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This failing in the West’s economies is also a failing of economics

many people have long felt the desire to do something with their lives besides consuming goods and having leisure. They desire to participate in a community in which they can interact and develop.

Our prevailing political economy is blind to the very concept of inclusion; it does not map out any remedy for the deficiency

injustice of another sort. Workers in decent jobs view the economy as unjust if they or their children have virtually no chance of climbing to a higher rung in the socioeconomic ladder

“Money is like blood. You need it to live but it isn’t the point of life.”4

justice is not everything that people need from their economy. They need an economy that is good as well as just. And for some decades, the Western economies have fallen short of any conception of a “good economy”—an economy offering a “good life,” or a life of “richness,” as some humanists call it

The good life as it is popularly conceived typically involves acquiring mastery in one’s work, thus gaining for oneself better terms—or means to rewards, whether material, like wealth, or nonmaterial—an experience we may call “prospering.”

As humanists and philosophers have conceived it, the good life involves using one’s imagination, exercising one’s creativity, taking fascinating journeys into the unknown, and acting on the world—an experience I call “flourishing.”

prospering and flourishing became prevalent in the nineteenth century when, in Europe and America, economies emerged with the dynamism to generate their own innovation.

What is the mechanism of the slowdown in productivity

prospering

In nineteenth-century Britain and America, and later Germany and France, a culture of exploration, experimentation, and ultimately innovation grew out of the individualism of the Renaissance, the vitalism of the Baroque era, and the expressionism of the Romantic period.

What made innovating so powerful in these economies was that it was not limited to elites. It permeated society from the less advantaged parts of the population on up.

High-enough wages, low-enough unemployment, and wide-enough access to engaging work are necessary for a “good-enough” economy—though far from sufficient. The material possibilities of the economy must be adequate for the nonmaterial possibilities to be widespread—the satisfactions of prospering and of flourishing through adventurous, creative, and even imaginative work.

today’s standard economics. This economics, despite its sophistication in some respects, makes no room for economies in which people are imagining new products and using their creativity to build them. What is most fundamentally “wrong with economics” is that it takes such an economy to be the norm—to be “as good as it gets.”

ince around 1970, or earlier in some cases, most of the continental Western European economies have come to resemble more completely the mechanical model of standard economics. Most companies are highly efficient. Households, apart from the very low-paid or unemployed, have gone on saving

In most of Western Europe, economic dynamism is now at lows not seen, I would judge, since the advent of dynamism in the nineteenth century. Imagining and creating new products has almost disappeared from the continent

The bleak levels of both unemployment and job satisfaction in Europe are testimony to its dreary economies.

a recent survey of household attitudes found that, in “happiness,” the median scores in Spain (54), France (51), Italy (48), and Greece (37) are all below those in the upper half of the nations labeled “emerging”—Mexico (79), Venezuela (74), Brazil (73), Argentina (66), Vietnam (64), Colombia (64), China (59), Indonesia (58), Chile (58), and Malaysia (56)

The US economy is not much better. Two economists, Stanley Fischer and Assar Lindbeck, wrote of a “Great Productivity Slowdown,” which they saw as beginning in the late 1960s.11 The slowdown in the growth of capital and labor combined—what is called “total factor productivity”—is star

though the injustices in the West’s economies are egregious, they ought not to be seen as a major cause of the productivity slowdowns and globalization. (For one thing, a slowdown of productivity started in the US in the mid-1960s and the sharp loss of manufacturing jobs to poorer countries occurred much later—from the late 1970s to the early 1990s.) Deeper causes must be at work.

The plausible explanation of the syndrome in America—the productivity slowdown and the decline of job satisfaction, among other things—is a critical loss of indigenous innovation in the established industries like traditional manufacturing and services that was not nearly offset by the innovation that flowered in a few new industries

hat then caused this narrowing of innovation? No single explanation is persuasive. Yet two classes of explanations have the ring of truth. One points to suppression of innovation by vested interests

some professions, such as those in education and medicine, have instituted regulation and licensing to curb experimentation and change, thus dampening innovation

established corporations—their owners and stakeholders—and entire industries, using their lobbyists, have obtained regulations and patents that make it harder for new firms to gain entry into the market and to compete with incumbents.

