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The starting point for appreciating that there is a distinctive part of our psychology for morality is seeing how moral judgments differ from other kinds of opinions we have on how people ought to behave.

Moralization is a psychological state that can be turned on and off like a switch, and when it is on, a distinctive mind-set commandeers our thinking. This is the mind-set that makes us deem actions immoral (“killing is wrong”), rather than merely disagreeable (“I hate brussels sprouts”), unfashionable (“bell-bottoms are out”) or imprudent (“don’t scratch mosquito bites”).

The first hallmark of moralization is that the rules it invokes are felt to be universal

Many of these moralizations, like the assault on smoking, may be understood as practical tactics to reduce some recently identified harm. But whether an activity flips our mental switches to the “moral” setting isn’t just a matter of how much harm it does

We all know what it feels like when the moralization switch flips inside us — the righteous glow, the burning dudgeon, the drive to recruit others to the cause.

The human moral sense turns out to be an organ of considerable complexity, with quirks that reflect its evolutionary history and its neurobiological foundations.

At the same time, many behaviors have been amoralized, switched from moral failings to lifestyle choices. They include divorce, illegitimacy, being a working mother, marijuana use and homosexuality.

This wave of amoralization has led the cultural right to lament that morality itself is under assault, as we see in the group that anointed itself the Moral Majority. In fact there seems to be a Law of Conservation of Moralization, so that as old behaviors are taken out of the moralized column, new ones are added to it.

Much of our recent social history, including the culture wars between liberals and conservatives, consists of the moralization or amoralization of particular kinds of behavior.

People don’t generally engage in moral reasoning, Haidt argues, but moral rationalization: they begin with the conclusion, coughed up by an unconscious emotion, and then work backward to a plausible justification.

When psychologists say “most people” they usually mean “most of the two dozen sophomores who filled out a questionnaire for beer money.” But in this case it means most of the 200,000 people from a hundred countries who shared their intuitions on a Web-based experiment conducted by the psychologists Fiery Cushman and Liane Young and the biologist Marc Hauser. A difference between the acceptability of switch-pulling and man-heaving, and an inability to justify the choice, was found in respondents from Europe, Asia and North and South America; among men and women, blacks and whites, teenagers and octogenarians, Hindus, Muslims, Buddhists, Christians, Jews and atheists; people with elementary-school educations and people with Ph.D.’s.

Joshua Greene, a philosopher and cognitive neuroscientist, suggests that evolution equipped people with a revulsion to manhandling an innocent person. This instinct, he suggests, tends to overwhelm any utilitarian calculus that would tot up the lives saved and lost

the findings corroborate Greene’s theory that our nonutilitarian intuitions come from the victory of an emotional impulse over a cost-benefit analysis.

Economics teaches that incentives matter and trade-offs are unavoidable. It shows how naive attempts to fix social problems, from poverty to climate change, can have unintended consequences. Introductory economics, at its best, enables people to see the unstated assumptions and hidden costs behind the rosy promises of politicians and businessmen.

A Chilean professor, Oscar Landerretche, worked with other economists to design a new curriculum. He, Sam Bowles, of the Santa Fe Institute, Wendy Carlin, of University College London (UCL), and Margaret Stevens, of Oxford University, painstakingly knitted contributions from economists around the world into a text that is free, online and offers interactive charts and videos of star economists. That text is the basis of economics modules taught by a small but growing number of instructors.

Students pay $300 or more for textbooks explaining that in competitive markets the price of a good should fall to the cost of producing an additional unit, and unsurprisingly regurgitate the expected answers. A study of 170 economics modules taught at seven universities found that marks in exams favoured the ability to “operate a model” over proofs of independent judgment.

“The Economy”, as the book is economically titled, covers the usual subjects, but in a very different way. It begins with the biggest of big pictures, explaining how capitalism and industrialisation transformed the world, inviting students to contemplate how it arrived at where it is today.

That could mean, eventually, a broader array of perspectives within economics departments, bigger and bolder research questions—and fewer profession-shaking traumas in future.

Messy complications, from environmental damage to inequality, are placed firmly in the foreground.

It explains cost curves, as other introductory texts do, but in the context of the Industrial Revolution, thus exposing students to debates about why industrialisation kicked off when and where it did.

Thomas Malthus’s ideas are used to teach students the uses and limitations of economic models, combining technical instruction with a valuable lesson from the history of economic thought.

“The Economy” does not dumb down economics; it uses maths readily, keeping students engaged through the topicality of the material. Quite early on, students have lessons in the weirdness in economics—from game theory to power dynamics within firms—that makes the subject fascinating and useful but are skimmed over in most introductory courses.

Homa Zarghamee, also at Barnard, appreciates having to spend less time “unteaching”, ie, explaining to students why the perfect-competition result they learned does not actually hold in most cases. A student who does not finish the course will not be left with a misleading idea of economics, she notes.

But the all-important exceptions are taught quite late in the curriculum—or, often, only in more advanced courses taken by those pursuing an economics degree.

Far from an unintended result of ill-conceived policies, she argues, the roughly 4m deaths from hunger in 1932 and 1933 were part of a deliberate campaign by Josef Stalin and the Bolshevik leadership to crush Ukrainian national aspirations, literally starving actual or potential bearers of those aspirations into submission to the Soviet order

The politics in this case was the Sovietisation of Ukraine; the means was starvation. Food supply was not mismanaged by Utopian dreamers. It was weaponised.

. “Red Famine” presents a Bolshevik government so hell-bent on extracting wealth and controlling labour that it was willing to confiscate the last remaining grain from hungry peasants (mostly but not exclusively in Ukraine) and then block them from fleeing famine-afflicted areas to search for food.

. Stalin was not only aware of the ensuing mass death (amounting to roughly 13% of Ukraine’s population). He actively sought to suppress knowledge of it (including banning the publication of census data), so as not to distract from the campaign to collectivise Soviet agriculture and extend the Communist Party’s reach into the countryside—a campaign Ms Applebaum calls a “revolution...more profound and more shocking than the original Bolshevik revolution itself”

The book’s most powerful passages describe the moral degradation that resulted from sustained hunger, as family solidarity and village traditions of hospitality withered in the face of the overwhelming desire to eat. Under a state of siege by Soviet authorities, hunger-crazed peasants took to consuming, grass, animal hides, manure and occasionally each other. People became indifferent to the sight of corpses lying in streets, and eventually to their own demis

While stressing Stalin’s goal of crushing Ukrainian nationalism, moreover, Ms Applebaum passes over a subtler truth. For along with its efforts to root out “bourgeois” nationalisms, the Kremlin relentlessly promoted a Soviet version of Ukrainian identity, as it did with most other ethnic minorities. Eight decades on, that legacy has done even more to shape today’s Ukraine than the Holodomor.

“We want to be very clear that all we are reporting is observation of an excess (a fairly significant one) and not a discovery of any kind,”

“I’m trying to be calm here, but it’s hard not to be hyperbolic,” said Neal Weiner, a particle theorist at New York University. “If this is real, calling it a game changer would be an understatement.”

