested it empirically to see whether it is healthier than intermittent fasts followed by large feasts. But Near Eastern religions (Judaism, Islam, and Orthodox Christianity) knew it, of course-just as they knew the need for debt avoidance-and so they had fast days.

In a seminal 1981 paper, the economist Sherwin Rosen worked out the mathematics behind these “winner-take-all” markets. One of his key insights was to explicitly model talent—labeled, innocuously, with the variable q in his formulas—as a factor with “imperfect substitution,” which Rosen explains as follows: “Hearing a succession of mediocre singers does not add up to a single outstanding performance.” In other words, talent is not a commodity you can buy in bulk and combine to reach the needed levels: There’s a premium to being the best. Therefore, if you’re in a marketplace where the consumer has access to all performers, and everyone’s q value is clear, the consumer will choose the very best. Even if the talent advantage of the best is small compared to the next rung down on the skill ladder, the superstars still win the bulk of the market.

Yes, I am imperfect and vulnerable and sometimes afraid, but that doesn’t change the truth that I am also brave and worthy of love and belonging. This definition is based on these fundamental ideals: Love and belonging are irreducible needs of all men, women, and children. We’re hardwired for connection—it’s what gives purpose and meaning to our lives. The absence of love, belonging, and connection always leads to suffering. If you roughly divide the men and women I’ve interviewed into two groups—those who feel a deep sense of love and belonging, and those who struggle for it—there’s only one variable that separates the groups: Those who feel lovable, who love, and who experience belonging simply believe they are worthy of love and belonging. They don’t have better or easier lives, they don’t have fewer struggles with addiction or depression, and they haven’t survived fewer traumas or bankruptcies or divorces, but in the midst of all of these struggles, they have developed practices that enable them to hold on to the belief that they are worthy of love, belonging, and even joy. A strong belief in our worthiness doesn’t just happen—it’s cultivated when we understand the guideposts as choices and daily practices. The main concern of Wholehearted men and women is living a life defined by courage, compassion, and connection. The Wholehearted identify vulnerability as the catalyst for courage, compassion, and connection. In fact, the willingness to be vulnerable emerged as the single clearest value shared by all of the women and men whom I would describe as Wholehearted. They attribute everything—from their professional success to their marriages to their proudest parenting moments—to their ability to be vulnerable.

If you take home one souvenir from this part of the tour, may I suggest that it be a suspicion of moral monists. Beware of anyone who insists that there is one true morality for all people, times, and places—particularly if that morality is founded upon a single moral foundation. Human societies are complex; their needs and challenges are variable. Our minds contain a toolbox of psychological systems, including the six moral foundations, which can be used to meet those challenges and construct effective moral communities. You don’t need to use all six, and there may be certain organizations or subcultures that can thrive with just one. But anyone who tells you that all societies, in all eras, should be using one particular moral matrix, resting on one particular configuration of moral foundations, is a fundamentalist of one sort or another. The philosopher Isaiah Berlin

We're not in Khlong Prem Prison yet. So let's assume we're winning." But inwardly, Anderson wonders. There are too many variables in play, and it makes him nervous. He remembers a time in Missouri when the Grahamites rioted. There had been tension, some small speeches, and then it had simply erupted in field burning. No one had seen the violence coming. Not a single intelligence officer had anticipated the cauldron boiling beneath the surface. Anderson had ended up perched atop a grain silo, choking on the smoke of HiGro fields going up in sheets of flame, firing steadily at rioters on the ground with a spring rifle he'd salvaged from a slow-moving security guard, and all the while he had wondered how everyone had missed the signs. They lost the facility because of that blindness. And now it is the same. A sudden eruption, and the surprise of realizing that the world he understands is not the one he actually inhabits.

Concerns about relative position also appear to affect labor force participation by much more than traditional economic factors. The economists David Neumark and Andrew Postlewaite, for example, investigated the labor force status of three thousand pairs of full sisters, one of whom in each pair did not work outside the home. Their aim was to discover what determined whether the other sister in each pair would seek paid employment. None of the usual economic suspects mattered much—not the local unemployment, vacancy, and wage rates, not the other sister’s education and experience. A single variable in their study explained far more of the variance in labor force participation rates than any other: a woman whose sister’s husband earned more than her own husband was 16 to 25 percent more likely than others to seek paid employment.43 As the essayist H. L. Mencken observed, “A wealthy man is one who earns $100 a year more than his wife’s sister’s husband.

This hall of epistemological mirrors was just one of the many challenges facing the researchers who wanted to bring LSD into the field of psychiatry and psychotherapy: psychedelic therapy could look more like shamanism or faith healing than medicine. Another challenge was the irrational exuberance that seemed to infect any researchers who got involved with LSD, an enthusiasm that might have improved the results of their experiments at the same time it fueled the skepticism of colleagues who remained psychedelic virgins. Yet a third challenge was how to fit psychedelics into the existing structures of science and psychiatry, if indeed that was possible. How do you do a controlled experiment with a psychedelic? How do you effectively blind your patients and clinicians or control for the powerful expectancy effect? When “set” and “setting” play such a big role in the patient’s experience, how can you hope to isolate a single variable or design a therapeutic application?

