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The crash, which killed all 49 people on board as well as one person on the ground, should never have happened.

aptain’s response to the stall warning, the investigators reported, “should have been automatic, but his improper flight control inputs were inconsistent with his training” and instead revealed “startle and confusion.

Automation has become so sophisticated that on a typical passenger flight, a human pilot holds the controls for a grand total of just three minutes.

We humans have been handing off chores, both physical and mental, to tools since the invention of the lever, the wheel, and the counting bead.

And that, many aviation and automation experts have concluded, is a problem. Overuse of automation erodes pilots’ expertise and dulls their reflexes,

No one doubts that autopilot has contributed to improvements in flight safety over the years. It reduces pilot fatigue and provides advance warnings of problems, and it can keep a plane airborne should the crew become disabled. But the steady overall decline in plane crashes masks the recent arrival of “a spectacularly new type of accident,”

“We’re forgetting how to fly.”

The experience of airlines should give us pause. It reveals that automation, for all its benefits, can take a toll on the performance and talents of those who rely on it. The implications go well beyond safety. Because automation alters how we act, how we learn, and what we know, it has an ethical dimension. The choices we make, or fail to make, about which tasks we hand off to machines shape our lives and the place we make for ourselves in the world.

What pilots spend a lot of time doing is monitoring screens and keying in data. They’ve become, it’s not much of an exaggeration to say, computer operators.

Examples of complacency and bias have been well documented in high-risk situations—on flight decks and battlefields, in factory control rooms—but recent studies suggest that the problems can bedevil anyone working with a computer

That may leave the person operating the computer to play the role of a high-tech clerk—entering data, monitoring outputs, and watching for failures. Rather than opening new frontiers of thought and action, software ends up narrowing our focus.

A labor-saving device doesn’t just provide a substitute for some isolated component of a job or other activity. It alters the character of the entire task, including the roles, attitudes, and skills of the people taking part.

when we work with computers, we often fall victim to two cognitive ailments—complacency and bias—that can undercut our performance and lead to mistakes. Automation complacency occurs when a computer lulls us into a false sense of security. Confident that the machine will work flawlessly and handle any problem that crops up, we allow our attention to drift.

Automation bias occurs when we place too much faith in the accuracy of the information coming through our monitors. Our trust in the software becomes so strong that we ignore or discount other information sources, including our own eyes and ears

Automation is different now. Computers can be programmed to perform complex activities in which a succession of tightly coordinated tasks is carried out through an evaluation of many variables. Many software programs take on intellectual work—observing and sensing, analyzing and judging, even making decisions—that until recently was considered the preserve of humans.

Automation turns us from actors into observers. Instead of manipulating the yoke, we watch the screen. That shift may make our lives easier, but it can also inhibit the development of expertise.

Since the late 1970s, psychologists have been documenting a phenomenon called the “generation effect.” It was first observed in studies of vocabulary, which revealed that people remember words much better when they actively call them to mind—when they generate them—than when they simply read them.

When you engage actively in a task, you set off intricate mental processes that allow you to retain more knowledge. You learn more and remember more. When you repeat the same task over a long period, your brain constructs specialized neural circuits dedicated to the activit

What looks like instinct is hard-won skill, skill that requires exactly the kind of struggle that modern software seeks to alleviate.

In many businesses, managers and other professionals have come to depend on decision-support systems to analyze information and suggest courses of action. Accountants, for example, use the systems in corporate audits. The applications speed the work, but some signs suggest that as the software becomes more capable, the accountants become less so.

You can put limits on the scope of automation, making sure that people working with computers perform challenging tasks rather than merely observing.

Experts used to assume that there were limits to the ability of programmers to automate complicated tasks, particularly those involving sensory perception, pattern recognition, and conceptual knowledge

Who needs humans, anyway? That question, in one rhetorical form or another, comes up frequently in discussions of automation. If computers’ abilities are expanding so quickly and if people, by comparison, seem slow, clumsy, and error-prone, why not build immaculately self-contained systems that perform flawlessly without any human oversight or intervention? Why not take the human factor out of the equation?

The cure for imperfect automation is total automation.

That idea is seductive, but no machine is infallible. Sooner or later, even the most advanced technology will break down, misfire, or, in the case of a computerized system, encounter circumstances that its designers never anticipated. As automation technologies become more complex, relying on interdependencies among algorithms, databases, sensors, and mechanical parts, the potential sources of failure multiply. They also become harder to detect.

conundrum of computer automation.

Because many system designers assume that human operators are “unreliable and inefficient,” at least when compared with a computer, they strive to give the operators as small a role as possible.

People end up functioning as mere monitors, passive watchers of screens. That’s a job that humans, with our notoriously wandering minds, are especially bad at

people have trouble maintaining their attention on a stable display of information for more than half an hour. “This means,” Bainbridge observed, “that it is humanly impossible to carry out the basic function of monitoring for unlikely abnormalities.”

a person’s skills “deteriorate when they are not used,” even an experienced operator will eventually begin to act like an inexperienced one if restricted to just watching.

You can program software to shift control back to human operators at frequent but irregular intervals; knowing that they may need to take command at any moment keeps people engaged, promoting situational awareness and learning.

What’s most astonishing, and unsettling, about computer automation is that it’s still in its early stages.

most software applications don’t foster learning and engagement. In fact, they have the opposite effect. That’s because taking the steps necessary to promote the development and maintenance of expertise almost always entails a sacrifice of speed and productivity.

Learning requires inefficiency. Businesses, which seek to maximize productivity and profit, would rarely accept such a trade-off. Individuals, too, almost always seek efficiency and convenience.

Abstract concerns about the fate of human talent can’t compete with the allure of saving time and money.

The small island of Igloolik, off the coast of the Melville Peninsula in the Nunavut territory of northern Canada, is a bewildering place in the winter.

, Inuit hunters have for some 4,000 years ventured out from their homes on the island and traveled across miles of ice and tundra to search for game. The hunters’ ability to navigate vast stretches of the barren Arctic terrain, where landmarks are few, snow formations are in constant flux, and trails disappear overnight, has amazed explorers and scientists for centuries. The Inuit’s extraordinary way-finding skills are born not of technological prowess—they long eschewed maps and compasses—but of a profound understanding of winds, snowdrift patterns, animal behavior, stars, and tides.

The Igloolik hunters have begun to rely on computer-generated maps to get around. Adoption of GPS technology has been particularly strong among younger Inuit, and it’s not hard to understand why.

But as GPS devices have proliferated on Igloolik, reports of serious accidents during hunts have spread. A hunter who hasn’t developed way-finding skills can easily become lost, particularly if his GPS receiver fails.

The routes so meticulously plotted on satellite maps can also give hunters tunnel vision, leading them onto thin ice or into other hazards a skilled navigator would avoid.