The second explanation points to a new repression of potential innovators by families and schools. As the corporatist values of control, solidarity, and protection are invoked to prohibit innovation, traditional values of conservatism and materialism are often invoked to inhibit a young person from undertaking an innovation.

ow might Western nations gain—or regain—widespread prospering and flourishing? Taking concrete actions will not help much without fresh thinking: people must first grasp that standard economics is not a guide to flourishing—it is a tool only for efficiency.

Widespread flourishing in a nation requires an economy energized by its own homegrown innovation from the grassroots on up. For such innovation a nation must possess the dynamism to imagine and create the new—economic freedoms are not sufficient. And dynamism needs to be nourished with strong human values.

a reform of education stands out. The problem here is not a perceived mismatch between skills taught and skills in demand

The problem is that young people are not taught to see the economy as a place where participants may imagine new things, where entrepreneurs may want to build them and investors may venture to back some of them. It is essential to educate young people to this image of the economy.

It will also be essential that high schools and colleges expose students to the human values expressed in the masterpieces of Western literature, so that young people will want to seek economies offering imaginative and creative careers. Education systems must put students in touch with the humanities in order to fuel the human desire to conceive the new and perchance to achieve innovations

This reorientation of general education will have to be supported by a similar reorientation of economic education.

By the end of World War II, however, American corporations were facing severe talent shortages. Their senior executives were growing old, and a dearth of hiring from the Depression through the war had resulted in a shortfall of able, well-trained managers. Finding people who had the potential to rise quickly through the ranks became an overriding preoccupation of American businesses. They began to devise a formal hiring-and-management system based in part on new studies of human behavior, and in part on military techniques developed during both world wars, when huge mobilization efforts and mass casualties created the need to get the right people into the right roles as efficiently as possible. By the 1950s, it was not unusual for companies to spend days with young applicants for professional jobs, conducting a battery of tests, all with an eye toward corner-office potential.

But companies abandoned their hard-edged practices for another important reason: many of their methods of evaluation turned out not to be very scientific.

this regime, so widespread in corporate America at mid-century, had almost disappeared by 1990. “I think an HR person from the late 1970s would be stunned to see how casually companies hire now,”

Many factors explain the change, he said, and then he ticked off a number of them: Increased job-switching has made it less important and less economical for companies to test so thoroughly. A heightened focus on short-term financial results has led to deep cuts in corporate functions that bear fruit only in the long term. The Civil Rights Act of 1964, which exposed companies to legal liability for discriminatory hiring practices, has made HR departments wary of any broadly applied and clearly scored test that might later be shown to be systematically biased.

about a quarter of the country’s corporations were using similar tests to evaluate managers and junior executives, usually to assess whether they were ready for bigger roles.

He has encouraged the company’s HR executives to think about applying the games to the recruitment and evaluation of all professional workers.

Knack makes app-based video games, among them Dungeon Scrawl, a quest game requiring the player to navigate a maze and solve puzzles, and Wasabi Waiter, which involves delivering the right sushi to the right customer at an increasingly crowded happy hour. These games aren’t just for play: they’ve been designed by a team of neuroscientists, psychologists, and data scientists to suss out human potential. Play one of them for just 20 minutes, says Guy Halfteck, Knack’s founder, and you’ll generate several megabytes of data, exponentially more than what’s collected by the SAT or a personality test. How long you hesitate before taking every action, the sequence of actions you take, how you solve problems—all of these factors and many more are logged as you play, and then are used to analyze your creativity, your persistence, your capacity to learn quickly from mistakes, your ability to prioritize, and even your social intelligence and personality. The end result, Halfteck says, is a high-resolution portrait of your psyche and intellect, and an assessment of your potential as a leader or an innovator.

When the results came back, Haringa recalled, his heart began to beat a little faster. Without ever seeing the ideas, without meeting or interviewing the people who’d proposed them, without knowing their title or background or academic pedigree, Knack’s algorithm had identified the people whose ideas had panned out. The top 10 percent of the idea generators as predicted by Knack were in fact those who’d gone furthest in the process.