Dr. Aprile’s Xenon experiment is currently the largest and most sensitive in an alphabet soup of efforts aimed at detecting and identifying dark matter

The best guess is that this dark matter consists of clouds of exotic subatomic particles left over from the Big Bang and known generically as WIMPs, for weakly interacting massive particles, hundreds or thousands of times more massive than a hydrogen atom.

The story of axions begins in 1977, when Roberto Peccei, a professor at the University of California, Los Angeles, who died on June 1, and Helen Quinn, emerita professor at Stanford, suggested a slight modification to the theory that governs strong nuclear forces, making sure that it is invariant to the direction of time, a feature that physicists consider a necessity for the universe.

in its most recent analysis of that experiment, the team had looked for electrons, rather than the heavier xenon nuclei, recoiling from collisions. Among other things, that could be the signature of particles much lighter than the putative WIMPs striking the xenon.

Simulations and calculations suggested that random events should have produced about 232 such recoils over the course of a year.

But from February 2017 to February 2018, the detector recorded 285, an excess of 53 recoils.

Dr. Aprile and her colleagues have wired a succession of vats containing liquid xenon with photomultipliers and other sensors. The hope is that her team’s device — far underground to shield it from cosmic rays and other worldly forms of interference — would spot the rare collision between a WIMP and a xenon atom. The collision should result in a flash of light and a cloud of electrical charge.

this modification implied the existence of a new subatomic particle. Dr. Wilczek called it the axion, and the name stuck.

Axions have never been detected either directly or indirectly. And the theory does not predict their mass, which makes it hard to look for them. It only predicts that they would be weird and would barely interact with regular matter

although they are not WIMPS, they share some of those particles’ imagined weird abilities, such as being able to float through Earth and our bodies like smoke through a screen door.

In order to fulfill the requirements of cosmologists, however, such dark-matter axions would need to have a mass of less than a thousandth of an electron volt in the units of mass and energy preferred by physicists

(By comparison, the electrons that dance around in your smartphone weigh in at half a million electron volts each.) What they lack in heft they would more than make up for in numbers.

That would make individual cosmic dark-matter axions too slow and ethereal to be detected by the Xenon experiment.But axions could also be produced by nuclear reactions in the sun, and those “solar axions” would have enough energy to ping the Xenon detector right where it is most sensitive

The other exciting, though slightly less likely, possibility is that the Xenon collaboration’s excess signals come from the wispy particles known as neutrinos, which are real, and weird, and zipping through our bodies by the trillions every second.

Ordinarily, these neutrinos would not contribute much to the excess of events the detector read. But they would do so if they had an intrinsic magnetism that physicists call a magnetic moment. That would give them a higher probability of interacting with the xenon and tripping the detector

According to the standard lore, neutrinos, which are electrically neutral, do not carry magnetism. The discovery that they did would require rewriting the rules as they apply to neutrinos.

That, said Dr. Weiner, would be “a very very big deal,” because it would imply that there are new fundamental particles out there to look for — new physics.

The important thing to remember amid all this, and the thing that has changed the game when it comes to the free speech/content moderation conversation, is that Elon Musk himself loves conspiracy theorie

The media isn’t just unduly critical—a perennial sore spot for Musk—but “all news is to some degree propaganda,” meaning he won’t label actual state-affiliated propaganda outlets on his platform to distinguish their stories from those of the New York Times.

In his mind, they’re engaged in the same activity, so he strikes the faux-populist note that the people can decide for themselves what is true, regardless of objectively very different track records from different sources.

Musk’s “just asking questions” maneuver is a classic Trump tactic that enables him to advertise conspiracy theories while maintaining a sort of deniability.

At what point should we infer that he’s taking the concerns of someone like Loomer seriously not despite but because of her unhinged beliefs?

Musk’s skepticism seems largely to extend to criticism of the far-right, while his credulity for right-wing sources is boundless.

This is part of the argument for content moderation that limits the dispersal of bullshit: People simply don’t have the time, energy, or inclination to seek out the boring truth when stimulated by some online outrage.

Refuting bullshit requires some technological literacy, perhaps some policy knowledge, but most of all it requires time and a willingness to challenge your own prior beliefs, two things that are in precious short supply online.

Brandolini’s Law holds that the amount of energy needed to refute bullshit is an order of magnitude bigger than that needed to produce it.

Here we can return to the example of Loomer’s tweet. People did fact-check her, but it hardly matters: Following Musk’s reply, she ended up receiving over 5 million views, an exponentially larger online readership than is normal for her. In the attention economy, this counts as a major win. “Thank you so much for posting about this, @elonmusk!” she gushed in response to his reply. “I truly appreciate it.”

the problem isn’t limited to elevating Loomer. Musk had his own stock of misinformation to add to the pile. After interacting with her account, Musk followed up last Tuesday by tweeting out last week a 2021 Federalist article claiming that Facebook founder Mark Zuckerberg had “bought” the 2020 election, an allegation previously raised by Trump and others, and which Musk had also brought up during his recent interview with Tucker Carlson.

If Zuckerberg wanted to use his vast fortune to tip the election, it would have been vastly more efficient to create a super PAC with targeted get-out-the-vote operations and advertising. Notwithstanding legitimate criticisms one can make about Facebook’s effect on democracy, and whatever Zuckerberg’s motivations, you have to squint hard to see this as something other than a positive act addressing a real problem.

It’s worth mentioning that the refutations I’ve just sketched of the conspiratorial claims made by Loomer and Musk come out to around 1,200 words. The tweets they wrote, read by millions, consisted of fewer than a hundred words in total. That’s Brandolini’s Law in action—an illustration of why Musk’s cynical free-speech-over-all approach amounts to a policy in favor of disinformation and against democracy.

Moderation is a subject where Zuckerberg’s actions provide a valuable point of contrast with Musk. Through Facebook’s independent oversight board, which has the power to overturn the company’s own moderation decisions, Zuckerberg has at least made an effort to have credible outside actors inform how Facebook deals with moderation issues

Meanwhile, we are still waiting on the content moderation council that Elon Musk promised last October:

The problem is about to get bigger than unhinged conspiracy theorists occasionally receiving a profile-elevating reply from Musk. Twitter is the venue that Tucker Carlson, whom advertisers fled and Fox News fired after it agreed to pay $787 million to settle a lawsuit over its election lies, has chosen to make his comeback. Carlson and Musk are natural allies: They share an obsessive anti-wokeness, a conspiratorial mindset, and an unaccountable sense of grievance peculiar to rich, famous, and powerful men who have taken it upon themselves to rail against the “elites,” however idiosyncratically construed

f the rumors are true that Trump is planning to return to Twitter after an exclusivity agreement with Truth Social expires in June, Musk’s social platform might be on the verge of becoming a gigantic rec room for the populist right.

These days, Twitter increasingly feels like a neighborhood where the amiable guy-next-door is gone and you suspect his replacement has a meth lab in the basement.

even if Twitter’s increasingly broken information environment doesn’t sway the results, it is profoundly damaging to our democracy that so many people have lost faith in our electoral system. The sort of claims that Musk is toying with in his feed these days do not help. It is one thing for the owner of a major source of information to be indifferent to the content that gets posted to that platform. It is vastly worse for an owner to actively fan the flames of disinformation and doubt.