An Inuit on a GPS-equipped snowmobile is not so different from a suburban commuter in a GPS-equipped SUV: as he devotes his attention to the instructions coming from the computer, he loses sight of his surroundings. He travels “blindfolded,” as Aporta puts it

A unique talent that has distinguished a people for centuries may evaporate in a generation.

Computer automation severs the ends from the means. It makes getting what we want easier, but it distances us from the work of knowing. As we transform ourselves into creatures of the screen, we face an existential question: Does our essence still lie in what we know, or are we now content to be defined by what we want?

Scientists think the brain's prefrontal cortex processes experiences, using sensory input from the eyes, ears, nose and mouth, sorts them into categories, and tags the various memory fragments with specific associations (smells of home, friends from camp, bugs, a pet, for example). When a memory cue comes in, the brain searches its circuits for related fragments and assembles them like a jigsaw puzzle. Some fragments bring associated fragments along, which is why one old memory often leads to others. Tastes and smells are particularly evocative

Each time people bring up the same memory, those related fragments and circuits become stronger. "When you are 80 years old, remembering your kindergarten days, it's really the memory of a memory of a memory," says Dr. Devi. That may help explain why children's earliest memories are so unstable: Their neural traces are weak and shallow, whereas the few memories we revisit as we get older lay down stronger traces. Still, because the brain is constantly reassembling the fragments, they are vulnerable to distortion.

“The big problem is that the culture is such that researchers spin their work in a way that tells a prettier story than what they really found,” said Jonathan Schooler, a psychologist at the University of California, Santa Barbara. “It’s almost like everyone is on steroids, and to compete you have to take steroids as well.”

Dr. Stapel published papers on the effect of power on hypocrisy, on racial stereotyping and on how advertisements affect how people view themselves. Many of his findings appeared in newspapers around the world, including The New York Times, which reported in December on his study about advertising and identity.

Dr. Stapel was able to operate for so long, the committee said, in large measure because he was “lord of the data,” the only person who saw the experimental evidence that had been gathered (or fabricated). This is a widespread problem in psychology, said Jelte M. Wicherts, a psychologist at the University of Amsterdam. In a recent survey, two-thirds of Dutch research psychologists said they did not make their raw data available for other researchers to see. “This is in violation of ethical rules established in the field,” Dr. Wicherts said.

In a survey of more than 2,000 American psychologists scheduled to be published this year, Leslie John of Harvard Business School and two colleagues found that 70 percent had acknowledged, anonymously, to cutting some corners in reporting data. About a third said they had reported an unexpected finding as predicted from the start, and about 1 percent admitted to falsifying data.

Also common is a self-serving statistical sloppiness. In an analysis published this year, Dr. Wicherts and Marjan Bakker, also at the University of Amsterdam, searched a random sample of 281 psychology papers for statistical errors. They found that about half of the papers in high-end journals contained some statistical error, and that about 15 percent of all papers had at least one error tha

t changed a reported finding — almost always in opposition to the authors’ hypothesis.

an analysis of 49 studies appearing Wednesday in the journal PLoS One, by Dr. Wicherts, Dr. Bakker and Dylan Molenaar, found that the more reluctant that scientists were to share their data, the more likely that evidence contradicted their reported findings.

“We know the general tendency of humans to draw the conclusions they want to draw — there’s a different threshold,” said Joseph P. Simmons, a psychologist at the University of Pennsylvania’s Wharton School. “With findings we want to see, we ask, ‘Can I believe this?’ With those we don’t, we ask, ‘Must I believe this?’

Three. Marx has often been criticized for oversimplifying the relationship between what he called the economic base and the intellectual superstructure. There is some truth to the charge, but it is largely a case of misplaced concreteness. He was quite aware that ideas influence the relations of production, as witness his comments about various reform movements. But he was primarily concerned with attacking the proposition that ideas had an ethereal and independent origin, and lived a self-contained existence

Four. Two questions take precedence under capitalism: how to turn a short-term profit, and how to keep the system from generating the very opposition that Marx predicted. Those two imperatives have so limited and inhibited the life of the mind, let alone the quality of life, that they amount to a denial of the human spirit. Let us agree that Adam Smith was correct in arguing that entrepreneurial capitalism freed human beings from the constraints of medieval and mercantilist cultures. We must also insist that Marx was correct in explaining how corporate capitalism forged new fetters.

Five. There is much in Lenin's thought and practice that I disagree with, but he understood at least one essential element of Marx's thought. Marx realized that capitalism increasingly defined human beings by their function in the marketplace - and so demeaned and stunted their lives, turning them inwards upon themselves as possessors of trivia.

permit me my summary of Marx's legacy. First, the weaknesses. I have mentioned some of those in passing, so here I will concentrate on the major items.

First, Marx never broke free of the capitalist conception of time defined as short-run success

On balance, however, the strengths of Marx outweigh his weaknesses. We can begin with his detailed, tough and persistent inquiry into the dynamics of capitalism that produced a strategy of intellectual inquiry that has provided a sustained tradition of exciting and consequential scholarship in almost every field of knowledge.

Then we must salute Marx's deep commitment to the proposition that, since human beings make their own history, we can devise and create a more human and equitable life as members of a community. We can change the world. That is indeed the ultimate purpose of knowledge - to change the world for the better.

Next we must acknowledge that the people who have acted on those Marxian contributions have indeed improved the lives of millions of non- and even anti-Marxists

he has seen mounting evidence that using a smartphone, or even hearing one ring or vibrate, produces a welter of distractions that makes it harder to concentrate on a difficult problem or job. The division of attention impedes reasoning and performance.

Another 2015 study, appearing in the Journal of Computer-Mediated Communication, showed that when people hear their phone ring but are unable to answer it, their blood pressure spikes, their pulse quickens, and their problem-solving skills decline.

The researchers recruited 520 undergraduates at UCSD and gave them two standard tests of intellectual acuity. One test gauged “available working-memory capacity,” a measure of how fully a person’s mind can focus on a particular task. The second assessed “fluid intelligence,” a person’s ability to interpret and solve an unfamiliar problem. The only variable in the experiment was the location of the subjects’ smartphones. Some of the students were asked to place their phones in front of them on their desks; others were told to stow their phones in their pockets or handbags; still others were required to leave their phones in a different room.

In both tests, the subjects whose phones were in view posted the worst scores, while those who left their phones in a different room did the best. The students who kept their phones in their pockets or bags came out in the middle. As the phone’s proximity increased, brainpower decreased.

In subsequent interviews, nearly all the participants said that their phones hadn’t been a distraction—that they hadn’t even thought about the devices during the experiment. They remained oblivious even as the phones disrupted their focus and thinking.