What Knack is doing, Haringa told me, “is almost like a paradigm shift.” It offers a way for his GameChanger unit to avoid wasting time on the 80 people out of 100—nearly all of whom look smart, well-trained, and plausible on paper—whose ideas just aren’t likely to work out.

Aptitude, skills, personal history, psychological stability, discretion, loyalty—companies at the time felt they had a need (and the right) to look into them all. That ambit is expanding once again, and this is undeniably unsettling. Should the ideas of scientists be dismissed because of the way they play a game? Should job candidates be ranked by what their Web habits say about them? Should the “data signature” of natural leaders play a role in promotion? These are all live questions today, and they prompt heavy concerns: that we will cede one of the most subtle and human of skills, the evaluation of the gifts and promise of other people, to machines; that the models will get it wrong; that some people will never get a shot in the new workforce.

scoring distance from work could violate equal-employment-opportunity standards. Marital status? Motherhood? Church membership? “Stuff like that,” Meyerle said, “we just don’t touch”—at least not in the U.S., where the legal environment is strict. Meyerle told me that Evolv has looked into these sorts of factors in its work for clients abroad, and that some of them produce “startling results.”

consider the alternative. A mountain of scholarly literature has shown that the intuitive way we now judge professional potential is rife with snap judgments and hidden biases, rooted in our upbringing or in deep neurological connections that doubtless served us well on the savanna but would seem to have less bearing on the world of work.

We may like to think that society has become more enlightened since those days, and in many ways it has, but our biases are mostly unconscious, and they can run surprisingly deep. Consider race. For a 2004 study called “Are Emily and Greg More Employable Than Lakisha and Jamal?,” the economists Sendhil Mullainathan and Marianne Bertrand put white-sounding names (Emily Walsh, Greg Baker) or black-sounding names (Lakisha Washington, Jamal Jones) on similar fictitious résumés, which they then sent out to a variety of companies in Boston and Chicago. To get the same number of callbacks, they learned, they needed to either send out half again as many résumés with black names as those with white names, or add eight extra years of relevant work experience to the résumés with black names.

a sociologist at Northwestern, spent parts of the three years from 2006 to 2008 interviewing professionals from elite investment banks, consultancies, and law firms about how they recruited, interviewed, and evaluated candidates, and concluded that among the most important factors driving their hiring recommendations were—wait for it—shared leisure interests.

Lacking “reliable predictors of future performance,” Rivera writes, “assessors purposefully used their own experiences as models of merit.” Former college athletes “typically prized participation in varsity sports above all other types of involvement.” People who’d majored in engineering gave engineers a leg up, believing they were better prepared.

the prevailing system of hiring and management in this country involves a level of dysfunction that should be inconceivable in an economy as sophisticated as ours. Recent survey data collected by the Corporate Executive Board, for example, indicate that nearly a quarter of all new hires leave their company within a year of their start date, and that hiring managers wish they’d never extended an offer to one out of every five members on their team

In the late 1990s, as these assessments shifted from paper to digital formats and proliferated, data scientists started doing massive tests of what makes for a successful customer-support technician or salesperson. This has unquestionably improved the quality of the workers at many firms.

In 2010, however, Xerox switched to an online evaluation that incorporates personality testing, cognitive-skill assessment, and multiple-choice questions about how the applicant would handle specific scenarios that he or she might encounter on the job. An algorithm behind the evaluation analyzes the responses, along with factual information gleaned from the candidate’s application, and spits out a color-coded rating: red (poor candidate), yellow (middling), or green (hire away). Those candidates who score best, I learned, tend to exhibit a creative but not overly inquisitive personality, and participate in at least one but not more than four social networks, among many other factors. (Previous experience, one of the few criteria that Xerox had explicitly screened for in the past, turns out to have no bearing on either productivity or retention

When Xerox started using the score in its hiring decisions, the quality of its hires immediately improved. The rate of attrition fell by 20 percent in the initial pilot period, and over time, the number of promotions rose. Xerox still interviews all candidates in person before deciding to hire them, Morse told me, but, she added, “We’re getting to the point where some of our hiring managers don’t even want to interview anymore”

Gone are the days, Ostberg told me, when, say, a small survey of college students would be used to predict the statistical validity of an evaluation tool. “We’ve got a data set of 347,000 actual employees who have gone through these different types of assessments or tools,” he told me, “and now we have performance-outcome data, and we can split those and slice and dice by industry and location.”