The true work of the mathematician is not experienced until the later parts of graduate school, when the student is challenged to create knowledge in the form of a novel proof. It is common to fill page after page with an attempt, the seasons turning, only to arrive precisely where you began, empty-handed — or to realize that a subtle flaw of logic doomed the whole enterprise from its outset. The steady state of mathematical research is to be completely stuck. It is a process that Charles Fefferman of Princeton, himself a onetime math prodigy turned Fields medalist, likens to ‘‘playing chess with the devil.’’ The rules of the devil’s game are special, though: The devil is vastly superior at chess, but, Fefferman explained, you may take back as many moves as you like, and the devil may not. You play a first game, and, of course, ‘‘he crushes you.’’ So you take back moves and try something different, and he crushes you again, ‘‘in much the same way.’’ If you are sufficiently wily, you will eventually discover a move that forces the devil to shift strategy; you still lose, but — aha! — you have your first clue.

Tao has emerged as one of the field’s great bridge-­builders. At the time of his Fields Medal, he had already made discoveries with more than 30 different collaborators. Since then, he has also become a prolific math blogger with a decidedly non-­Gaussian ebullience: He celebrates the work of others, shares favorite tricks, documents his progress and delights at any corrections that follow in the comments. He has organized cooperative online efforts to work on problems. ‘‘Terry is what a great 21st-­century mathematician looks like,’’ Jordan Ellenberg, a mathematician at the University of Wisconsin, Madison, who has collaborated with Tao, told me. He is ‘‘part of a network, always communicating, always connecting what he is doing with what other people are doing.’’

Most mathematicians tend to specialize, but Tao ranges widely, learning from others and then working with them to make discoveries. Markus Keel, a longtime collaborator and close friend, reaches to science fiction to explain Tao’s ability to rapidly digest and employ mathematical ideas: Seeing Tao in action, Keel told me, reminds him of the scene in ‘‘The Matrix’’ when Neo has martial arts downloaded into his brain and then, opening his eyes, declares, ‘‘I know kung fu.’’ The citation for Tao’s Fields Medal, awarded in 2006, is a litany of boundary hopping and notes particularly ‘‘beautiful work’’ on Horn’s conjecture, which Tao completed with a friend he had played foosball with in graduate school. It was a new area of mathematics for Tao, at a great remove from his known stamping grounds. ‘‘This is akin,’’ the citation read, ‘‘to a leading English-­language novelist suddenly producing the definitive Russian novel.’’

For their work, Tao and Green salvaged a crucial bit from an earlier proof done by others, which had been discarded as incorrect, and aimed at a different goal. Other maneuvers came from masterful proofs by Timothy Gowers of England and Endre Szemeredi of Hungary. Their work, in turn, relied on contributions from Erdos, Klaus Roth and Frank Ramsey, an Englishman who died at age 26 in 1930, and on and on, into history. Ask mathematicians about their experience of the craft, and most will talk about an intense feeling of intellectual camaraderie. ‘‘A very central part of any mathematician’s life is this sense of connection to other minds, alive today and going back to Pythagoras,’’ said Steven Strogatz, a professor of mathematics at Cornell University. ‘‘We are having this conversation with each other going over the millennia.’’

As a group, the people drawn to mathematics tend to value certainty and logic and a neatness of outcome, so this game becomes a special kind of torture. And yet this is what any ­would-be mathematician must summon the courage to face down: weeks, months, years on a problem that may or may not even be possible to unlock. You find yourself sitting in a room without doors or windows, and you can shout and carry on all you want, but no one is listening.

An effort to prove that 1 equals 0 is not likely to yield much fruit, it’s true, but the hacker’s mind-set can be extremely useful when doing math. Long ago, mathematicians invented a number that when multiplied by itself equals negative 1, an idea that seemed to break the basic rules of multiplication. It was so far outside what mathematicians were doing at the time that they called it ‘‘imaginary.’’ Yet imaginary numbers proved a powerful invention, and modern physics and engineering could not function without them.

Early encounters with math can be misleading. The subject seems to be about learning rules — how and when to apply ancient tricks to arrive at an answer. Four cookies remain in the cookie jar; the ball moves at 12.5 feet per second. Really, though, to be a mathematician is to experiment. Mathematical research is a fundamentally creative act. Lore has it that when David Hilbert, arguably the most influential mathematician of fin de siècle Europe, heard that a colleague had left to pursue fiction, he quipped: ‘‘He did not have enough imagination for mathematics.’’

Many people think that substantial progress on Navier-­Stokes may be impossible, and years ago, Tao told me, he wrote a blog post concurring with this view. Now he has some small bit of hope. The twin-prime conjecture had the same feel, a sense of breaking through the wall of intimidation that has scared off many aspirants. Outside the world of mathematics, both Navier-­Stokes and the twin-prime conjecture are described as problems. But for Tao and others in the field, they are more like opponents. Tao’s opponent has been known to taunt him, convincing him that he is overlooking the obvious, or to fight back, making quick escapes when none should be possible. Now the opponent appears to have revealed a weakness. But Tao said he has been here before, thinking he has found a way through the defenses, when in fact he was being led into an ambush. ‘‘You learn to get suspicious,’’ Tao said. ‘‘You learn to be on the lookout.’’

Code is too hard to think about. Before trying to understand the attempts themselves, then, it’s worth understanding why this might be: what it is about code that makes it so foreign to the mind, and so unlike anything that came before it.

Technological progress used to change the way the world looked—you could watch the roads getting paved; you could see the skylines rise. Today you can hardly tell when something is remade, because so often it is remade by code.

Software has enabled us to make the most intricate machines that have ever existed. And yet we have hardly noticed, because all of that complexity is packed into tiny silicon chips as millions and millions of lines of cod

The programmer, the renowned Dutch computer scientist Edsger Dijkstra wrote in 1988, “has to be able to think in terms of conceptual hierarchies that are much deeper than a single mind ever needed to face before.” Dijkstra meant this as a warning.

As programmers eagerly poured software into critical systems, they became, more and more, the linchpins of the built world—and Dijkstra thought they had perhaps overestimated themselves.

What made programming so difficult was that it required you to think like a computer.

“The problem is that software engineers don’t understand the problem they’re trying to solve, and don’t care to,” says Leveson, the MIT software-safety expert. The reason is that they’re too wrapped up in getting their code to work.

Though he runs a lab that studies the future of computing, he seems less interested in technology per se than in the minds of the people who use it. Like any good toolmaker, he has a way of looking at the world that is equal parts technical and humane. He graduated top of his class at the California Institute of Technology for electrical engineering,

“The serious problems that have happened with software have to do with requirements, not coding errors.” When you’re writing code that controls a car’s throttle, for instance, what’s important is the rules about when and how and by how much to open it. But these systems have become so complicated that hardly anyone can keep them straight in their head. “There’s 100 million lines of code in cars now,” Leveson says. “You just cannot anticipate all these things.”

a nearly decade-long investigation into claims of so-called unintended acceleration in Toyota cars. Toyota blamed the incidents on poorly designed floor mats, “sticky” pedals, and driver error, but outsiders suspected that faulty software might be responsible

software experts spend 18 months with the Toyota code, picking up where NASA left off. Barr described what they found as “spaghetti code,” programmer lingo for software that has become a tangled mess. Code turns to spaghetti when it accretes over many years, with feature after feature piling on top of, and being woven around

Using the same model as the Camry involved in the accident, Barr’s team demonstrated that there were actually more than 10 million ways for the onboard computer to cause unintended acceleration. They showed that as little as a single bit flip—a one in the computer’s memory becoming a zero or vice versa—could make a car run out of control. The fail-safe code that Toyota had put in place wasn’t enough to stop it

. In all, Toyota recalled more than 9 million cars, and paid nearly $3 billion in settlements and fines related to unintended acceleration.