In a 2013 study conducted at the University of Essex in England, 142 participants were divided into pairs and asked to converse in private for ten minutes. Half talked with a phone in the room, half without a phone present. The subjects were then given tests of affinity, trust and empathy. “The mere presence of mobile phones,” the researchers reported in the Journal of Social and Personal Relationships, “inhibited the development of interpersonal closeness and trust” and diminished “the extent to which individuals felt empathy and understanding from their partners.”

the “integration of smartphones into daily life” appears to cause a “brain drain” that can diminish such vital mental skills as “learning, logical reasoning, abstract thought, problem solving, and creativity.”

 Smartphones have become so entangled with our existence that, even when we’re not peering or pawing at them, they tug at our attention, diverting precious cognitive resources. Just suppressing the desire to check our phone, which we do routinely and subconsciously throughout the day, can debilitate our thinking.

They found that students who didn’t bring their phones to the classroom scored a full letter-grade higher on a test of the material presented than those who brought their phones. It didn’t matter whether the students who had their phones used them or not: All of them scored equally poorly.

A study of nearly a hundred secondary schools in the U.K., published last year in the journal Labour Economics, found that when schools ban smartphones, students’ examination scores go up substantially, with the weakest students benefiting the most.

Social skills and relationships seem to suffer as well.

Because smartphones serve as constant reminders of all the friends we could be chatting with electronically, they pull at our minds when we’re talking with people in person, leaving our conversations shallower and less satisfying.

A second experiment conducted by the researchers produced similar results, while also revealing that the more heavily students relied on their phones in their everyday lives, the greater the cognitive penalty they suffered.

The evidence that our phones can get inside our heads so forcefully is unsettling. It suggests that our thoughts and feelings, far from being sequestered in our skulls, can be skewed by external forces we’re not even aware o

 Scientists have long known that the brain is a monitoring system as well as a thinking system. Its attention is drawn toward any object that is new, intriguing or otherwise striking — that has, in the psychological jargon, “salience.”

even in the history of captivating media, the smartphone stands out. It is an attention magnet unlike any our minds have had to grapple with before. Because the phone is packed with so many forms of information and so many useful and entertaining functions, it acts as what Dr. Ward calls a “supernormal stimulus,” one that can “hijack” attention whenever it is part of our surroundings — and it is always part of our surroundings.

Imagine combining a mailbox, a newspaper, a TV, a radio, a photo album, a public library and a boisterous party attended by everyone you know, and then compressing them all into a single, small, radiant object. That is what a smartphone represents to us. No wonder we can’t take our minds off it.

The irony of the smartphone is that the qualities that make it so appealing to us — its constant connection to the net, its multiplicity of apps, its responsiveness, its portability — are the very ones that give it such sway over our minds.

Phone makers like Apple and Samsung and app writers like Facebook, Google and Snap design their products to consume as much of our attention as possible during every one of our waking hours

Social media apps were designed to exploit “a vulnerability in human psychology,” former Facebook president Sean Parker said in a recent interview. “[We] understood this consciously. And we did it anyway.”

A quarter-century ago, when we first started going online, we took it on faith that the web would make us smarter: More information would breed sharper thinking. We now know it’s not that simple.

As strange as it might seem, people’s knowledge and understanding may actually dwindle as gadgets grant them easier access to online data stores

In a seminal 2011 study published in Science, a team of researchers — led by the Columbia University psychologist Betsy Sparrow and including the late Harvard memory expert Daniel Wegner — had a group of volunteers read forty brief, factual statements (such as “The space shuttle Columbia disintegrated during re-entry over Texas in Feb. 2003”) and then type the statements into a computer. Half the people were told that the machine would save what they typed; half were told that the statements would be erased.

Afterward, the researchers asked the subjects to write down as many of the statements as they could remember. Those who believed that the facts had been recorded in the computer demonstrated much weaker recall than those who assumed the facts wouldn’t be stored. Anticipating that information would be readily available in digital form seemed to reduce the mental effort that people made to remember it

The researchers dubbed this phenomenon the “Google effect” and noted its broad implications: “Because search engines are continually available to us, we may often be in a state of not feeling we need to encode the information internally. When we need it, we will look it up.”

as the pioneering psychologist and philosopher William James said in an 1892 lecture, “the art of remembering is the art of thinking.”

Only by encoding information in our biological memory can we weave the rich intellectual associations that form the essence of personal knowledge and give rise to critical and conceptual thinking. No matter how much information swirls around us, the less well-stocked our memory, the less we have to think with.

As Dr. Wegner and Dr. Ward explained in a 2013 Scientific American article, when people call up information through their devices, they often end up suffering from delusions of intelligence. They feel as though “their own mental capacities” had generated the information, not their devices. “The advent of the ‘information age’ seems to have created a generation of people who feel they know more than ever before,” the scholars concluded, even though “they may know ever less about the world around them.”

That insight sheds light on society’s current gullibility crisis, in which people are all too quick to credit lies and half-truths spread through social media. If your phone has sapped your powers of discernment, you’ll believe anything it tells you.

Data, the novelist and critic Cynthia Ozick once wrote, is “memory without history.” Her observation points to the problem with allowing smartphones to commandeer our brains

When we constrict our capacity for reasoning and recall or transfer those skills to a gadget, we sacrifice our ability to turn information into knowledge. We get the data but lose the meaning

We need to give our minds more room to think. And that means putting some distance between ourselves and our phones.

Harvard Business School professor emerita Shoshana Zuboff argues in her new book that the Valley’s wealth and power are predicated on an insidious, essentially pathological form of private enterprise—what she calls “surveillance capitalism.” Pioneered by Google, perfected by Facebook, and now spreading throughout the economy, surveillance capitalism uses human life as its raw material. Our everyday experiences, distilled into data, have become a privately-owned business asset used to predict and mold our behavior, whether we’re shopping or socializing, working or voting.

By reengineering the economy and society to their own benefit, Google and Facebook are perverting capitalism in a way that undermines personal freedom and corrodes democracy.

Under the Fordist model of mass production and consumption that prevailed for much of the twentieth century, industrial capitalism achieved a relatively benign balance among the contending interests of business owners, workers, and consumers. Enlightened executives understood that good pay and decent working conditions would ensure a prosperous middle class eager to buy the goods and services their companies produced. It was the product itself — made by workers, sold by companies, bought by consumers — that tied the interests of capitalism’s participants together. Economic and social equilibrium was negotiated through the product.

By removing the tangible product from the center of commerce, surveillance capitalism upsets the equilibrium. Whenever we use free apps and online services, it’s often said, we become the products, our attention harvested and sold to advertisers

this truism gets it wrong. Surveillance capitalism’s real products, vaporous but immensely valuable, are predictions about our future behavior — what we’ll look at, where we’ll go, what we’ll buy, what opinions we’ll hold — that internet companies derive from our personal data and sell to businesses, political operatives, and other bidders.