Evolv’s tests allow companies to capture data about everybody who applies for work, and everybody who gets hired—a complete data set from which sample bias, long a major vexation for industrial-organization psychologists, simply disappears. The sheer number of observations that this approach makes possible allows Evolv to say with precision which attributes matter more to the success of retail-sales workers (decisiveness, spatial orientation, persuasiveness) or customer-service personnel at call centers (rapport-building)

There are some data that Evolv simply won’t use, out of a concern that the information might lead to systematic bias against whole classes of people

the idea that hiring was a science fell out of favor. But now it’s coming back, thanks to new technologies and methods of analysis that are cheaper, faster, and much-wider-ranging than what we had before

what most excites him are the possibilities that arise from monitoring the entire life cycle of a worker at any given company.

Now the two companies are working together to marry pre-hire assessments to an increasing array of post-hire data: about not only performance and duration of service but also who trained the employees; who has managed them; whether they were promoted to a supervisory role, and how quickly; how they performed in that role; and why they eventually left.

What begins with an online screening test for entry-level workers ends with the transformation of nearly every aspect of hiring, performance assessment, and management.

I turned to Sandy Pentland, the director of the Human Dynamics Laboratory at MIT. In recent years, Pentland has pioneered the use of specialized electronic “badges” that transmit data about employees’ interactions as they go about their days. The badges capture all sorts of information about formal and informal conversations: their length; the tone of voice and gestures of the people involved; how much those people talk, listen, and interrupt; the degree to which they demonstrate empathy and extroversion; and more. Each badge generates about 100 data points a minute.

he tried the badges out on about 2,500 people, in 21 different organizations, and learned a number of interesting lessons. About a third of team performance, he discovered, can usually be predicted merely by the number of face-to-face exchanges among team members. (Too many is as much of a problem as too few.) Using data gathered by the badges, he was able to predict which teams would win a business-plan contest, and which workers would (rightly) say they’d had a “productive” or “creative” day. Not only that, but he claimed that his researchers had discovered the “data signature” of natural leaders, whom he called “charismatic connectors” and all of whom, he reported, circulate actively, give their time democratically to others, engage in brief but energetic conversations, and listen at least as much as they talk.

His group is developing apps to allow team members to view their own metrics more or less in real time, so that they can see, relative to the benchmarks of highly successful employees, whether they’re getting out of their offices enough, or listening enough, or spending enough time with people outside their own team.

Torrents of data are routinely collected by American companies and now sit on corporate servers, or in the cloud, awaiting analysis. Bloomberg reportedly logs every keystroke of every employee, along with their comings and goings in the office. The Las Vegas casino Harrah’s tracks the smiles of the card dealers and waitstaff on the floor (its analytics team has quantified the impact of smiling on customer satisfaction). E‑mail, of course, presents an especially rich vein to be mined for insights about our productivity, our treatment of co-workers, our willingness to collaborate or lend a hand, our patterns of written language, and what those patterns reveal about our intelligence, social skills, and behavior.

people analytics will ultimately have a vastly larger impact on the economy than the algorithms that now trade on Wall Street or figure out which ads to show us. He reminded me that we’ve witnessed this kind of transformation before in the history of management science. Near the turn of the 20th century, both Frederick Taylor and Henry Ford famously paced the factory floor with stopwatches, to improve worker efficiency.

“The quantities of data that those earlier generations were working with,” he said, “were infinitesimal compared to what’s available now. There’s been a real sea change in the past five years, where the quantities have just grown so large—petabytes, exabytes, zetta—that you start to be able to do things you never could before.”