The idea of thermal time reverses this observation. That is to say, instead of inquiring how time produces dissipation in heat, it asks how heat produces time. Thanks to Boltzmann, we know that the notion of heat comes from the fact that we interact with averages. The idea of thermal time is that the notion of time, as well, comes from the fact that we interact only with averages of many variables.* As long as we have a complete description of a system, all the variables of the system are on the same footing; none of them act as a time variable. That is to say, none is correlated to irreversible phenomena. But as soon as we describe the system by means of averages of many variables, we have a preferred variable that functions like common time. A time along which heat dissipates. The time of our everyday experience. Hence time is not a fundamental constituent of the world, but it appears because the world is immense, and we are small systems within the world, interacting only with macroscopic variables that average among innumerable small, microscopic variables. We, in our everyday lives, never see a single elementary particle, or a single quantum of space. We see stones, mountains, the faces of our friends—and each of these things we see is formed by myriads of elementary components. We are always correlated with averages. Averages behave like averages: they disperse heat and, intrinsically, generate time.

At first, Mahalo garnered significant attention and traffic. At its high point, 14.1 million users worldwide visited the site monthly.[lxxxix] But over time, users began to lose interest. Although the payout of the bounties were variable, somehow users did not find the monetary rewards enticing enough. But as Mahalo struggled to retain users, another Q&A site began to boom. Quora, launched in 2010 by two former Facebook employees, quickly grew in popularity. Unlike Mahalo, Quora did not offer a single cent to anyone answering user questions. Why, then, have users stayed highly engaged with Quora, but not with Mahalo, despite its variable monetary rewards? In Mahalo’s case, executives assumed that paying users would drive repeat engagement with the site. After all, people like money, right? Unfortunately, Mahalo had an incomplete understanding of its users’ drivers. Ultimately, the company found that people did not want to use a Q&A site to make money. If the trigger was a desire for monetary rewards, the user was better off spending their time earning an hourly wage. And if the payouts were meant to take the form of a game, like a slot machine, then the rewards came far too infrequently and were too small to matter. However, Quora demonstrated that social rewards and the variable reinforcement of recognition from peers proved to be much more frequent and salient motivators. Quora instituted an upvoting system that reports user satisfaction with answers and provides a steady stream of social feedback. Quora’s social rewards have proven more attractive than Mahalo’s monetary rewards. Only by understanding what truly matters to users can a company correctly match the right variable reward to their intended behavior.

The five letters of the SOLID acronym stand for: Single Responsibility Principle: a class should have one and only one responsibility; that is, only one reason to change. The Lack of Cohesion Of Methods metric indicates the antipattern of too large a class. Open/Closed Principle: a class should be open for extension, but closed against modification. The Case Statement design smell suggests a violation. Liskov Substitution Principle: a method designed to work on an object of type T should also work on an object of any subtype of T. That is, all of T’s subtypes should preserve T’s “contract.” The refused bequest design smell often indicates a violation. Dependency Injection Principle: if two classes depend on each other but their implementations may change, it would be better for them to both depend on a separate abstract interface which is “injected” between them. Demeter Principle: a method can call other methods in its own class, and methods on the classes of its own instance variables; everything else is taboo. A design smell that indicates a violation is inappropriate intimacy.

Let me illustrate this with an example. For someone who works in a busy office, there could be twenty instances a day of needing something that a colleague is using. For any single one of these instances, being low on the personality trait of Agreeableness might make, say, only a 10 per cent difference to the probability of snapping irritably at that colleague. For predicting snapping in any single episode, then, the power of the personality variable is quite weak. However, aggregated across all instances, low Agreeableness will mean an average of one extra bout of irritable behaviour per day, or five per week, or more than two hundred per year. This is a hugely important difference that is bound to have an impact on a person’s life, and yet it stems from what, in the single shot, is only a very slightly raised probability of annoyance. The more we aggregate behaviours across multiple instances, the more important personality as a predictor becomes.

second variable, we find that Z = 2.103, p = .035. This value is larger than that obtained by the parametric test, p = .019.21 SUMMARY When analysts need to determine whether two groups have different means of a continuous variable, the t-test is the tool of choice. This situation arises, for example, when analysts compare measurements at two points in time or the responses of two different groups. There are three common t-tests, involving independent samples, dependent (paired) samples, and the one-sample t-test. T-tests are parametric tests, which means that variables in these tests must meet certain assumptions, notably that they are normally distributed. The requirement of normally distributed variables follows from how parametric tests make inferences. Specifically, t-tests have four assumptions: One variable is continuous, and the other variable is dichotomous. The two distributions have equal variances. The observations are independent. The two distributions are normally distributed. The assumption of homogeneous variances does not apply to dependent-samples and one-sample t-tests because both are based on only a single variable for testing significance. When assumptions of normality are not met, variable transformation may be used. The search for alternative ways for dealing with normality problems may lead analysts to consider nonparametric alternatives. The chief advantage of nonparametric tests is that they do not require continuous variables to be normally distributed. The chief disadvantage is that they yield higher levels of statistical significance, making it less likely that the null hypothesis may be rejected. A nonparametric alternative for the independent-samples t-test is the Mann-Whitney test, and the nonparametric alternative for the dependent-samples t-test is the Wilcoxon

You can use volatile variables only when all the following criteria are met: Writes to the variable do not depend on its current value, or you can ensure that only a single thread ever updates the value; The variable does not participate in invariants with other state variables; and Locking is not required for any other reason while the variable is being accessed.