Unlike financial derivatives, which they in some ways resemble, these new data derivatives draw their value, parasite-like, from human experience.To the Googles and Facebooks of the world, we are neither the customer nor the product. We are the source of what Silicon Valley technologists call “data exhaust” — the informational byproducts of online activity that become the inputs to prediction algorithms

internet companies operate in what Zuboff terms “extreme structural independence from people.” When databases displace goods as the engine of the economy, our own interests, as consumers but also as citizens, cease to be part of the negotiation. We are no longer one of the forces guiding the market’s invisible hand. We are the objects of surveillance and control.

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.

Some soldiers married Asian women and brought them home as war brides, while others primarily viewed Asian women as sexual objects. Both approaches perpetuated stereotypes of Asian women as sexually submissive, either as ideal wives or sexually exotic prostitutes.

In online digital pornography, Asian women are disproportionately presented as victims of rape, compared to white women or women of other racial backgrounds.

Harmful stereotypes of Asian women in American popular culture date back to at least the 19th century. Back then, American missionaries and military personnel in Asia viewed the women they met there as exotic and submissive.

The most recent high-profile example of this dynamic is the 2015 rape of a woman by white Stanford student Brock Turner. Not until 2019 did the woman, Chanel Miller, reveal her name and identity as an Asian American woman.

In March 2020, Asian American and Pacific Islander community organizations joined with San Francisco State University’s Asian American Studies Program to document incidents of anti-Asian racism occurring across the country during the COVID-19 pandemic.

The group has found that Asian women report hate incidents 2.3 times as often as Asian men.

Asian women are not the only targets of racial and sexual violence. Any non-white woman has a greater risk of these perils than white women do.

“The social isolation since then, over all this time, it just got to him,”

“This is a charming, funny kid, also sensitive and anxious,” she said. “He couldn’t find a job; he couldn’t really go out. And he started using marijuana again, and Xanax.”

The teenager’s frustration finally boiled over this month, when he deliberately cut himself.

The doctors sent him home, she said, “with no support, no therapy, nothing.”

Surveys and statistics show that for young people who are anxious by nature, or feeling emotionally fragile already, the pandemic and its isolation have pushed them to the brink

Rates of suicidal thinking and behavior are up by 25 percent or more from similar periods in 2019, according to a just-published analysis of surveys of young patients coming into the emergency room.

For these teenagers, there aren’t many places to turn.

Finally, when a crisis hits, many of these teenagers end up in the local emergency department — the one place desperate families so often go for help.

“This is a national crisis we’re facing,”

For the young people coming undone, however, pandemic life presents unusual challenges, pediatricians say

“What parents and children are consistently reporting is an increase in all symptoms — a child who was a little anxious before the pandemic became very anxious over this past year,”

“This giant boy, crying — it’s terrible to see.” The young man has had panic attacks, twice followed by a blackout. During one, he fell and injured his face.

These young people do not necessarily qualify for a psychiatric diagnosis, nor are they “traumatized” in the strict sense of having had a life-threatening experience (or the perception of one).

Rather, they are trying to manage an interruption in their

The result is grief, but grief without a name or a specific cause, an experience some psychologists call “ambiguous loss.”

“Everything that used to be familiar and give structure to their lives, and predictability, and normalcy, is gone,” said Sharon Young, a therapist in Hendersonville. “Kids need all these things even more than adults do, and it’s hard for them to feel emotionally safe when they’re no longer there.”

Nationwide Children’s Hospital in Columbus, Ohio, has an emergency department that is a decent size for a pediatric hospital, with capacity for 62 children or adolescents

“This was huge problem pre-pandemic,” said Dr. David Axelson, chief of psychiatry and behavioral health at the hospital. “We were seeing a rise in emergency department visits for mental health problems in kids, specifically for suicidal thinking and self-harm. Our emergency department was overwhelmed with it, having to board kids on the medical unit while waiting for psych beds.”

“We have to say no,” Dr. Axelson said.

Like many other parents, she is now looking after an unstable child and wondering where to go next. A drug rehab program may be needed, as well as regular therapy.

“Covid has put our system under a microscope in terms of the things that don’t work,”

“We had a shaky system of care in pediatric mental health prior to this pandemic, and now we have all these added stressors on it, all these kids coming in for pandemic-related issues. Hospitals everywhere are scrambling to adjust.”

But the VHS tape itself may be immortal.

Today, a robust marketplace exists, both virtually and in real life, for this ephemera.

“Hold steady. Price seems fair. It is a Classic.”

Driving the passionate collection of this form of media is the belief that VHS offers something that other types of media cannot.

“The general perception that people can essentially order whatever movie they want from home is flat-out wrong,”

“promised as a giant video store on the internet, where a customer was only one click away from the exact film they were looking for.”

“Anything that you can think of is on VHS tape, because, you’ve got to think, it was a revolutionary piece of the media,”

“It was a way for everyone to capture something and then put it out there.”

preservation

“just so much culture packed into VHS,”

a movie studio, an independent filmmaker, a parent shooting their kid’s first steps, etc.

finds the medium inspirational

“some weird, obscure movie on VHS I would have seen at my friend’s house, late at night, after his parents were asleep.

“The quality feels raw but warm and full of flavor,” he said of VHS.

views them as a byway connecting her with the past

from reels depicting family gatherings to movies that just never made the jump to DVD

“I think we were the last to grow up without the internet, cellphones or social media,” and clinging to the “old analog ways,” she said, feels “very natural.”

“I think that people are nostalgic for the aura of the VHS era,”

“So many cultural touch points are rooted there,” Mr. Harris said of the 1980s.

It was, he believes, “a time when, in some ways, Americans knew who we were.”

Not only could film connoisseurs peruse the aisles of video stores on Friday nights, but they could also compose home movies, from the artful to the inane

“In its heyday, it was mass-produced and widely adopted,”

She inherited some of them from her grandmother, a children’s librarian with a vast collection.

Historical Journal of Film, Radio and Television

the first technology that allowed mass, large-scale home media access to films.”

Mr. Arrow said that home videos captured on VHS, or taped television programs that contain old commercials and snippets from the news, are particularly insightful in diving into cultural history.

“There’ll be a news break, and you’ll see, like: Oh my god, O.J.’s still in the Bronco, and it’s on the news, and then it’ll cut back to ‘Mission Impossible’ or something.”

Marginalized communities, Mr. Harris said, who were not well represented in media in the 1980s, benefited from VHS technology, which allowed them to create an archival system that now brings to life people and communities that were otherwise absent from the screen.