People analytics will unquestionably provide many workers with more options and more power. Gild, for example, helps companies find undervalued software programmers, working indirectly to raise those people’s pay. Other companies are doing similar work. One called Entelo, for instance, specializes in using algorithms to identify potentially unhappy programmers who might be receptive to a phone cal

He sees it not only as a boon to a business’s productivity and overall health but also as an important new tool that individual employees can use for self-improvement: a sort of radically expanded The 7 Habits of Highly Effective People, custom-written for each of us, or at least each type of job, in the workforce.

the most exotic development in people analytics today is the creation of algorithms to assess the potential of all workers, across all companies, all the time.

The way Gild arrives at these scores is not simple. The company’s algorithms begin by scouring the Web for any and all open-source code, and for the coders who wrote it. They evaluate the code for its simplicity, elegance, documentation, and several other factors, including the frequency with which it’s been adopted by other programmers. For code that was written for paid projects, they look at completion times and other measures of productivity. Then they look at questions and answers on social forums such as Stack Overflow, a popular destination for programmers seeking advice on challenging projects. They consider how popular a given coder’s advice is, and how widely that advice ranges.

The algorithms go further still. They assess the way coders use language on social networks from LinkedIn to Twitter; the company has determined that certain phrases and words used in association with one another can distinguish expert programmers from less skilled ones. Gild knows these phrases and words are associated with good coding because it can correlate them with its evaluation of open-source code, and with the language and online behavior of programmers in good positions at prestigious companies.

having made those correlations, Gild can then score programmers who haven’t written open-source code at all, by analyzing the host of clues embedded in their online histories. They’re not all obvious, or easy to explain. Vivienne Ming, Gild’s chief scientist, told me that one solid predictor of strong coding is an affinity for a particular Japanese manga site.

Gild’s CEO, Sheeroy Desai, told me he believes his company’s approach can be applied to any occupation characterized by large, active online communities, where people post and cite individual work, ask and answer professional questions, and get feedback on projects. Graphic design is one field that the company is now looking at, and many scientific, technical, and engineering roles might also fit the bill. Regardless of their occupation, most people leave “data exhaust” in their wake, a kind of digital aura that can reveal a lot about a potential hire.

professionally relevant personality traits can be judged effectively merely by scanning Facebook feeds and photos. LinkedIn, of course, captures an enormous amount of professional data and network information, across just about every profession. A controversial start-up called Klout has made its mission the measurement and public scoring of people’s online social influence.

Mullainathan expressed amazement at how little most creative and professional workers (himself included) know about what makes them effective or ineffective in the office. Most of us can’t even say with any certainty how long we’ve spent gathering information for a given project, or our pattern of information-gathering, never mind know which parts of the pattern should be reinforced, and which jettisoned. As Mullainathan put it, we don’t know our own “production function.”

Over time, better job-matching technologies are likely to begin serving people directly, helping them see more clearly which jobs might suit them and which companies could use their skills. In the future, Gild plans to let programmers see their own profiles and take skills challenges to try to improve their scores. It intends to show them its estimates of their market value, too, and to recommend coursework that might allow them to raise their scores even more. Not least, it plans to make accessible the scores of typical hires at specific companies, so that software engineers can better see the profile they’d need to land a particular job

Knack, for its part, is making some of its video games available to anyone with a smartphone, so people can get a better sense of their strengths, and of the fields in which their strengths would be most valued. (Palo Alto High School recently adopted the games to help students assess careers.) Ultimately, the company hopes to act as matchmaker between a large network of people who play its games (or have ever played its games) and a widening roster of corporate clients, each with its own specific profile for any given type of job.

When I began my reporting for this story, I was worried that people analytics, if it worked at all, would only widen the divergent arcs of our professional lives, further gilding the path of the meritocratic elite from cradle to grave, and shutting out some workers more definitively. But I now believe the opposite is likely to happen, and that we’re headed toward a labor market that’s fairer to people at every stage of their careers

For decades, as we’ve assessed people’s potential in the professional workforce, the most important piece of data—the one that launches careers or keeps them grounded—has been educational background: typically, whether and where people went to college, and how they did there. Over the past couple of generations, colleges and universities have become the gatekeepers to a prosperous life. A degree has become a signal of intelligence and conscientiousness, one that grows stronger the more selective the school and the higher a student’s GPA, that is easily understood by employers, and that, until the advent of people analytics, was probably unrivaled in its predictive powers.