Of course, different people will have different approaches to life. And individuals may feel differently about different diseases—particularly if some specific disease runs in the family.7 Adding to that variability is the fact that people may feel differently at different points in life. When we have major responsibilities to others, such as young children, we are likely to place more value on the “staying alive” side of the equation. But later in life, we may place more value on “staying well.” So we should expect that people will make different decisions about early diagnosis and that individuals’ decisions may change over time. In short, there is no single right answer.

ThreadLocal, which allows you to associate a per-thread value with a value-holding object. Thread-Local provides get and set accessormethods that maintain a separate copy of the value for each thread that uses it, so a get returns the most recent value passed to set from the currently executing thread. Thread-local variables are often used to prevent sharing in designs based on mutable Singletons or global variables. For example, a single-threaded application might maintain a global database connection that is initialized at startup to avoid having to pass a Connection to every method. Since JDBC connections may not be thread-safe, a multithreaded application that uses a global connection without additional coordination is not thread-safe either. By using a ThreadLocal to store the JDBC connection, as in ConnectionHolder in Listing 3.10, each thread will have its own connection. Listing

If you are porting a single-threaded application to a multithreaded environment, you can preserve thread safety by converting shared global variables into ThreadLocals, if the semantics of the shared globals permits this; an applicationwide cache would not be as useful if it were turned into a number of thread-local caches. ThreadLocal is widely used in implementing application frameworks. For example, J2EE containers associate a transaction context with an executing thread for the duration of an EJB call. This is easily implemented using a static Thread-Local holding the transaction context: when

To preserve state consistency, update related state variables in a single atomic operation.

A special case of thread confinement applies to volatile variables. It is safe to perform read-modify-write operations on shared volatile variables as long as you ensure that the volatile variable is only written from a single thread. In this case, you are confining the modification to a single thread to prevent race conditions, and the visibility guarantees for volatile variables ensure that other threads see the most up-to-date value. Because

There was a single upright metal plate, wired to the grid of an enormous vacuum tube. Several smaller tubes behind the detector tube made the instrument more sensitive. "It works," explained Gerry, "like an electric variable condenser --" "But I say, it has only one wall. Surely all condensers have two." "Exactly. Only in this case the second wall is formed by any metallic body which comes within a certain range. When I switch on the current, there'll be a perfect electronic balance in the vacuum-tube set-up. It will be upset by the approach of any metal, which naturally changes the capacity. Any such change is registered on the dial here, and rings an alarm bell." "Very ingenious," drawled Michaels. "Especially for Venus, which is poor in metals. Don't worry, Miss Carlyle; we'll find Mr. Strike all right. That's a pretty tough lad to hurt.

The number of axes of social division under intersectionality can be almost infinite—but they cannot be reduced to the individual. (People often joke that the individual is the logical endpoint of an intersectional approach that divides people into smaller and smaller groups—but this misunderstands the fundamental reliance on group identity. Even if a person were a unique mix of marginalized identities, thus intersectionally a unique individual, she would be understood through each and all of those group identities, with the details to be filled in by Theory. She would not be understood as an individual.) Consequently, the categories in which intersectionality is interested are numerous. In addition to those of race, sex, class, sexuality, gender identity, religion, immigration status, physical ability, mental health, and body size, there are subcategories, such as exact skin tone, body shape, and abstruse gender identities and sexualities, which number in the hundreds. These all have to be understood in relation to one another so that the positionality each intersection of them confers can be identified and engaged. Moreover, this doesn’t just make intersectionality incredibly internally complex. It is also messy because it is so highly interpretive and operates on so many elements of identity simultaneously, each of which has different claims to a relative degree of marginalization, not all of which are directly comparable. However, there is nothing complex about the overarching idea of intersectionality, or the Theories upon which it is built. Nothing could be simpler. It does the same thing over and over again: look for the power imbalances, bigotry, and biases that it assumes must be present and pick at them. It reduces everything to one single variable, one single topic of conversation, one single focus and interpretation: prejudice, as understood under the power dynamics asserted by Theory. Thus, for example, disparate outcomes can have one, and only one, explanation, and it is prejudicial bigotry.

When required to work for set wages for a certain amount of time at work (i.e., a fixed-interval schedule, where one merely has to wait a certain amount of time before a single response will bring a reward), they tend to work slowly during the first part of the interval, gradually increasing behavioral output as “paytime” arrives (yielding a scalloped curve). When placed on variable-ratio and variable-interval schedules, where things are unpredictable (as in Las Vegas), animals work at constant steady rates, but the rates are substantially faster with variable-ratio schedules (just the way profiteers in Las Vegas want us to behave when playing one-armed bandits). Both humans and animals work in about the same way on such schedules. Unfortunately, behaviorism provided no cogent mechanistic explanation of why and how the brain generates such consistent learned behavior patterns.

[Critical Race Theory] reduces everything to one single variable, one single topic of conversation, one single focus and interpretation: prejudice

In April 2014 a settlement agreement was reached in court, and Detroit had to pay $85 million to USB AG and Bank of America Corporation to terminate the swaps. The use of variable-rate instruments, such as swaps, to finance debt was the single "biggest contributing factor to the increase in Detroit's legacy expenses.

As Siegel has said, of all the variables that encourage the development of secure attachment in a child, the single most powerful one is the degree to which the child’s parent has made coherent sense of his or her own story.