The nature of VHS, Mr. Harris said, made self-documentation “readily available,

people who lacked representation could “begin to build a library, an archive, to affirm their existence and that of their community.”

VHS enthusiasts agree that these tapes occupy an irreplaceable place in culture.

“It’s like a time capsule,”

“The medium is like no other.”

This insight goes a long way to explaining the current fetishization of experience, especially if it is (redundantly) “lived.” Black people from all walks of life find themselves deferred to by non-blacks

black people certainly don’t all “feel” or “experience” the same things. Nor do they all "experience" the same event in an identical way. Finally, even when their experiences are similar, they don’t all think about or interpret their experiences in the same way.

we must begin to attend in a serious way to heterodox black voices

This need is especially urgent given the ideological homogeneity of the “antiracist” outlook and efforts of elite institutions, including media, corporations, and an overwhelmingly progressive academia. For the arbiters of what it means to be black that dominate these institutions, there is a fairly narrowly prescribed “authentic” black narrative, black perspective, and black position on every issue that matters.

When we hear the demand to “listen to black voices,” what is usually meant is “listen to the right black voices.”

Many non-black people have heard a certain construction of “the black voice” so often that they are perplexed by black people who don’t fit the familiar model.

Similarly, many activists are not in fact “pro-black”: they are pro a rather specific conception of “blackness” that is not necessarily endorsed by all black people.

This is where our new website, Free Black Thought (FBT), seeks to intervene in the national conversation. FBT honors black individuals for their distinctive, diverse, and heterodox perspectives, and offers up for all to hear a polyphony, perhaps even a cacophony, of different and differing black voices.

The practical effects of the new antiracism are everywhere to be seen, but in few places more clearly than in our children’s schools

one might reasonably question what could be wrong with teaching children “antiracist” precepts. But the details here are full of devils.

To take an example that could affect millions of students, the state of California has adopted a statewide Ethnic Studies Model Curriculum (ESMC) that reflects “antiracist” ideas. The ESMC’s content inadvertently confirms that contemporary antiracism is often not so much an extension of the civil rights movement but in certain respects a tacit abandonment of its ideals.

It has thus been condemned as a “perversion of history” by Dr. Clarence Jones, MLK’s legal counsel, advisor, speechwriter, and Scholar in Residence at the Martin Luther King, Jr. Institute at Stanford University:

Essentialist thinking about race has also gained ground in some schools. For example, in one elite school, students “are pressured to conform their opinions to those broadly associated with their race and gender and to minimize or dismiss individual experiences that don’t match those assumptions.” These students report feeling that “they must never challenge any of the premises of [the school’s] ‘antiracist’ teachings.”

In contrast, the non-white students were taught that they were “folx (sic) who do not benefit from their social identities,” and “have little to no privilege and power.”

The children with “white” in their identity map were taught that they were part of the “dominant culture” which has been “created and maintained…to hold power and stay in power.” They were also taught that they had “privilege” and that “those with privilege have power over others.

Or consider the third-grade students at R.I. Meyerholz Elementary School in Cupertino, California

Or take New York City’s public school system, one of the largest educators of non-white children in America. In an effort to root out “implicit bias,” former Schools Chancellor Richard Carranza had his administrators trained in the dangers of “white supremacy culture.”

A slide from a training presentation listed “perfectionism,” “individualism,” “objectivity” and “worship of the written word” as white supremacist cultural traits to be “dismantled,”

Finally, some schools are adopting antiracist ideas of the sort espoused by Ibram X. Kendi, according to whom, if metrics such as tests and grades reveal disparities in achievement, the project of measuring achievement must itself be racist.

Parents are justifiably worried about such innovations. What black parent wants her child to hear that grading or math are “racist” as a substitute for objective assessment and real learning? What black parent wants her child told she shouldn’t worry about working hard, thinking objectively, or taking a deep interest in reading and writing because these things are not authentically black?

Clearly, our children’s prospects for success depend on the public being able to have an honest and free-ranging discussion about this new antiracism and its utilization in schools. Even if some black people have adopted its tenets, many more, perhaps most, hold complex perspectives that draw from a constellation of rather different ideologies.

So let’s listen to what some heterodox black people have to say about the new antiracism in our schools.

Coleman Hughes, a fellow at the Manhattan Institute, points to a self-defeating feature of Kendi-inspired grading and testing reforms: If we reject high academic standards for black children, they are unlikely to rise to “those same rejected standards” and racial disparity is unlikely to decrease

Chloé Valdary, the founder of Theory of Enchantment, worries that antiracism may “reinforce a shallow dogma of racial essentialism by describing black and white people in generalizing ways” and discourage “fellowship among peers of different races.”

We hope it’s obvious that the point we’re trying to make is not that everyone should accept uncritically everything these heterodox black thinkers say. Our point in composing this essay is that we all desperately need to hear what these thinkers say so we can have a genuine conversation

We promote no particular politics or agenda beyond a desire to offer a wide range of alternatives to the predictable fare emanating from elite mainstream outlets. At FBT, Marxists rub shoulders with laissez-faire libertarians. We have no desire to adjudicate who is “authentically black” or whom to prefer.

a recent psychological study suggests a simpler explanation. Its core idea is what you might call “oppression creep” or, more neutrally, “prevalence-induced concept change.” The more progress we observe, the greater the remaining injustices appear.

We seem incapable of keeping a concept stable over time when the prevalence of that concept declines.

lthough modern societies have made extraordinary progress in solving a wide range of social problems, from poverty and illiteracy to violence and infant mortality, the majority of people believe that the world is getting worse. The fact that concepts grow larger when their instances grow smaller may be one source of that pessimism.

We see relatively, not absolutely. We change our standards all the time, depending on context.

When blue dots became rare, purple dots began to look blue; when threatening faces became rare, neutral faces began to appear threatening … This happened even when the change in the prevalence of instances was abrupt, even when participants were explicitly told that the prevalence of instances would change, and even when participants were instructed and paid to ignore these changes.

We seem to be wired to assume a given threat remains just as menacing even when its actual prevalence has declined:

“In other words, when the prevalence of blue dots decreased, participants’ concept of blue expanded to include dots that it had previously excluded.”

This study may help explain why, in the midst of tremendous gains for gays, women, and racial minorities, we still insist more than ever that we live in a patriarchal, misogynist, white supremacist, homophobic era.

whatever the cause, the result is that we steadfastly refuse to accept the fact of progress, in a cycle of eternal frustration at what injustices will always remain

We never seem to be able to say: “Okay, we’re done now, we’ve got this, politics has done all it reasonably could, now let’s move on with our lives.” We can only ever say: “It’s worse than ever!” And fe

picking someone who has bent the truth so often about so many things — her ancestry, her commitment to serving a full term as senator, the schools her kids went to, the job her father had (according to her brother), or the time she was “fired” for being pregnant — is an unnecessary burden.