The problem is that programmers are having a hard time keeping up with their own creations. Since the 1980s, the way programmers work and the tools they use have changed remarkably little.

“Visual Studio is one of the single largest pieces of software in the world,” he said. “It’s over 55 million lines of code. And one of the things that I found out in this study is more than 98 percent of it is completely irrelevant. All this work had been put into this thing, but it missed the fundamental problems that people faced. And the biggest one that I took away from it was that basically people are playing computer inside their head.” Programmers were like chess players trying to play with a blindfold on—so much of their mental energy is spent just trying to picture where the pieces are that there’s hardly any left over to think about the game itself.

The fact that the two of them were thinking about the same problem in the same terms, at the same time, was not a coincidence. They had both just seen the same remarkable talk, given to a group of software-engineering students in a Montreal hotel by a computer researcher named Bret Victor. The talk, which went viral when it was posted online in February 2012, seemed to be making two bold claims. The first was that the way we make software is fundamentally broken. The second was that Victor knew how to fix it.

This is the trouble with making things out of code, as opposed to something physical. “The complexity,” as Leveson puts it, “is invisible to the eye.”

in early 2012, Victor had finally landed upon the principle that seemed to thread through all of his work. (He actually called the talk “Inventing on Principle.”) The principle was this: “Creators need an immediate connection to what they’re creating.” The problem with programming was that it violated the principle. That’s why software systems were so hard to think about, and so rife with bugs: The programmer, staring at a page of text, was abstracted from whatever it was they were actually making.

“Our current conception of what a computer program is,” he said, is “derived straight from Fortran and ALGOL in the late ’50s. Those languages were designed for punch cards.”

WYSIWYG (pronounced “wizzywig”) came along. It stood for “What You See Is What You Get.”

Victor’s point was that programming itself should be like that. For him, the idea that people were doing important work, like designing adaptive cruise-control systems or trying to understand cancer, by staring at a text editor, was appalling.

With the right interface, it was almost as if you weren’t working with code at all; you were manipulating the game’s behavior directly.

When the audience first saw this in action, they literally gasped. They knew they weren’t looking at a kid’s game, but rather the future of their industry. Most software involved behavior that unfolded, in complex ways, over time, and Victor had shown that if you were imaginative enough, you could develop ways to see that behavior and change it, as if playing with it in your hands. One programmer who saw the talk wrote later: “Suddenly all of my tools feel obsolete.”

hen John Resig saw the “Inventing on Principle” talk, he scrapped his plans for the Khan Academy programming curriculum. He wanted the site’s programming exercises to work just like Victor’s demos. On the left-hand side you’d have the code, and on the right, the running program: a picture or game or simulation. If you changed the code, it’d instantly change the picture. “In an environment that is truly responsive,” Resig wrote about the approach, “you can completely change the model of how a student learns ... [They] can now immediately see the result and intuit how underlying systems inherently work without ever following an explicit explanation.” Khan Academy has become perhaps the largest computer-programming class in the world, with a million students, on average, actively using the program each month.

The ideas spread. The notion of liveness, of being able to see data flowing through your program instantly, made its way into flagship programming tools offered by Google and Apple. The default language for making new iPhone and Mac apps, called Swift, was developed by Apple from the ground up to support an environment, called Playgrounds, that was directly inspired by Light Table.

“Typically the main problem with software coding—and I’m a coder myself,” Bantegnie says, “is not the skills of the coders. The people know how to code. The problem is what to code. Because most of the requirements are kind of natural language, ambiguous, and a requirement is never extremely precise, it’s often understood differently by the guy who’s supposed to code.”

In a pair of later talks, “Stop Drawing Dead Fish” and “Drawing Dynamic Visualizations,” Victor went one further. He demoed two programs he’d built—the first for animators, the second for scientists trying to visualize their data—each of which took a process that used to involve writing lots of custom code and reduced it to playing around in a WYSIWYG interface.

Victor suggested that the same trick could be pulled for nearly every problem where code was being written today. “I’m not sure that programming has to exist at all,” he told me. “Or at least software developers.” In his mind, a software developer’s proper role was to create tools that removed the need for software developers. Only then would people with the most urgent computational problems be able to grasp those problems directly, without the intermediate muck of code.

Victor implored professional software developers to stop pouring their talent into tools for building apps like Snapchat and Uber. “The inconveniences of daily life are not the significant problems,” he wrote. Instead, they should focus on scientists and engineers—as he put it to me, “these people that are doing work that actually matters, and critically matters, and using really, really bad tools.”

Bantegnie’s company is one of the pioneers in the industrial use of model-based design, in which you no longer write code directly. Instead, you create a kind of flowchart that describes the rules your program should follow (the “model”), and the computer generates code for you based on those rules

In a model-based design tool, you’d represent this rule with a small diagram, as though drawing the logic out on a whiteboard, made of boxes that represent different states—like “door open,” “moving,” and “door closed”—and lines that define how you can get from one state to the other. The diagrams make the system’s rules obvious: Just by looking, you can see that the only way to get the elevator moving is to close the door, or that the only way to get the door open is to stop.

. In traditional programming, your task is to take complex rules and translate them into code; most of your energy is spent doing the translating, rather than thinking about the rules themselves. In the model-based approach, all you have is the rules. So that’s what you spend your time thinking about. It’s a way of focusing less on the machine and more on the problem you’re trying to get it to solve.

“Everyone thought I was interested in programming environments,” he said. Really he was interested in how people see and understand systems—as he puts it, in the “visual representation of dynamic behavior.” Although code had increasingly become the tool of choice for creating dynamic behavior, it remained one of the worst tools for understanding it. The point of “Inventing on Principle” was to show that you could mitigate that problem by making the connection between a system’s behavior and its code immediate.

On this view, software becomes unruly because the media for describing what software should do—conversations, prose descriptions, drawings on a sheet of paper—are too different from the media describing what software does do, namely, code itself.

for this approach to succeed, much of the work has to be done well before the project even begins. Someone first has to build a tool for developing models that are natural for people—that feel just like the notes and drawings they’d make on their own—while still being unambiguous enough for a computer to understand. They have to make a program that turns these models into real code. And finally they have to prove that the generated code will always do what it’s supposed to.

tice brings order and accountability to large codebases. But, Shivappa says, “it’s a very labor-intensive process.” He estimates that before they used model-based design, on a two-year-long project only two to three months was spent writing code—the rest was spent working on the documentation.

uch of the benefit of the model-based approach comes from being able to add requirements on the fly while still ensuring that existing ones are met; with every change, the computer can verify that your program still works. You’re free to tweak your blueprint without fear of introducing new bugs. Your code is, in FAA parlance, “correct by construction.”