First of all, just because two variables coexist (the therma-organisms’ presence and the beginning of global warming) does not mean that they imply any correlation whatsoever. Also, using the quantity of events a variable contributed to in a past occurrence as a way to conclude that it’s probable for the same variable to contribute to a different quantity of events in a current occurrence does not always give you a valid claim. Just because it may appear that the therma-organisms would be able to contribute to a single event (murder) because they, assuming that it was true, have contributed to two events in a past occurrence simultaneously (global warming and natural disasters correlated with global warming) does not mean it would make it valid for the race of therma-organisms to be overall capable of murder because they’re biologically therma!

programmer can fully prepare for in advance. It’s impossible for them to upload every single variable. As you observe the market, in real time, you will see those unpredictable moments and you will profit in them. You must be very strategic with every trade you enter. Never forget that in the equally strategic world of chess, Garry Kasparov did win some of his rounds against IBM’s Deep Blue. More recently, even IBM’s Watson got answer after answer wrong when playing on Jeopardy! You must also remember that any one organization’s powerful “black box” is trading against all of the other

The Great Acceleration marks the phenomenal growth of the global socio-economic system, the human part of the Earth System. It is difficult to overestimate the scale and speed of change. In little over two generations—or a single lifetime—humanity (or until very recently a small fraction of it) has become a planetary-scale geological force. Hitherto human activities were insignificant compared with the biophysical Earth System, and the two could operate independently. However, it is now impossible to view one as separate from the other. The Great Acceleration trends provide a dynamic view of the emergent, planetary-scale coupling, via globalization, between the socio-economic system and the biophysical Earth System. We have reached a point where many biophysical indicators have clearly moved beyond the bounds of Holocene variability. We are now living in a no-analogue world.

One of his key insights was to explicitly model talent—labeled, innocuously, with the variable q in his formulas—as a factor with “imperfect substitution,” which Rosen explains as follows: “Hearing a succession of mediocre singers does not add up to a single outstanding performance.” In other words, talent is not a commodity you can buy in bulk and combine to reach the needed levels: There’s a premium to being the best.

myname) The name of this function is testfunc. It has a single parameter, myname, and its body is the block of code immediately following the line beginning with def (short for define). A parameter is a variable that

The moral of the story? Beware of intellectuals who make a monotheism out of their theories of motivation. Beware, in more technical terms, of blanket univariate (single variable) causes for diverse, complex problems. Of course, power plays a role in history, as does economics. But the same can be said of jealousy, love, hunger, sex, cooperation, revelation, anger, disgust, sadness, anxiety, religion, compassion, disease, technology, hatred, and chance—none of which can definitively be reduced to another.

Prescribe only daily doses of Vitamin D3 for patients and test subjects, doing anything else makes all data comparisons completely worthless. If we can all agree to daily dosing of Vitamin D3 we can then have a much more workable data set to work with by limiting the treatment to a single variable- the dose. Also, by eliminating the periodic bolus injection protocol we will also drastically reduce the risk of falls caused by high dose Vitamin D3 aggravating an underlying magnesium deficiency. -Never prescribe Vitamin D3 without also giving the patient magnesium and Vitamin K2.

Pearson’s correlation coefficient is represented by the Greek letter rho (ρ) for the population parameter and r for a sample statistic. This coefficient is a single number that measures both the strength and direction of the linear relationship between two continuous variables. Values can range from -1 to +1.

It is unclear whether the behavioral sciences will ever reach the point when logically derived predictions drive progress. Behavior is more variable than the dance of photons, and its explanation involves multiple layers, from the physiological to the mental. We cannot afford to look through a single pair of glasses; we need lots of different glasses to see reality. Theories do assist in this effort, by guiding our attention and making large amounts of data graspable, but they also induce selective blindness.

The dominant model of disease in our time is biomedical, built on a foundation of molecular biology. As Engel explains, It assumes disease to be fully accounted for by deviations from the norm of measurable biological (somatic) variables. It leaves no room within its framework for the social, psychological, and behavioral dimensions of illness. The biomedical model not only requires that disease be dealt with as an entity independent of social behavior, it also demands that behavioral aberrations be explained on the basis of disordered somatic (biochemical or neurophysiological) processes. Thus the biomedical model embraces both reductionism, the philosophic view that complex phenomena are ultimately derived from a single primary principle, and mind-body dualism.

In a seminal 1981 paper, the economist Sherwin Rosen worked out the mathematics behind these “winner-take-all” markets. One of his key insights was to explicitly model talent—labeled, innocuously, with the variable q in his formulas—as a factor with “imperfect substitution,” which Rosen explains as follows: “Hearing a succession of mediocre singers does not add up to a single outstanding performance.” In other words, talent is not a commodity you can buy in bulk and combine to reach the needed levels: There’s a premium to being the best. Therefore, if you’re in a marketplace where the consumer has access to all performers, and everyone’s q value is clear, the consumer will choose the very best.