The Democrat I think is most likely to lose to Trump is Elizabeth Warren.I admire her ambition and grit and aggression, but nominating a woke, preachy Harvard professor plays directly into Trump’s hands

Pete Buttigieg’s appeal has waned for me.

over time, the combination of his perfect résumé, his actorly ability to change register as he unpacks a sentence, and his smoothness and self-love have begun to worry me. My fear is that his appeal will fade

Klobuchar, to my mind, is the better midwestern option. She is an engaging and successful politician. But there’s a reason she seemingly can’t get more traction. She just doesn’t command a room

I so want Biden to be ten years younger. I can’t help but be very fond of the man, and he does have a mix of qualities that appeal to both African-Americans and white working-class midwesterners. What I worry about is his constant stumbling in his speech, his muddling of words, those many moments when his eyes close, and his face twitches, as he tries to finish a sentence

Sanders has been on the far left all his life, and the oppo research the GOP throws at him could be brutal. He’s a man, after all, who sided with a Marxist-Leninist party that supported Ayatollah Khomeini during the hostage crisis in 1979. He loved the monstrous dictator Fidel Castro and took his 1988 honeymoon in the Soviet Union, no less, where he openly and publicly criticized his own country and praised many aspects of the Soviet system

On two key issues, immigration and identity politics, Bernie has sensibilities and instincts that could neutralize these two strong points for Trump. Sanders has always loathed the idea of open borders and the effect they have on domestic wages, and he doesn’t fit well with the entire woke industry. He still believes in class struggle, not the culture war

Biden has an advantage because of Obama, his appeal to the midwestern voters (if he wins back Pennsylvania, that would work wonders), and his rapport with African-Americans. But he also seems pretty out of it.

Cognitive dissonance emerges when you feel that some of your beliefs and behaviors or certain information from the outside world don’t really fit together.

Being distracted by the second task didn’t hurt actual performance on the first task, but it did impair the subjects’ ability to be introspective (again, by accurately self-reporting how they did). The finding supports previous widespread evidence that multitasking leads to lower cognitive performance.

But our solipsism is frequently given outward expression rather than inward exploration, with more emphasis than ever before on images.

“That hints at the way that, as our technologies increase the intensity of stimulation and the flow of new things, we adapt to that pace,” Mr. Carr said. “We become less patient. When moments without stimulation arise, we start to feel panicked and don’t know what to do with them, because we’ve trained ourselves to expect this stimulation — new notifications and alerts and so on.”

Mr. Carr also noted counterarguments: Formulating relatively simple thoughts on the internet can yield more complex ones through real-time exchanges with others, and people whose reflex is to post a notion hastily rather than let it sit may not have been the most deliberative thinkers in a pre-smartphone time, either.

“We’ve adopted the Google ideal of the mind, which is that you have a question that you can answer quickly: close-ended, well-defined questions. Lost in that conception is that there’s also this open-ended way of thinking where you’re not always trying to answer a question. You’re trying to go where that thought leads you. As a society, we’re saying that that way of thinking isn’t as important anymore. It’s viewed as inefficient.”

Many big companies are what one would describe as multi-business firms, an organizational form where you have a corporate parent and subsidiary units," said co-author Abhinav Gupta

"Narcissism affects people's desire to be distinctive," Gupta said. "It's correlated by people wanting glory for themselves. We hypothesized that business-unit heads that have those traits would be the ones to say, 'We don't want to work with you. We have sufficient skills and knowledge and abilities that we will work independently.' That was very strongly borne out based on our research design."

The study found that unit-head narcissism can prevent knowledge sharing. That tendency diminished in fast-changing or complex environments because narcissists had an excuse to pursue external ideas. But when businesses have high inter-unit competition, narcissists are more tempted to distinguish themselves from other units.

"This research kind of goes against the grain of that. If you create the perception of competition inside an organization, then that will have some downstream effects. You will be essentially foregoing some essential knowledge-sharing activities."

Confirmation bias shows up most blatantly in our current political divide, where each side seems unable to allow that the other side is right about anything.

The whole idea of cognitive biases and faulty heuristics—the shortcuts and rules of thumb by which we make judgments and predictions—was more or less invented in the 1970s by Amos Tversky and Daniel Kahneman

versky died in 1996. Kahneman won the 2002 Nobel Prize in Economics for the work the two men did together, which he summarized in his 2011 best seller, Thinking, Fast and Slow. Another best seller, last year’s The Undoing Project, by Michael Lewis, tells the story of the sometimes contentious collaboration between Tversky and Kahneman

Another key figure in the field is the University of Chicago economist Richard Thaler. One of the biases he’s most linked with is the endowment effect, which leads us to place an irrationally high value on our possessions.

In an experiment conducted by Thaler, Kahneman, and Jack L. Knetsch, half the participants were given a mug and then asked how much they would sell it for. The average answer was $5.78. The rest of the group said they would spend, on average, $2.21 for the same mug. This flew in the face of classic economic theory, which says that at a given time and among a certain population, an item has a market value that does not depend on whether one owns it or not. Thaler won the 2017 Nobel Prize in Economics.

“The question that is most often asked about cognitive illusions is whether they can be overcome. The message … is not encouraging.”

that’s not so easy in the real world, when we’re dealing with people and situations rather than lines. “Unfortunately, this sensible procedure is least likely to be applied when it is needed most,” Kahneman writes. “We would all like to have a warning bell that rings loudly whenever we are about to make a serious error, but no such bell is available.”

At least with the optical illusion, our slow-thinking, analytic mind—what Kahneman calls System 2—will recognize a Müller-Lyer situation and convince itself not to trust the fast-twitch System 1’s perception

Kahneman and others draw an analogy based on an understanding of the Müller-Lyer illusion, two parallel lines with arrows at each end. One line’s arrows point in; the other line’s arrows point out. Because of the direction of the arrows, the latter line appears shorter than the former, but in fact the two lines are the same length.

Because biases appear to be so hardwired and inalterable, most of the attention paid to countering them hasn’t dealt with the problematic thoughts, judgments, or predictions themselves

Is it really impossible, however, to shed or significantly mitigate one’s biases? Some studies have tentatively answered that question in the affirmative.

what if the person undergoing the de-biasing strategies was highly motivated and self-selected? In other words, what if it was me?

Over an apple pastry and tea with milk, he told me, “Temperament has a lot to do with my position. You won’t find anyone more pessimistic than I am.”

I met with Kahneman

“I see the picture as unequal lines,” he said. “The goal is not to trust what I think I see. To understand that I shouldn’t believe my lying eyes.” That’s doable with the optical illusion, he said, but extremely difficult with real-world cognitive biases.