“people are not so easily transitioning to model-based software development: They perceive it as another opportunity to lose control, even more than they have already.”

The bias against model-based design, sometimes known as model-driven engineering, or MDE, is in fact so ingrained that according to a recent paper, “Some even argue that there is a stronger need to investigate people’s perception of MDE than to research new MDE technologies.”

“Human intuition is poor at estimating the true probability of supposedly ‘extremely rare’ combinations of events in systems operating at a scale of millions of requests per second,” he wrote in a paper. “That human fallibility means that some of the more subtle, dangerous bugs turn out to be errors in design; the code faithfully implements the intended design, but the design fails to correctly handle a particular ‘rare’ scenario.”

Newcombe was convinced that the algorithms behind truly critical systems—systems storing a significant portion of the web’s data, for instance—ought to be not just good, but perfect. A single subtle bug could be catastrophic. But he knew how hard bugs were to find, especially as an algorithm grew more complex. You could do all the testing you wanted and you’d never find them all.

An algorithm written in TLA+ could in principle be proven correct. In practice, it allowed you to create a realistic model of your problem and test it not just thoroughly, but exhaustively. This was exactly what he’d been looking for: a language for writing perfect algorithms.

TLA+, which stands for “Temporal Logic of Actions,” is similar in spirit to model-based design: It’s a language for writing down the requirements—TLA+ calls them “specifications”—of computer programs. These specifications can then be completely verified by a computer. That is, before you write any code, you write a concise outline of your program’s logic, along with the constraints you need it to satisfy

Programmers are drawn to the nitty-gritty of coding because code is what makes programs go; spending time on anything else can seem like a distraction. And there is a patient joy, a meditative kind of satisfaction, to be had from puzzling out the micro-mechanics of code. But code, Lamport argues, was never meant to be a medium for thought. “It really does constrain your ability to think when you’re thinking in terms of a programming language,”

Code makes you miss the forest for the trees: It draws your attention to the working of individual pieces, rather than to the bigger picture of how your program fits together, or what it’s supposed to do—and whether it actually does what you think. This is why Lamport created TLA+. As with model-based design, TLA+ draws your focus to the high-level structure of a system, its essential logic, rather than to the code that implements it.

But TLA+ occupies just a small, far corner of the mainstream, if it can be said to take up any space there at all. Even to a seasoned engineer like Newcombe, the language read at first as bizarre and esoteric—a zoo of symbols.

this is a failure of education. Though programming was born in mathematics, it has since largely been divorced from it. Most programmers aren’t very fluent in the kind of math—logic and set theory, mostly—that you need to work with TLA+. “Very few programmers—and including very few teachers of programming—understand the very basic concepts and how they’re applied in practice. And they seem to think that all they need is code,” Lamport says. “The idea that there’s some higher level than the code in which you need to be able to think precisely, and that mathematics actually allows you to think precisely about it, is just completely foreign. Because they never learned it.”

“In the 15th century,” he said, “people used to build cathedrals without knowing calculus, and nowadays I don’t think you’d allow anyone to build a cathedral without knowing calculus. And I would hope that after some suitably long period of time, people won’t be allowed to write programs if they don’t understand these simple things.”

Programmers, as a species, are relentlessly pragmatic. Tools like TLA+ reek of the ivory tower. When programmers encounter “formal methods” (so called because they involve mathematical, “formally” precise descriptions of programs), their deep-seated instinct is to recoil.

Formal methods had an image problem. And the way to fix it wasn’t to implore programmers to change—it was to change yourself. Newcombe realized that to bring tools like TLA+ to the programming mainstream, you had to start speaking their language.

he presented TLA+ as a new kind of “pseudocode,” a stepping-stone to real code that allowed you to exhaustively test your algorithms—and that got you thinking precisely early on in the design process. “Engineers think in terms of debugging rather than ‘verification,’” he wrote, so he titled his internal talk on the subject to fellow Amazon engineers “Debugging Designs.” Rather than bemoan the fact that programmers see the world in code, Newcombe embraced it. He knew he’d lose them otherwise. “I’ve had a bunch of people say, ‘Now I get it,’” Newcombe says.

In the world of the self-driving car, software can’t be an afterthought. It can’t be built like today’s airline-reservation systems or 911 systems or stock-trading systems. Code will be put in charge of hundreds of millions of lives on the road and it has to work. That is no small task.

Our 21st century tools and technologies that seem to work miracles and make us feel invincible and powerful, are practically defenseless in the face of COVID-19. We are left with only the primitive weapons of cloth masks and keeping our distance.

Where we once dismissed people who bag our groceries, or drive our kids’ school buses, for example, now we realize they are “essential workers.” 

“There is enough.” As Buckminster Fuller, futurist, famed architect, and creator of the geodesic dome said, there is enough of every resource for everyone on the planet; it’s just a matter of distribution.

We are experiencing that now, watching sharing be taken to an entirely new level to manage COVID-19.  

Competitors were in their corners, battling out for marketshare or geopolitical power and dominance.

With literally everyone working from home and only “seeing” each other on Zoom or Skype, how we look, what we wear, and the usual vanity concerns are out the window.

The way women lead was (still) being undervalued and dismissed as “too soft” or second-rate in many powerful circles.

Women are the majority force in the healthcare battalions keeping us and our loved ones alive and giving comfort to those who lose their battles in our absence. 

There are many more shifts occurring from COVID-19 – from a greater appreciation for nature, to more kindness and compassion, a clearer sense of how we spend our time, to careers and businesses.

On the republican picture, owning is a relationship that presupposes law, if only the inchoate law of informal custom.

The conflict between the images is important because it shows up in alternative visions of the economy and the relationship between the economy and the state.

You own something only insofar as it is a matter of accepted convention that given the way you came to hold it — given public recognition of the title you have to the property — you enjoy public protection against those who would take it from you

This view of property, prominent in Rousseau and presupposed in the broader republican tradition, is scarcely questionable in view of the salient diversity in systems of property

These observations, scarcely richer than platitudes, are important for giving us a perspective on the market and the economy, undermining the libertarian image. That picture represents the market as a res privata, a private thing, suggesting that the role of the state is merely to lay low the hills in the way of the market and smooth the paths for its operation. And so it depicts any other interventions of government in the market as dubious on philosophical, not just empirical, grounds.

this image accounts for the continuing attachment to austerity among those on the right. They are philosophically opposed to Keynesianism, not just opposed on empirical grounds, and their ideological stance makes empirically based arguments for Keynesianism invisible to them.

In distinguishing between objectivity and impartiality, Human Events’ editors created a space where “bias” was an appropriate journalistic value, one that could work in tandem with objectivity.

two events in the early 1960s convinced the right that creating conservative media wasn’t enough to achieve balance. Conservatives would also have to discredit existing media.

Conservative discontent with the FCC focused on the Fairness Doctrine

Conservatives felt the Fairness Doctrine unfairly tilted the playing field against them. Though devised to encourage controversial broadcasting, in practice the doctrine often led broadcasters to avoid controversy so they wouldn’t have to give away free airtime. To conservatives, avoiding controversy inevitably meant silencing right-wing voices.

the right repeatedly challenged the central assumptions the FCC—and Americans more broadly—made about journalism. For much of the 20th century, journalists cleaved to the idea of objectivity. Opinion and analysis had their place, but that place was distinct and separate from the news. Conservative broadcasts, on the other hand, were by their very nature opinion. Fairness dictated these partisan broadcasters provide airtime for a response.