ahead: Wholehearted living is about engaging in our lives from a place of worthiness. It means cultivating the courage, compassion, and connection to wake up in the morning and think, No matter what gets done and how much is left undone, I am enough. It’s going to bed at night thinking, Yes, I am imperfect and vulnerable and sometimes afraid, but that doesn’t change the truth that I am also brave and worthy of love and belonging. This definition is based on these fundamental ideals: Love and belonging are irreducible needs of all men, women, and children. We’re hardwired for connection—it’s what gives purpose and meaning to our lives. The absence of love, belonging, and connection always leads to suffering. If you roughly divide the men and women I’ve interviewed into two groups—those who feel a deep sense of love and belonging, and those who struggle for it—there’s only one variable that separates the groups: Those who feel lovable, who love, and who experience belonging simply believe they are worthy of love and belonging. They don’t have better or easier lives, they don’t have fewer struggles with addiction or depression, and they haven’t survived fewer traumas or bankruptcies or divorces, but in the midst of all of these struggles, they have developed practices that enable them to hold on to the belief that they are worthy of love, belonging, and even joy. A strong belief in our worthiness doesn’t just happen—it’s cultivated when we understand the guideposts as choices and daily practices. The main concern of Wholehearted men and women is living a life defined by courage, compassion, and connection. The Wholehearted identify vulnerability as the catalyst for courage, compassion, and connection. In fact, the willingness to be vulnerable emerged as the single clearest value shared by all of the women and men whom I would describe as Wholehearted. They attribute everything—from their professional success to their marriages to their proudest parenting moments—to their ability to be vulnerable.

To make this point, I often do the same coin flipping exercise that I explained during the probability discussion. In a class of forty students or so, I’ll have each student flip a coin. Any student who flips tails is eliminated; the rest flip again. In the second round, those who flip tails are once again eliminated. I continue the rounds of flipping until one student has flipped five or six heads in a row. You may recall some of the silly follow-up questions: “What’s your secret? Is it in the wrist? Can you teach us to flip heads all the time? Maybe it’s that Harvard sweatshirt you’re wearing.” Obviously the string of heads is just luck; the students have all watched it happen. However, that is not necessarily how the result could or would be interpreted in a scientific context. The probability of flipping five heads in a row is 1/32, or .03. This is comfortably below the .05 threshold we typically use to reject a null hypothesis. Our null hypothesis in this case is that the student has no special talent for flipping heads; the lucky string of heads (which is bound to happen for at least one student when I start with a large group) allows us to reject the null hypothesis and adopt the alternative hypothesis: This student has a special ability to flip heads. After he has achieved this impressive feat, we can study him for clues about his flipping success—his flipping form, his athletic training, his extraordinary concentration while the coin is in the air, and so on. And it is all nonsense. This phenomenon can plague even legitimate research. The accepted convention is to reject a null hypothesis when we observe something that would happen by chance only 1 in 20 times or less if the null hypothesis were true. Of course, if we conduct 20 studies, or if we include 20 junk variables in a single regression equation, then on average we will get 1 bogus statistically significant finding.

1. In the future, when training soldiers for the battlefield in the Far East, it is necessary to strengthen the training in long-distance marches. It is not as simple as physical endurance but also involves complex variables such as lifestyle and mental habits, the distribution of energy, and so forth. Otherwise, it’s impossible to explain why Chinese soldiers who are far weaker than American soldiers have such a great advantage over their American counterparts in long-distance marching.

The critical race theorists pretend to reveal a deeper understanding of racism in the United States, but by reducing the complex phenomenon of inequality to a single causal variable—racism—their theory is dangerously incomplete. Their policy of “anti-racism”—the destruction of middle-class norms and the construction of a racial patronage machine—would deepen racial divisions, not transcend them. Even worse, it would undermine the very institutions that are essential to addressing inequality in America.27

After reading Hooked, the founders of the Fitbod App targeted a very specific user habit. Unlike competitors who went after vague behaviors like “build a healthy lifestyle,” Fitbod sought to own the internal trigger related to the uncomfortable feeling of uncertainty of not knowing what to do in the gym. Fitbod’s action phase quickly solves the user’s psychological discomfort by providing very specific instructions with a single tap of the app. In Fitbod’s variable rewards phase, discover which exercise to do, how much weight to lift, and how many repetitions to complete to beat their personal best. Finally, the data users enter when they complete an exercise improves the service and loads the next external trigger, thus perpetuating the habit of using the app.

The most important part of the PoW process is that one of the four variables is the identity of the previous block, which includes when that block was created, its set of transactions, the identity of the block before that, and the block’s nonce. If innovative investors keep following this logic, they will realize that this links every single block in Bitcoin’s blockchain together. As a result, no information in any past block, even if it was created years ago, can be changed without changing all of the blocks after it. Such a change would be rejected by the distributed set of miners, and this property is what makes Bitcoin’s blockchain and the transactions therein immutable.

By now I hope that it's clear; though we often blame other factors, there is no single variable more relevant to heart disease than insulin resistance.

Recall that GDP, gross domestic product, the dominant metric in economics for the last century, consists of a combination of consumption, plus private investments, plus government spending, plus exports-minus-imports. Criticisms of GDP are many, as it includes destructive activities as positive economic numbers, and excludes many kinds of negative externalities, as well as issues of health, social reproduction, citizen satisfaction, and so on. Alternative measures that compensate for these deficiencies include: the Genuine Progress Indicator, which uses twenty-six different variables to determine its single index number; the UN’s Human Development Index, developed by Pakistani economist Mahbub ul Haq in 1990, which combines life expectancy, education levels, and gross national income per capita (later the UN introduced the inequality-adjusted HDI); the UN’s Inclusive Wealth Report, which combines manufactured capital, human capital, natural capital, adjusted by factors including carbon emissions; the Happy Planet Index, created by the New Economic Forum, which combines well-being as reported by citizens, life expectancy, and inequality of outcomes, divided by ecological footprint (by this rubric the US scores 20.1 out of 100, and comes in 108th out of 140 countries rated); the Food Sustainability Index, formulated by Barilla Center for Food and Nutrition, which uses fifty-eight metrics to measure food security, welfare, and ecological sustainability; the Ecological Footprint, as developed by the Global Footprint Network, which estimates how much land it would take to sustainably support the lifestyle of a town or country, an amount always larger by considerable margins than the political entities being evaluated, except for Cuba and a few other countries; and Bhutan’s famous Gross National Happiness, which uses thirty-three metrics to measure the titular quality in quantitative terms.