the limitations of that signal—the way it degrades with age, its overall imprecision, its many inherent biases, its extraordinary cost—are obvious. “Academic environments are artificial environments,” Laszlo Bock, Google’s senior vice president of people operations, told The New York Times in June. “People who succeed there are sort of finely trained, they’re conditioned to succeed in that environment,” which is often quite different from the workplace.

because one’s college history is such a crucial signal in our labor market, perfectly able people who simply couldn’t sit still in a classroom at the age of 16, or who didn’t have their act together at 18, or who chose not to go to graduate school at 22, routinely get left behind for good. That such early factors so profoundly affect career arcs and hiring decisions made two or three decades later is, on its face, absurd.

I spoke with managers at a lot of companies who are using advanced analytics to reevaluate and reshape their hiring, and nearly all of them told me that their research is leading them toward pools of candidates who didn’t attend college—for tech jobs, for high-end sales positions, for some managerial roles. In some limited cases, this is because their analytics revealed no benefit whatsoever to hiring people with college degrees; in other cases, and more often, it’s because they revealed signals that function far better than college history,

Google, too, is hiring a growing number of nongraduates. Many of the people I talked with reported that when it comes to high-paying and fast-track jobs, they’re reducing their preference for Ivy Leaguers and graduates of other highly selective schools.

This process is just beginning. Online courses are proliferating, and so are online markets that involve crowd-sourcing. Both arenas offer new opportunities for workers to build skills and showcase competence. Neither produces the kind of instantly recognizable signals of potential that a degree from a selective college, or a first job at a prestigious firm, might. That’s a problem for traditional hiring managers, because sifting through lots of small signals is so difficult and time-consuming.

all of these new developments raise philosophical questions. As professional performance becomes easier to measure and see, will we become slaves to our own status and potential, ever-focused on the metrics that tell us how and whether we are measuring up? Will too much knowledge about our limitations hinder achievement and stifle our dreams? All I can offer in response to these questions, ironically, is my own gut sense, which leads me to feel cautiously optimistic.

Google’s understanding of the promise of analytics is probably better than anybody else’s, and the company has been changing its hiring and management practices as a result of its ongoing analyses. (Brainteasers are no longer used in interviews, because they do not correlate with job success; GPA is not considered for anyone more than two years out of school, for the same reason—the list goes on.) But for all of Google’s technological enthusiasm, these same practices are still deeply human. A real, live person looks at every résumé the company receives. Hiring decisions are made by committee and are based in no small part on opinions formed during structured interviews.

In another experiment, we found that longer response times were correlated with less diverse responses, ruling out the possibility that participants with low mental loads simply took more time to generate an interesting response.

it seems that with a high mental load, you need more time to generate even a conventional thought. These experiments suggest that the mind’s natural tendency is to explore and to favor novelty, but when occupied it looks for the most familiar and inevitably least interesting solution.

Much of our lives are spent somewhere between those extremes. There are functional benefits to both modes: If we were not exploratory, we would never have ventured out of the caves; if we did not exploit the certainty of the familiar, we would have taken too many risks and gone extinct. But there needs to be a healthy balance

In general, there is a tension in our brains between exploration and exploitation. When we are exploratory, we attend to things with a wide scope, curious and desiring to learn. Other times, we rely on, or “exploit,” what we already know, leaning on our expectations, trusting the comfort of a predictable environment

All these loads can consume mental capacity, leading to dull thought and anhedonia — a flattened ability to experience pleasure.

ancient meditative practice helps free the mind to have richer experiences of the present

your life leaves too much room for your mind to wander. As a result, only a small fraction of your mental capacity remains engaged in what is before it, and mind-wandering and ruminations become a tax on the quality of your life

Honing an ability to unburden the load on your mind, be it through meditation or some other practice, can bring with it a wonderfully magnified experience of the world — and, as our study suggests, of your own mind.