Conservatives saw the media landscape differently. They viewed objectivity as a mask concealing entrenched liberal bias, hiding the slanted reporting that dominated American media. Because of this, the right believed fairness did not require a response to conservative broadcasts; conservative broadcasts were the response. Unable to bring the FCC around to their position, conservatives increasingly saw the commission as a powerful government agency dedicated to maintaining media’s liberal tilt.

In calling coverage of Goldwater “unfounded in fact,” Manion was making another argument to which conservatives anchored their charges of liberal bias: Established media did not just slant the news—they fabricated it. And if established media couldn’t be counted on for truth, the argument went, then surely they should be required to offer both sides of the argument. In the years that followed, conservatives began an active campaign against liberal bias

The combined forces of the administration and its conservative media-research wing had an effect. By 1971 CBS Radio had launched Spectrum, a debate show featuring conservatives like Stan Evans, James Kilpatrick, and Phyllis Schlafly. That same year 60 Minutes pitted conservative Kilpatrick against liberal Nicholas von Hoffman in a regular segment called “Point/Counterpoint.” By then, even the publisher of Human Events, in the midst of selling his paper as an alternative to liberal media, had to admit that conservatives were popping up all over established media—even the editorial pages of “that holy house organ of Liberalism—the New York Times.”

So balance and bias became part of the American news diet long before Ailes entered the conservative media game. Why does that matter? It makes Ailes’s successes at Fox News far more understandable—and far less Ailes-centric. By the time Ailes entered the game, the American right had spent a generation seeking out conservative alternatives to the “liberal media,” and America’s news media was already in the midst of a revolution that made Fox News possible.

I actually think Peterson was right to observe that it’s remarkable how many students at the universities where they tested some of his theories hadn’t been told these things. Though I thought it was interesting that he seemed to think that teaching this kind of thing was a job for the educational system rather than the parents

I think perhaps we’re both lucky in that though our backgrounds are different, we both come from relatively stable families with parents and surrounding adults who inculcated these “rules” intrinsically, from our youth on. So the Peterson gospel doesn’t feel new to us.

The fact that there are whole swaths of our generation who are advantaged by already knowing this information about how to make your life better, and another whole swath who is being left behind, character and life-formation wise, because they don’t. And they are left to rely on Jordan Peterson.

He is convinced of the importance and significance of these stories, these words — and religion, and its significance. At one point he stated that he didn’t have a materialist view of the world, but actually a “deeply religious” one.

Just in the week or so I was reading “12 Rules,” I had several men my age come up to me on buses or in coffee shops and strike up conversations with me about Peterson — the one thing they all talked about right away was how the book had a lot of “hard truths” that they needed to hear

largely the message you come away with is that if you don’t like the way things are going, it’s your fault and your fault alone. And that’s an easier message to believe when you’re a white male and systemic obstacles aren’t really a thing you run into.

Jordan Peterson professes not to be religious, but he is. His book is built on what he describes as archetypal myths from different cultures, but leans *very* heavily on Judeo-Christian ones especially — Cain and Abel and the stories of Jesus’s life, from his temptation in the desert to his death and resurrection.

This tendency was even more pronounced in his live lecture. Basically every line, every piece of advice he gave, was supported by a Bible verse. At one point, he quoted the gospel of Matthew: “Knock and the door will be opened to you” — and said, “This is how life works, ACTUALLY” — basically glaring at the crowd and daring them to disagree.

One thing that’s definitely central to the book is telling people (particularly men) that life is hard, and you need to get it together.

He’s not keeping great company. But I think his personal work and statements are generally benign, in many cases actually helpful, in that they urge young people to seek out a better-structured and more meaningful life.

I agree it’s inaccurate to label him as alt-right, though that is a low bar to clear. Frankly I see him more as a mainstream conservative. I think part of the reason people get this wrong is that there’s a big gap between what boosted his fame and what the central thrust of his book is

I think “traditionalist” is probably the best label for him — both because his views are traditionalist and because his worldview is so dependent on traditions (or at least what he sees as traditions.)

Trade, or exchange, is engaged in precisely because both parties benefit; if they did not expect to gain, they would not agree to the exchange.

This simple reasoning refutes the argument against free trade typical of the “mercantilist” period of sixteenth- to eighteenth-century Europe and classically expounded by the famed sixteenth-century French essayist Montaigne.

At each stage of production from natural resource to consumer good, money is voluntarily exchanged for capital goods, labor services, and land resources. At each step of the way, terms of exchanges, or prices, are determined by the voluntary interactions of suppliers and demanders. This market is “free” because choices, at each step, are made freely and voluntarily.

We can immediately see the fallacy in this still-popular viewpoint: the willingness and even eagerness to trade means that both parties benefit. In modern game-theory jargon, trade is a win-win situation, a “positive-sum” rather than a “zero-sum” or “negative-sum” game.

Each one values the two goods or services differently, and these differences set the scene for an exchange.

Two factors determine the terms of any agreement: how much each participant values each good in question, and each participant’s bargaining skills.

the market in relation to how favorably buyers evaluate these goods—in shorthand, by the interaction of their supply with the demand for them.

On the other hand, given the buyers’ evaluation, or demand, for a good, if the supply increases, each unit of supply—each baseball card or loaf of bread—will fall in value, and therefore the price of the good will fall. The reverse occurs if the supply of the good decreases.

The market, then, is not simply an array; it is a highly complex, interacting latticework of exchanges.

Production begins with natural resources, and then various forms of machines and capital goods, until finally, goods are sold to the consumer.

The mercantilists argued that in any trade, one party can benefit only at the expense of the other—that in every transaction there is a winner and a loser, an “exploiter” and an “exploited.”

A common charge against the free-market society is that it institutes “the law of the jungle,” of “dog eat dog,” that it spurns human cooperation for competition and exalts material success as opposed to spiritual values, philosophy, or leisure activities.

Saving and investment can then develop capital goods and increase the productivity and wages of workers, thereby increasing their standard of living.

The free competitive market also rewards and stimulates technological innovation that allows the innovator to get a head start in satisfying consumer wants in new and creative ways.

Government, in every society, is the only lawful system of coercion. Taxation is a coerced exchange, and the heavier the burden of taxation on production, the more likely it is that economic growth will falter and decline

The ultimate in government coercion is socialism.

Under socialist central planning the socialist planning board lacks a price system for land or capital goods.

Market socialism is, in fact, a contradiction in terms.

The fashionable discussion of market socialism often overlooks one crucial aspect of the market: When two goods are exchanged, what is really exchanged is the property titles in those goods.

This means that the key to the existence and flourishing of the free market is a society in which the rights and titles of private property are respected, defended, and kept secure.

The key to socialism, on the other hand, is government ownership of the means of production, land, and capital goods.