Scheffe tests also produce “homogeneous subsets,” that is, groups that have statistically identical means. Both the three largest and the three smallest populations have identical means. The Tukey levels of statistical significance are, respectively, .725 and .165 (both > .05). This is shown in Table 13.3. Figure 13.2 Group Boxplots Table 13.2 ANOVA Table Third, is the increase in means linear? This test is an option on many statistical software packages that produces an additional line of output in the ANOVA table, called the “linear term for unweighted sum of squares,” with the appropriate F-test. Here, that F-test statistic is 7.85, p = .006 < .01, and so we conclude that the apparent linear increase is indeed significant: wetland loss is linearly associated with the increased surrounding population of watersheds.8 Figure 13.2 does not clearly show this, but the enlarged Y-axis in Figure 13.3 does. Fourth, are our findings robust? One concern is that the statistical validity is affected by observations that statistically (although not substantively) are outliers. Removing the seven outliers identified earlier does not affect our conclusions. The resulting variable remains normally distributed, and there are no (new) outliers for any group. The resulting variable has equal variances across the groups (Levene’s test = 1.03, p = .38 > .05). The global F-test is 3.44 (p = .019 < .05), and the Bonferroni post-hoc test similarly finds that only the differences between the “Small” and “Large” group means are significant (p = .031). The increase remains linear (F = 6.74, p = .011 < .05). Thus, we conclude that the presence of observations with large values does not alter our conclusions. Table 13.3 Homogeneous Subsets Figure 13.3 Watershed Loss, by Population We also test the robustness of conclusions for different variable transformations. The extreme skewness of the untransformed variable allows for only a limited range of root transformations that produce normality. Within this range (power 0.222 through 0.275), the preceding conclusions are replicated fully. Natural log and base-10 log transformations also result in normality and replicate these results, except that the post-hoc tests fail to identify that the means of the “Large” and “Small” groups are significantly different. However, the global F-test is (marginally) significant (F = 2.80, p = .043 < .05), which suggests that this difference is too small to detect with this transformation. A single, independent-samples t-test for this difference is significant (t = 2.47, p = .017 < .05), suggesting that this problem may have been exacerbated by the limited number of observations. In sum, we find converging evidence for our conclusions. As this example also shows, when using statistics, analysts frequently must exercise judgment and justify their decisions.9 Finally, what is the practical significance of this analysis? The wetland loss among watersheds with large surrounding populations is [(3.21 – 2.52)/2.52 =] 27.4 percent greater than among those surrounded by small populations. It is up to managers and elected officials to determine whether a difference of this magnitude warrants intervention in watersheds with large surrounding populations.10

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Beyond One-Way ANOVA The approach described in the preceding section is called one-way ANOVA. This scenario is easily generalized to accommodate more than one independent variable. These independent variables are either discrete (called factors) or continuous (called covariates). These approaches are called n-way ANOVA or ANCOVA (the “C” indicates the presence of covariates). Two way ANOVA, for example, allows for testing of the effect of two different independent variables on the dependent variable, as well as the interaction of these two independent variables. An interaction effect between two variables describes the way that variables “work together” to have an effect on the dependent variable. This is perhaps best illustrated by an example. Suppose that an analyst wants to know whether the number of health care information workshops attended, as well as a person’s education, are associated with healthy lifestyle behaviors. Although we can surely theorize how attending health care information workshops and a person’s education can each affect an individual’s healthy lifestyle behaviors, it is also easy to see that the level of education can affect a person’s propensity for attending health care information workshops, as well. Hence, an interaction effect could also exist between these two independent variables (factors). The effects of each independent variable on the dependent variable are called main effects (as distinct from interaction effects). To continue the earlier example, suppose that in addition to population, an analyst also wants to consider a measure of the watershed’s preexisting condition, such as the number of plant and animal species at risk in the watershed. Two-way ANOVA produces the results shown in Table 13.4, using the transformed variable mentioned earlier. The first row, labeled “model,” refers to the combined effects of all main and interaction effects in the model on the dependent variable. This is the global F-test. The “model” row shows that the two main effects and the single interaction effect, when considered together, are significantly associated with changes in the dependent variable (p < .000). However, the results also show a reduced significance level of “population” (now, p = .064), which seems related to the interaction effect (p = .076). Although neither effect is significant at conventional levels, the results do suggest that an interaction effect is present between population and watershed condition (of which the number of at-risk species is an indicator) on watershed wetland loss. Post-hoc tests are only provided separately for each of the independent variables (factors), and the results show the same homogeneous grouping for both of the independent variables. Table 13.4 Two-Way ANOVA Results As we noted earlier, ANOVA is a family of statistical techniques that allow for a broad range of rather complex experimental designs. Complete coverage of these techniques is well beyond the scope of this book, but in general, many of these techniques aim to discern the effect of variables in the presence of other (control) variables. ANOVA is but one approach for addressing control variables. A far more common approach in public policy, economics, political science, and public administration (as well as in many others fields) is multiple regression (see Chapter 15). Many analysts feel that ANOVA and regression are largely equivalent. Historically, the preference for ANOVA stems from its uses in medical and agricultural research, with applications in education and psychology. Finally, the ANOVA approach can be generalized to allow for testing on two or more dependent variables. This approach is called multiple analysis of variance, or MANOVA. Regression-based analysis can also be used for dealing with multiple dependent variables, as mentioned in Chapter 17.