In this context, his pessimism relates, first, to the impossibility of effecting any changes to System 1—the quick-thinking part of our brain and the one that makes mistaken judgments tantamount to the Müller-Lyer line illusion

he most effective check against them, as Kahneman says, is from the outside: Others can perceive our errors more readily than we can.

“slow-thinking organizations,” as he puts it, can institute policies that include the monitoring of individual decisions and predictions. They can also require procedures such as checklists and “premortems,”

A premortem attempts to counter optimism bias by requiring team members to imagine that a project has gone very, very badly and write a sentence or two describing how that happened. Conducting this exercise, it turns out, helps people think ahead.

“My position is that none of these things have any effect on System 1,” Kahneman said. “You can’t improve intuition.

Perhaps, with very long-term training, lots of talk, and exposure to behavioral economics, what you can do is cue reasoning, so you can engage System 2 to follow rules. Unfortunately, the world doesn’t provide cues. And for most people, in the heat of argument the rules go out the window.

Kahneman describes an even earlier Nisbett article that showed subjects’ disinclination to believe statistical and other general evidence, basing their judgments instead on individual examples and vivid anecdotes. (This bias is known as base-rate neglect.)

over the years, Nisbett had come to emphasize in his research and thinking the possibility of training people to overcome or avoid a number of pitfalls, including base-rate neglect, fundamental attribution error, and the sunk-cost fallacy.

Nisbett’s second-favorite example is that economists, who have absorbed the lessons of the sunk-cost fallacy, routinely walk out of bad movies and leave bad restaurant meals uneaten.

When Nisbett asks the same question of students who have completed the statistics course, about 70 percent give the right answer. He believes this result shows, pace Kahneman, that the law of large numbers can be absorbed into System 2—and maybe into System 1 as well, even when there are minimal cues.

about half give the right answer: the law of large numbers, which holds that outlier results are much more frequent when the sample size (at bats, in this case) is small. Over the course of the season, as the number of at bats increases, regression to the mean is inevitabl

When Nisbett has to give an example of his approach, he usually brings up the baseball-phenom survey. This involved telephoning University of Michigan students on the pretense of conducting a poll about sports, and asking them why there are always several Major League batters with .450 batting averages early in a season, yet no player has ever finished a season with an average that high.

we’ve tested Michigan students over four years, and they show a huge increase in ability to solve problems. Graduate students in psychology also show a huge gain.”

, “I know from my own research on teaching people how to reason statistically that just a few examples in two or three domains are sufficient to improve people’s reasoning for an indefinitely large number of events.”

isbett suggested another factor: “You and Amos specialized in hard problems for which you were drawn to the wrong answer. I began to study easy problems, which you guys would never get wrong but untutored people routinely do … Then you can look at the effects of instruction on such easy problems, which turn out to be huge.”

Nisbett suggested that I take “Mindware: Critical Thinking for the Information Age,” an online Coursera course in which he goes over what he considers the most effective de-biasing skills and concepts. Then, to see how much I had learned, I would take a survey he gives to Michigan undergraduates. So I did.

he course consists of eight lessons by Nisbett—who comes across on-screen as the authoritative but approachable psych professor we all would like to have had—interspersed with some graphics and quizzes. I recommend it. He explains the availability heuristic this way: “People are surprised that suicides outnumber homicides, and drownings outnumber deaths by fire. People always think crime is increasing” even if it’s not.

When I finished the course, Nisbett sent me the survey he and colleagues administer to Michigan undergrads

It contains a few dozen problems meant to measure the subjects’ resistance to cognitive biases

I got it right. Indeed, when I emailed my completed test, Nisbett replied, “My guess is that very few if any UM seniors did as well as you. I’m sure at least some psych students, at least after 2 years in school, did as well. But note that you came fairly close to a perfect score.”

Nevertheless, I did not feel that reading Mindware and taking the Coursera course had necessarily rid me of my biases

For his part, Nisbett insisted that the results were meaningful. “If you’re doing better in a testing context,” he told me, “you’ll jolly well be doing better in the real world.”

The New York–based NeuroLeadership Institute offers organizations and individuals a variety of training sessions, webinars, and conferences that promise, among other things, to use brain science to teach participants to counter bias. This year’s two-day summit will be held in New York next month; for $2,845, you could learn, for example, “why are our brains so bad at thinking about the future, and how do we do it better?”

Philip E. Tetlock, a professor at the University of Pennsylvania’s Wharton School, and his wife and research partner, Barbara Mellers, have for years been studying what they call “superforecasters”: people who manage to sidestep cognitive biases and predict future events with far more accuracy than the pundits

One of the most important ingredients is what Tetlock calls “the outside view.” The inside view is a product of fundamental attribution error, base-rate neglect, and other biases that are constantly cajoling us into resting our judgments and predictions on good or vivid stories instead of on data and statistics

In 2006, seeking to prevent another mistake of that magnitude, the U.S. government created the Intelligence Advanced Research Projects Activity (iarpa), an agency designed to use cutting-edge research and technology to improve intelligence-gathering and analysis. In 2011, iarpa initiated a program, Sirius, to fund the development of “serious” video games that could combat or mitigate what were deemed to be the six most damaging biases: confirmation bias, fundamental attribution error, the bias blind spot (the feeling that one is less biased than the average person), the anchoring effect, the representativeness heuristic, and projection bias (the assumption that everybody else’s thinking is the same as one’s own).

most promising are a handful of video games. Their genesis was in the Iraq War

Together with collaborators who included staff from Creative Technologies, a company specializing in games and other simulations, and Leidos, a defense, intelligence, and health research company that does a lot of government work, Morewedge devised Missing. Some subjects played the game, which takes about three hours to complete, while others watched a video about cognitive bias. All were tested on bias-mitigation skills before the training, immediately afterward, and then finally after eight to 12 weeks had passed.

“The literature on training suggests books and classes are fine entertainment but largely ineffectual. But the game has very large effects. It surprised everyone.”

he said he saw the results as supporting the research and insights of Richard Nisbett. “Nisbett’s work was largely written off by the field, the assumption being that training can’t reduce bias,

even the positive results reminded me of something Daniel Kahneman had told me. “Pencil-and-paper doesn’t convince me,” he said. “A test can be given even a couple of years later. But the test cues the test-taker. It reminds him what it’s all about.”

Morewedge told me that some tentative real-world scenarios along the lines of Missing have shown “promising results,” but that it’s too soon to talk about them.

In the future, I will monitor my thoughts and reactions as best I can

Gelfand and Harrington predicted that “‘tight’ states would exhibit a higher incidence of natural disasters, greater environmental vulnerability, fewer natural resources, greater incidence of disease and higher mortality rates, higher population density, and greater degrees of external threat.”