Under socialism, therefore, there can be no market in land or capital goods worthy of the name.

ome critics of the free market argue that property rights are in conflict with “human” rights. But the critics fail to realize that in a free-market system, every person has a property right over his own person and his own labor and can make free contracts for those services.

The free market and the free price system make goods from around the world available to consumers.

It is the coercive countries with little or no market activity—the notable examples in the last half of the twentieth century were the communist countries—where the grind of daily existence not only impoverishes people materially but also deadens their spirit.

there are plenty of ludicrous lies and insane conspiracy theories that Fox News spews out on a daily basis. But it remains tethered to, and in conversation with, the mainstream. That’s where its power comes from.

a closed system like Parler can’t influence the mainstream media, because no one in the media knows or cares what goes on there.

They were right about control but wrong about the controllers. Our Internet handlers, not government, are using operant conditioning to modify our behavior today.

we now know how to design cue, activity, and reward systems to more effectively leverage our brain chemistry and program human behavior.

The beauty of the Internet is that by combining big data, behavioral targeting, wearable and mobile devices, and GPS, application developers can design more effective operant conditioning environments and keep us in virtual Skinner boxes as long as we have a smart phone in our pockets.

Operant conditioning techniques will and are currently being used to program the behavior of susceptible Internet users -- young men who play MMORPG (Massively Multiplayer Online Role-Playing Games) for forty hours a week, women who commit hours to social networks, shoppers seeking the thrill of a deal, and poker players.

As smart devices become integrated into our lives, retailers who will know where we are standing in stores and fast food restaurants and bars will find ways to provide us with cues to trigger behaviors.

The real question is how many hundreds of millions of us will become susceptible to what I believe will prove to be history's most potent marketing techniques.

This suggests that we imagine a complex reality in which things might be true — materially, spiritually, psychologically

Westerners, by contrast, not only tolerate fantasy play but actively encourage it, for adults as well as for children. We are novel readers, movie watchers and game players.

“Inventive pretend,” in which children pretend the fantastic or impossible (enchanted princesses, dragon hunters) “is rarely — if ever — observed in non-industrialized or traditional cultures,”

Science leads us to draw a sharp line between what is real and what is unreal. At the same time, we live in an age in which we are exquisitely aware that there are many theories, both religious and scientific, to explain the world, and many ways to be human.

Probably fiction does for us what the vision of Aslan did for Bob: it helps us to learn what we find emotionally true in the face of irreconcilable contradictions.

fiction teaches us how to think about what we take to be true. In the cacophony of an information-soaked age, we need it.

The iron law of Darwinian evolution is that everything that exists strives with all its power to reproduce, to extend life into the future, and that every feature of every creature can be explained as an adaptation toward this end. For the artist to deny any connection with the enterprise of life, then, is to assert his freedom from this universal imperative; to reclaim negatively the autonomy that evolution seems to deny to human beings. It is only because we can freely choose our own ends that we can decide not to live for life, but for some other value that we posit. The artist’s decision to produce spiritual offspring rather than physical ones is thus allied to the monk’s celibacy and the warrior’s death for his country, as gestures that deny the empire of mere life.

Animals produce beauty on their bodies; humans can also produce it in their artifacts. The natural inference, then, would be that art is a human form of sexual display, a way for males to impress females with spectacularly redundant creations.

For Darwin, the human sense of beauty was not different in kind from the bird’s.

Still, Darwin recognized that the human sense of beauty was mediated by “complex ideas and trains of thought,” which make it impossible to explain in terms as straightforward as a bird’s:

Put more positively, one might say that any given work of art can be discussed critically and historically, but not deduced from the laws of evolution.

with the rise of evolutionary psychology, it was only a matter of time before the attempt was made to explain art in Darwinian terms. After all, if ethics and politics can be explained by game theory and reciprocal altruism, there is no reason why aesthetics should be different: in each case, what appears to be a realm of human autonomy can be reduced to the covert expression of biological imperatives

Still, there is an unmistakable sense in discussions of Darwinian aesthetics that by linking art to fitness, we can secure it against charges of irrelevance or frivolousness—that mattering to reproduction is what makes art, or anything, really matter.

The first popular effort in this direction was the late Denis Dutton’s much-discussed book The Art Instinct, which appeared in 2009.

Dutton’s Darwinism was aesthetically conservative: “Darwinian aesthetics,” he wrote, “can restore the vital place of beauty, skill, and pleasure as high artistic values.” Dutton’s argument has recently been reiterated and refined by a number of new books,

“The universality of art and artistic behaviors, their spontaneous appearance everywhere across the globe ... and the fact that in most cases they can be easily recognized as artistic across cultures suggest that they derive from a natural, innate source: a universal human psychology.”

Again like language, art is universal in the sense that any local expression of it can be “learned” by anyone.

Yet earlier theorists of evolution were reluctant to say that art was an evolutionary adaptation like language, for the simple reason that it does not appear to be evolutionarily adaptive.

Stephen Jay Gould suggested that art was not an evolutionary adaptation but what he called a “spandrel”—that is, a showy but accidental by-product of other adaptations that were truly functiona

the very words “success” and “failure,” despite themselves, bring an emotive and ethical dimension into the discussion, so impossible is it for human beings to inhabit a valueless world. In the nineteenth century, the idea that fitness for survival was a positive good motivated social Darwinism and eugenics. Proponents of these ideas thought that in some way they were serving progress by promoting the flourishing of the human race, when the basic premise of Darwinism is that there is no such thing as progress or regress, only differential rates of reproduction

In particular, Darwin suggests that it is impossible to explain the history or the conventions of any art by the general imperatives of evolution

Boyd begins with the premise that human beings are pattern-seeking animals: both our physical perceptions and our social interactions are determined by our brain’s innate need to find and to

Art, then, can be defined as the calisthenics of pattern-finding. “Just as animal physical play refines performance, flexibility, and efficiency in key behaviors,” Boyd writes, “so human art refines our performance in our key perceptual and cognitive modes, in sight (the visual arts), sound (music), and social cognition (story). These three modes of art, I propose, are adaptations ... they show evidence of special design in humans, design that offers survival and especially reproductive advantages.”

make coherent patterns

“Over 100 likes is good, for me. And comments. You just comment to make a joke or tag someone.”

The best thing is the little notification box, which means someone liked, tagged or followed her on Instagram. She has 604 followers. There are only 25 photos on her page because she deletes most of what she posts. The ones that don’t get enough likes, don’t have good enough lighting or don’t show the coolest moments in her life must be deleted.

Sociologists, advertisers, stock market analysts – everyone wants to know what happens when the generation born glued to screens has to look up and interact with the world.

“It kind of, almost, promotes you as a good person. If someone says, ‘tbh you’re nice and pretty,’ that kind of, like, validates you in the comments. Then people can look at it and say ‘Oh, she’s nice and pretty.’ ”

School is where she thrives: She is beloved by her teachers, will soon star as young Simba in the eighth-grade performance of “The Lion King” musical, and gets straight A’s. Her school doesn’t offer a math course challenging enough for her, so she takes honors algebra online through Johns Hopkins University.

“Happy birthday posts are a pretty big deal,” she says. “It really shows who cares enough to put you on their page.”

He checks the phone bill to see who she’s called and how much she’s been texting, but she barely calls anyone and chats mostly through Snapchat, where her messages disappear.