variable. In social science, this is called a nomothetic mode of explanation—the isolation of the most important factors. This approach is consistent with the philosophy of seeking complete but parsimonious explanations in science.1 The second part involves addressing those variables that were not considered as being of most relevance. Regarding the first part, the specification of the “most important” independent variables is a judicious undertaking. The use of a nomothetic strategy implies that a range of plausible models exists—different analysts may identify different sets of “most important” independent variables. Analysts should ask which different factors are most likely to affect or cause their dependent variable, and they are likely to justify, identify, and operationalize their choices differently. Thus, the term full model specification does not imply that only one model or even a best model exists, but rather it refers to a family of plausible models. Most researchers agree that specification should (1) be driven by theory, that is, by persuasive arguments and perspectives that identify and justify which factors are most important, and (2) inform why the set of such variables is regarded as complete and parsimonious. In practice, the search for complete, parsimonious, and theory-driven explanations usually results in multiple regression models with about 5–12 independent variables; theory seldom results in less than 5 variables, and parsimony and problems of statistical estimation, discussed further, seldom result in models with more than 12. Key Point We cannot examine the effect of all possible variables. Rather, we focus on the most relevant ones. The search for parsimonious explanations often leads analysts to first identify different categories of factors that most affect their dependent variable. Then, after these categories of factors have been identified, analysts turn to the task of trying to measure each, through either single or index variables. As an example,

single or index variables. As an example, consider the dependent variable “high school violence,” discussed in Chapter 2. We ask: “What are the most important, distinct factors affecting or causing high school violence?” Some plausible factors are (1) student access to weapons, (2) student isolation from others, (3) peer groups that are prone to violence, (4) lack of enforcement of school nonviolence policies, (5) participation in anger management programs, and (6) familiarity with warning signals (among teachers and staff). Perhaps you can think of other factors. Then, following the strategies discussed in Chapter 3—conceptualization, operationalization, and index variable construction—we use either single variables or index measures as independent variables to measure each of these factors. This approach provides for the inclusion of programs or policies as independent variables, as well as variables that measure salient rival hypotheses. The strategy of full model specification requires that analysts not overlook important factors. Thus, analysts do well to carefully justify their model and to consult past studies and interview those who have direct experience with, or other opinions about, the research subject. Doing so might lead analysts to include additional variables, such as the socioeconomic status of students’ parents. Then, after a fully specified model has been identified, analysts often include additional variables of interest. These may be variables of lesser relevance, speculative consequences, or variables that analysts want to test for their lack of impact, such as rival hypotheses. Demographic variables, such as the age of students, might be added. When additional variables are included, analysts should identify which independent variables constitute the nomothetic explanation, and which serve some other purpose. Remember, all variables included in models must be theoretically justified. Analysts must argue how each variable could plausibly affect their dependent variable. The second part of “all of the variables that affect the dependent variable” acknowledges all of the other variables that are not identified (or included) in the model. They are omitted; these variables are not among “the most important factors” that affect the dependent variable. The cumulative effect of these other variables is, by definition, contained in the error term, described later in this chapter. The assumption of full model specification is that these other variables are justifiably omitted only when their cumulative effect on the dependent variable is zero. This approach is plausible because each of these many unknown variables may have a different magnitude, thus making it possible that their effects cancel each other out. The argument, quite clearly, is not that each of these other factors has no impact on the dependent variable—but only that their cumulative effect is zero. The validity of multiple regression models centers on examining the behavior of the error term in this regard. If the cumulative effect of all the other variables is not zero, then additional independent variables may have to be considered. The specification of the multiple regression model is as follows:

regression line will have larger standard deviations and, hence, larger standard errors. The computer calculates the slope, intercept, standard error of the slope, and the level at which the slope is statistically significant. Key Point The significance of the slope tests the relationship. Consider the following example. A management analyst with the Department of Defense wishes to evaluate the impact of teamwork on the productivity of naval shipyard repair facilities. Although all shipyards are required to use teamwork management strategies, these strategies are assumed to vary in practice. Coincidentally, a recently implemented employee survey asked about the perceived use and effectiveness of teamwork. These items have been aggregated into a single index variable that measures teamwork. Employees were also asked questions about perceived performance, as measured by productivity, customer orientation, planning and scheduling, and employee motivation. These items were combined into an index measure of work productivity. Both index measures are continuous variables. The analyst wants to know whether a relationship exists between perceived productivity and teamwork. Table 14.1 shows the computer output obtained from a simple regression. The slope, b, is 0.223; the slope coefficient of teamwork is positive; and the slope is significant at the 1 percent level. Thus, perceptions of teamwork are positively associated with productivity. The t-test statistic, 5.053, is calculated as 0.223/0.044 (rounding errors explain the difference from the printed value of t). Other statistics shown in Table 14.1 are discussed below. The appropriate notation for this relationship is shown below. Either the t-test statistic or the standard error should be shown in parentheses, directly below the regression coefficient; analysts should state which statistic is shown. Here, we show the t-test statistic:3 The level of significance of the regression coefficient is indicated with asterisks, which conforms to the p-value legend that should also be shown. Typically, two asterisks are used to indicate a 1 percent level of significance, one asterisk for a 5 percent level of significance, and no asterisk for coefficients that are insignificant.4 Table 14.1 Simple Regression Output Note: SEE = standard error of the estimate; SE = standard error; Sig. = significance.