In these cultures, leaders are fearful of losing their livelihood and use the same fear to lead their employees. So, it is not surprising that so many of us have experienced unjust and inequitable working conditions in command and control cultures where leaders use fear and aggression as the means to drive employee productivity.

Eventually, the very thing she was trying to protect, her livelihood, was lost. Would she have saved her own job had she opted for a different organizational structure or a nicer management style?

Though, historically, the command and control management structure and its many iterations of management styles has been successfully used by many companies, there is evidence that organizational cultures based on value systems that reward initiative, creativity and innovation result in greater employee productivity, loyalty and engagement than cultures based on control. In other words, organization's that adopt command and control management structures, even when coupled with friendlier management styles, are likely to find that employee productivity, engagement and loyalty are not as high as those organizational cultures that encourage initiative, creativity and innovation.

when students become stuck on a problem, they are often too eager to throw up their hands in frustration, rather than buckle down and try to think creatively (in the words of Dan Meyer, students often lack patient problem solving skills).  Many students don't even think of mathematics as a subject that requires creativity

a website called Math Goes Pop!.

“We’re not talking about making sure that everybody has private music lessons,” Woetzel said. “We’re talking about a way of educating that involves artistic sensibilities—artistic habits of mind. The ability to re-assess and to imagine. To be in a science class and not think it’s about memorization entirely,” but to imagine its applications.

“People still don’t get it,” Woodard said. “They think it’s play time. They think it’s touchy feel-y. But it’s undeniable what music, painting, [and] movement do to the brain. It becomes more receptive to scientific ideas.”

“You cannot be an innovator in any category,” Woodard said, “unless that creative instinct is exercised.”

What do we mean by motivation? It's been defined as a predisposition to behave in a purposeful manner to achieve specific, unmet needs and the will to achieve, and the inner force that drives individuals to accomplish personal and organizational goals. And why do we need motivated employees? The answer is survival.

It turns out that people are motivated by interesting work, challenge, and increasing responsibility—intrinsic factors. People have a deep-seated need for growth and achievement.

Even understanding what constitutes human motivation  has been a centuries old puzzle, addressed as far back as Aristotle.

. Pink concludes that extrinsic motivators work only in a surprisingly narrow band of circumstances; rewards often destroy creativity and employee performance; and the secret to high performance isn’t reward and punishment but that unseen intrinsic drive—the drive to do something  because it is meaningful.

true motivation boils down to three elements: Autonomy, the desire to direct our own lives; mastery, the desire to continually improve at something that matters to us, and purpose, the desire to do things in service of something larger than ourselves.

The carrot-and-stick approach worked well for typical tasks of the early 20th century —routine, unchallenging and highly controlled. For these tasks, where the process is straightforward and lateral thinking is not required, rewards can provide a small motivational boost without any harmful side effects

obs in the 21st century have changed dramatically. They have become more complex, more interesting and more self-directed, and this is where the carrot-and-stick approach has become unstuck.

By trial and error, Ms. Oakley had learned how to learn: “The higher I went, it started to gradually make more and more sense.”

“The way you learn intensively for a language is very similar to learning well in math and science,”

“In learning math and science through K-12, it’s long been held that practice and repetition will kill your creativity,” she says. “One mistake we make in the school system is we emphasize understanding. But if you don’t build those neural circuits with practice, it’ll all slip away. You can understand out the wazoo, but it’ll just disappear if you’re not practicing with it.”

In places like China and India, “practice and repetition and rote and memorization are really important parts of education.” She sees value in both methods: “There are real benefits for Western approaches—that it really does help with creativity. And there are also real benefits to Asian approaches—that it builds a solid foundation in the most difficult disciplines, math and science.

The best education would actually be a combination of both approaches.”

She defines a “mindshift” as “a change in your outlook that occurs through intensive learning”—such as her own mastery of math and engineering.

The book is filled with advice for people who are considering a career change or who seek to develop “an attitude of lifelong learning,” even in retirement.