Some of Katherine’s very best friends have never been to her house, or she to theirs. To Dave, it seems like they rarely hang out, but he knows that to her, it seems like they’re together all the time.

Dave Pommerening wants to figure out how to get her to use it less. One month, she ate up 18 gigabytes of data. Most large plans max out at 10. He intervened and capped her at four GB. “I don’t want to crimp it too much,” he says. “That’s something, from my perspective, I’m going to have to figure out, how to get my arms around that.”

Even if her dad tried snooping around her apps, the true dramas of teenage girl life are not written in the comments. Like how sometimes, Katherine’s friends will borrow her phone just to un-like all the Instagram photos of girls they don’t like. Katherine can’t go back to those girls’ pages and re-like the photos because that would be stalking, which is forbidden.

Or how last week, at the middle school dance, her friends got the phone numbers of 10 boys, but then they had to delete five of them because they were seventh-graders. And before she could add the boys on Snapchat, she realized she had to change her username because it was her childhood nickname and that was totally embarrassing.

Then, because she changed her username, her Snapchat score reverted to zero. The app awards about one point for every snap you send and receive. It’s also totally embarrassing and stressful to have a low Snapchat score. So in one day, she sent enough snaps to earn 1,000 points.

Snapchat is where flirting happens. She doesn’t know anyone who has sent a naked picture to a boy, but she knows it happens with older girls, who know they have met the right guy.

Nothing her dad could find on her phone shows that for as good as Katherine is at math, basketball and singing, she wants to get better at her phone. To be one of the girls who knows what to post, how to caption it, when to like, what to comment.

Katherine doesn’t need magazines or billboards to see computer-perfect women. They’re right on her phone, all the time, in between photos of her normal-looking friends. There’s Aisha, there’s Kendall Jenner’s butt. There’s Olivia, there’s YouTube star Jenna Marbles in lingerie.

The whole world is at her fingertips and has been for years. This, Katherine offers as a theory one day, is why she doesn’t feel like she’s 13 years old at all. She’s probably, like, 16.

“I don’t feel like a child anymore” she says. “I’m not doing anything childish. At the end of sixth grade” — when all her friends got phones and downloaded Snapchat, Instagram and Twitter — “I just stopped doing everything I normally did. Playing games at recess, playing with toys, all of it, done.”

Her scooter sat in the garage, covered in dust. Her stuffed animals were passed down to Lila. The wooden playground in the back yard stood empty. She kept her skateboard with neon yellow wheels, because riding it is still cool to her friends.

On the morning of her 14th birthday, Katherine wakes up to an alarm ringing on her phone. It’s 6:30 a.m. She rolls over and shuts it off in the dark. Her grandparents, here to celebrate the end of her first year of teenagehood, are sleeping in the guest room down the hall. She can hear the dogs shuffling across the hardwood downstairs, waiting to be fed. Propping herself up on her peace-sign-covered pillow, she opens Instagram. Later, Lila will give her a Starbucks gift card. Her dad will bring doughnuts to her class. Her grandparents will take her to the Melting Pot for dinner. But first, her friends will decide whether to post pictures of Katherine for her birthday. Whether they like her enough to put a picture of her on their page. Those pictures, if they come, will get likes and maybe tbhs. They should be posted in the morning, any minute now. She scrolls past a friend posing in a bikini on the beach. Then a picture posted by Kendall Jenner. A selfie with coffee. A basketball Vine. A selfie with a girl’s tongue out. She scrolls, she waits. For that little notification box to appear.

In the last four years, Stanford has increased the number of senior faculty by 25 percent, and 11 scholars with millions in cumulative salary have either been recruited from other top programs or resisted poaching attempts by those programs.

The specialties of the new recruits vary, but they are all examples of how the momentum in economics has shifted away from theoretical modeling and toward “empirical microeconomics,” the analysis of how things work in the real world, often arranging complex experiments or exploiting large sets of data. That kind of work requires lots of research assistants, work across disciplines including fields like sociology and computer science, and the use of advanced computational techniques unavailable a generation ago.

the scholars who have newly signed on with Stanford described a university particularly well suited to research in that vein, with a combination of lab space, strong budgets for research support and proximity to engineering talent.

The Chicago School, under the intellectual imprint of Milton Friedman, was a leader in neoclassical thought that emphasizes the efficiency of markets and the risks of government intervention. M.I.T.’s economics department has a long record of economic thought in the Keynesian tradition, and it produced several of the top policy makers who have guided the world economy through the tumultuous last several years.

“There isn’t a Stanford school of thought,” said B. Douglas Bernheim, chairman of the university’s economics department. “This isn’t a doctrinaire place. Generally doctrine involves simplification, and increasingly we recognize that these social issues we’re trying to figure out are phenomenally complicated. The consensus at Stanford has focused around the idea that you have to be open to a lot of approaches and ways of thinking about things, and to be rigorous, thorough and careful in bringing the highest standard of craft to bear on your research.”

“My sense is this is a good development for economics,” Mr. Chetty said. “I think Stanford is going to be another great department at the level of Harvard and M.I.T. doing this type of work, which is an example of economics becoming a deeper field. It’s a great thing for all the universities — I don’t think it’s a zero-sum game.”

While science and philosophy do at times overlap, they are fundamentally different approaches to understanding. So philosophers should not add to the conceptual confusion that subsumes all knowledge into science.

various disciplines we ordinarily treat as science are at least as — if not more —philosophical than scientific. Take for example mathematics, theoretical physics, psychology and economics. These are predominately rational conceptual disciplines. That is, they are not chiefly reliant on empirical observation. For unlike science, they may be conducted while sitting in an armchair with eyes closed.

unlike empirical observations, which may be mistaken or incomplete, philosophical findings depend primarily on rational and logical principles. As such, whereas science tends to alter and update its findings day to day through trial and error, logical deductions are timeless.

while mathematics is empirically testable at such rudimentary levels, it stops being so in its purest forms, like analysis and number theory. Proofs in these areas are conducted entirely conceptually

Logically fallacious arguments can be rather sophisticated and persuasive. But they are nevertheless invalid and always will be. Exposing such errors is part of philosophy’s stock and trade.

Wittgenstein showed that an ordinary word such as “game” is used consistently in myriad contrasting ways without possessing any essential unifying definition. Though this may seem impossible, the meaning of such terms is actually determined by their contextual usage

Ultimately as a result of Wittgenstein’s philosophy, we know that natural language is a public phenomenon that cannot logically be invented in isolation.

These are essentially conceptual clarifications. And as such, they are relatively timeless philosophical truths.

This is also why jurisprudence qualifies as an objective body of knowledge

Supreme Court justices are not so much scientific as philosophical experts on the nature of justice. And that is not to say their expertise does not count as genuine knowledge. In the best cases, it rises to the loftier level of wisdom

Though philosophy does sometimes employ thought experiments, these aren’t actually scientific, for they are conducted entirely in the imagination.

in ethics, science cannot necessarily tell us what to value

evidence of how most people happen to be does not necessarily tell us everything about how we should aspire to be. For how we should aspire to be is a conceptual question, namely, of how we ought to act, as opposed to an empirical question of how we do act.