In phase space the complete state of knowledge about a dynamical system at a single instant in time collapses to a point. That point is the dynamical system-at that instant. At the next instant, though, the system will have changed, ever so slightly and so the point moves. The history of the system time can be charted by the moving point, tracing its orbit through phase space with the passage of time. How can all the information about a complicated system be stored in a point? If the system has only two variables, the answer is simple. It is straight from the Cartesian geometry taught in high school-one variable on the horizontal axis, the other on the vertical. If the system is a swinging, frictionless pendulum, one variable is position and the other velocity, and they change continuously, making a line of points that traces a loop, repeating itself forever, around and around. The same system with a higher energy level-swinging faster and farther-forms a loop in phase space similar to the first, but larger. A little realism, in the form of friction, changes the picture. We do not need the equations of motion to know the density of a pendulum subject to friction. Every orbit must eventually end up at the same place, the center: position 0, velocity 0. This central fixed point "attracts" the orbits. Instead of looping around forever, they spiral inward. The friction dissipates the system's energy, and in phase space the dissipation shows itself as a pull toward the center, from the outer regions of high energy to the inner regions of low energy. The attractor-the simplest kind possible-is like a pinpoint magnet embedded in a rubber sheet.

it was noted that one aspect of a class was to act as a repository for code and information shared by all instances (objects) of that class. In terms of efficiency, this is a good idea because storage space is minimized and changes can be made in a single place. It becomes very tempting, however, to use this fact to justify creating your class hierarchy based on shared code instead of shared behaviors. A similar error arises from treating object variables (instance variables) as if they were data attributes and then creating your hierarchy based on shared attributes. Always create hierarchies based on shared behaviors, side

The divorce boom had a huge impact on never or not-yet married women. First, it created more single people, helping to slowly destigmatize the figure of the woman without a ring on her finger. It also forced a very public reckoning with marriage as an institution of variable quality. The realization that a bad marriage might be bad enough to cause a painful split provided ammunition to those women who preferred to abstain from marriage than to enter a flawed one.

The second route that motor commands from the cortex can take out towards the muscles is called the multineuronal pathway. The initial cell bodies of the pathway are also in the motor cortex, but they immediately synapse to long chains of internuncial neurons descending from the motor cortex to other cortical areas, to the lower brain, and on down the cord. The final links in these chains end not directly upon the neurons of the motor units, but rather upon the spinal internuncial circuits which organize the patterns of stereotyped spinal reflexes. Thus instead of bypassing the intermediate net, this pathway channels motor commands all the way through it. Impulses take longer to travel from the cortex to the motor unit, since they pass through many more synapses and are influenced by input from many other sources along their way. This arrangement makes possible the second kind of motor control—the setting into motion of the stereotyped reflex units of movement that are organized in the subconscious levels of the lower brain and spinal cord. For instance, the individual circuits and the overall sequential arrangements which control walking or swallowing are in the cord; the multineuronal pathway has only to stimulate these sequences into activity, and then steer their general course without the cortex having to pay attention to the details involved in each separate step. All coordinated movements require the interaction of both of these pathways. The conscious mind is not competent to direct every single muscular adjustment involved in general movements, and the reflexes are powerless to arrange themselves into new, finely controlled sequences. Successfully integrated, the two of these paths together provide the best aspects of millions of years of repetitive practice and of moment to moment changes in intent or shifts in the surrounding environment. One is basic vocabulary, the other is variable sentence structure; the language of coordinated movement cannot go forward without both working together.

In a seminal 1981 paper, the economist Sherwin Rosen worked out the mathematics behind these “winner-take-all” markets. One of his key insights was to explicitly model talent—labeled, innocuously, with the variable q in his formulas—as a factor with “imperfect substitution,” which Rosen explains as follows: “Hearing a succession of mediocre singers does not add up to a single outstanding performance.” In other words, talent is not a commodity you can buy in bulk and combine to reach the needed levels: There’s a premium to being the best.

The single most accurate predictor of whether a person voted for Donald Trump in the 2016 election was not religion, wealth, education, or even political party; it was believing the United States is and should be a Christian nation.26 Researchers studied this connection and were able to control for other characteristics to ensure that Christian nationalism was not simply a proxy for other forms of intolerance or other variables related to vote choice.27 They concluded, “The more someone believed the United States is—and should be—a Christian nation, the more likely they were to vote for Trump.

In 1997, the Brooklyn Museum staged an exhibition, Monet and the Mediterranean, which included seventy-one paintings Claude Monet created during trips to the French and Italian Rivieras (in 1884 and 1888) and to Venice (in 1908).7 Instead of single, signature works, the exhibition showcased how Monet experimented, changing one variable at a time. For instance, for his Grand Canal series, Monet painted the same church from the same location but at different times of day to study changes in lighting. He also painted the Doge’s Palace for another series, showing the same building from different perspectives. Monet used this method of painting the same subject with small variations to perfect his technique.8 This illustrates the aspect of incrementalization in Layer 1, isolating and iterating the novel parts of the problem from what is considered already developed, tested, and validated