The South dominated the tight states: Mississippi, Alabama Arkansas, Oklahoma, Tennessee, Texas, Louisiana, Kentucky, South Carolina and North Carolina

states in New England and on the West Coast were the loosest: California, Oregon, Washington, Maine, Massachusetts, Connecticut, New Hampshire and Vermont.

Cultural differences, Gelfand continued, “have a certain logic — a rationale that makes good sense,” noting that “cultures that have threats need rules to coordinate to survive (think about how incredibly coordinated Japan is in response to natural disasters).

“Rule Makers, Rule Breakers: How Tight and Loose Cultures Wire the World” in 2018, in which she described the results of a 2016 pre-election survey she and two colleagues had commissioned

The results were telling: People who felt the country was facing greater threats desired greater tightness. This desire, in turn, correctly predicted their support for Trump. In fact, desired tightness predicted support for Trump far better than other measures. For example, a desire for tightness predicted a vote for Trump with 44 times more accuracy than other popular measures of authoritarianism.

The 2016 election, Gelfand continued, “turned largely on primal cultural reflexes — ones that had been conditioned not only by cultural forces, but by a candidate who was able to exploit them.”

Gelfand said:Some groups have much stronger norms than others; they’re tight. Others have much weaker norms; they’re loose. Of course, all cultures have areas in which they are tight and loose — but cultures vary in the degree to which they emphasize norms and compliance with them.

In both 2016 and 2020, Donald Trump carried all 10 of the top “tight” states; Hillary Clinton and Joe Biden carried all 10 of the top “loose” states.

The tight-loose concept, Gelfand argued,is an important framework to understand the rise of President Donald Trump and other leaders in Poland, Hungary, Italy, and France,

cultures that don’t have a lot of threat can afford to be more permissive and loose.”

The gist is this: when people perceive threat — whether real or imagined, they want strong rules and autocratic leaders to help them survive

My research has found that within minutes of exposing study participants to false information about terrorist incidents, overpopulation, pathogen outbreaks and natural disasters, their minds tightened. They wanted stronger rules and punishments.

Gelfand writes that tightness encourages conscientiousness, social order and self-control on the plus side, along with close-mindedness, conventional thinking and cultural inertia on the minus side.

Looseness, Gelfand posits, fosters tolerance, creativity and adaptability, along with such liabilities as social disorder, a lack of coordination and impulsive behavior.

If liberalism and conservatism have historically played a complementary role, each checking the other to constrain extremism, why are the left and right so destructively hostile to each other now, and why is the contemporary political system so polarized?

Along the same lines, if liberals and conservatives hold differing moral visions, not just about what makes a good government but about what makes a good life, what turned the relationship between left and right from competitive to mutually destructive?

As a set, Niemi wrote, conservative binding values encompassthe values oriented around group preservation, are associated with judgments, decisions, and interpersonal orientations that sacrifice the welfare of individuals

She cited research thatfound 47 percent of the most extreme conservatives strongly endorsed the view that “The world is becoming a more and more dangerous place,” compared to 19 percent of the most extreme liberals

Conservatives and liberals, Niemi continued,see different things as threats — the nature of the threat and how it happens to stir one’s moral values (and their associated emotions) is a better clue to why liberals and conservatives react differently.

Unlike liberals, conservatives strongly endorse the binding moral values aimed at protecting groups and relationships. They judge transgressions involving personal and national betrayal, disobedience to authority, and disgusting or impure acts such as sexually or spiritually unchaste behavior as morally relevant and wrong.

Underlying these differences are competing sets of liberal and conservative moral priorities, with liberals placing more stress than conservatives on caring, kindness, fairness and rights — known among scholars as “individualizing values

conservatives focus more on loyalty, hierarchy, deference to authority, sanctity and a higher standard of disgust, known as “binding values.”

Niemi contended that sensitivity to various types of threat is a key factor in driving differences between the far left and far right.

For example, binding values are associated with Machiavellianism (e.g., status-seeking and lying, getting ahead by any means, 2013); victim derogation, blame, and beliefs that victims were causal contributors for a variety of harmful acts (2016, 2020); and a tendency to excuse transgressions of ingroup members with attributions to the situation rather than the person (2023).

Niemi cited a paper she and Liane Young, a professor of psychology at Boston College, published in 2016, “When and Why We See Victims as Responsible: The Impact of Ideology on Attitudes Toward Victims,” which tested responses of men and women to descriptions of crimes including sexual assaults and robberies.

We measured moral values associated with unconditionally prohibiting harm (“individualizing values”) versus moral values associated with prohibiting behavior that destabilizes groups and relationships (“binding values”: loyalty, obedience to authority, and purity)

Increased endorsement of binding values predicted increased ratings of victims as contaminated, increased blame and responsibility attributed to victims, increased perceptions of victims’ (versus perpetrators’) behaviors as contributing to the outcome, and decreased focus on perpetrators.

A central explanation typically offered for the current situation in American politics is that partisanship and political ideology have developed into strong social identities where the mass public is increasingly sorted — along social, partisan, and ideological lines.

What happened to people ecologically affected social-political developments, including the content of the rules people made and how they enforced them

Just as ecological factors differing from region to region over the globe produced different cultural values, ecological factors differed throughout the U.S. historically and today, producing our regional and state-level dimensions of culture and political patterns.

Joshua Hartshorne, who is also a professor of psychology at Boston College, took issue with the binding versus individualizing values theory as an explanation for the tendency of conservatives to blame victims:

I would guess that the reason conservatives are more likely to blame the victim has less to do with binding values and more to do with the just-world bias (the belief that good things happen to good people and bad things happen to bad people, therefore if a bad thing happened to you, you must be a bad person).

Belief in a just world, Hartshorne argued, is crucial for those seeking to protect the status quo:It seems psychologically necessary for anyone who wants to advocate for keeping things the way they are that the haves should keep on having, and the have-nots have got as much as they deserve. I don’t see how you could advocate for such a position while simultaneously viewing yourself as moral (and almost everyone believes that they themselves are moral) without also believing in the just world

Conversely, if you generally believe the world is not just, and you view yourself as a moral person, then you are likely to feel like you have an obligation to change things.

I asked Lene Aaroe, a political scientist at Aarhus University in Denmark, why the contemporary American political system is as polarized as it is now, given that the liberal-conservative schism is longstanding. What has happened to produce such intense hostility between left and right?

There is variation across countries in hostility between left and right. The United States is a particularly polarized case which calls for a contextual explanatio

I then asked Aaroe why surveys find that conservatives are happier than liberals. “Some research,” she replied, “suggests that experiences of inequality constitute a larger psychological burden to liberals because it is more difficult for liberals to rationalize inequality as a phenomenon with positive consequences.”