Her progression from desultory student to respected scholar led her to a sideline in the study of learning itself. She’s published two books on the subject, “A Mind for Numbers: How to Excel at Math and Science (Even if You Flunked Algebra)” (2014) and the new “Mindshift: Break Through Obstacles to Learning and Discover Your Hidden Potential.

they developed a massive open online course, “Learning How to Learn,” which by some measures is the world’s most popular MOOC

Behold the proverbial “aha!” moment — a key phenomenon that emerges in a range of situations, from offering a solution to a problem or a new interpretation of a situation to more simple feats such as understanding a joke or solving a crossword puzzle.

There are many different representations we use colloquially to describe good ideas — sparks, flashes, light-bulb moments; inspirations and innovations; muses and visions.

they usually materialize abruptly, without warning and seemingly out of thin air.

Laboratories and psychologists have attempted to study this phenomenon using behavioral methods for nearly a century, resulting merely in speculations as to where these ideas come from and how they form. Lately, though, with recent advancements and tools of cognitive neuroscience, researchers are able to explain the inner workings of the brain during moments of insigh

scientists have found that these sudden sparks are the result of a complex series of brain states.

Findings also suggest that we require more neural processes operating at different time scales in these moments than we use when solving a problem analytically or methodically.

Participants were presented with three words (e.g., crab, pine, sauce), and were instructed to think of a single word that forms a familiar two-word phrase with all three (e.g., apple can join with crab, pine, and sauce to form pineapple, crabapple, and applesauce). As soon as participants thought of a solution word, they pressed a button to indicate whether the answer had come to them suddenly (through insight), or if they used a methodical hypothesis testing approach — in other words, a trial-and-error approach.

Gamma activity indicates a constellation of neurons binding together for the first time in the brain to create a new neural network pathway.

This is the creation of a new idea. Immediately following that gamma spike, the new idea pops into our consciousness, which we identify as the aha! moment.

Even movies that explore adaptive responses to climate change make glum assumptions. In Christopher Nolan’s “Interstellar,” humanity’s future lies on a far distant planet; Earth is unsalvageable. James Cameron’s first “Avatar” movie imagines that resource crises will drive humanity to galaxy-wide pillage.

If activists, be they filmmakers or politicians, want to persuade the public to adopt new behaviors, or even to do more than simply despair, they need to give the ordinary person a vision for what to do.

The idea that pop culture can tell these stories creatively and dynamically is not merely speculative.

“The Ministry for the Future” novelist Kim Stanley Robinson has spent decades creatively imagining how humanity might respond to harsh conditions, whether that means Mars and the asteroid belt or a drowned New York City.

In Robinson’s telling, climate change will upend our lives, but we all have something to contribute to the response to this radical reordering.

e stories creatively and dyn

What happened in 2012, when the oldest Gen-Z babies were in their middle teens? That was the year Facebook acquired Instagram and young people flocked to the latter site. It was also “the beginning of the selfie era.”

Mr. Haidt’s research, confirmed by that of others, shows that depression rates started to rise “all of a sudden” around 2013, “especially for teen girls,” but “it’s only Gen Z, not the older generations.” If you’d stopped collecting data in 2011, he says, you’d see little change from previous years. “By 2015 it’s an epidemic.” (His data are available in an open-source document.)

Mr. Haidt imagines “literally launching our children into outer space” and letting their bodies grow there: “They would come out deformed and broken. Their limbs wouldn’t be right. You can’t physically grow up in outer space. Human bodies can’t do that.” Yet “we basically do that to them socially. We launched them into outer space around the year 2012,” he says, “and then we expect that they will grow up normally without having normal human experiences.”

He calls this phenomenon “compare and despair” and says: “It seems social because you’re communicating with people. But it’s performative. You don’t actually get social relationships. You get weak, fake social links.”

That meant the first social-media generation was one of “weakened kids” who “hadn’t practiced the skills of adulthood in a low-stakes environment” with other children. They were deprived of “the normal toughening, the normal strengthening, the normal anti-fragility.

Now, their childhood “is largely just through the phone. They no longer even hang out together.” Teenagers even drive less than earlier generations did.

Mr. Haidt especially worries about girls. By 2020 more than 25% of female teenagers had “a major depression.” The comparable number for boys was just under 9%.

The comparable numbers for millennials at the same age registered at half the Gen-Z rate: about 13% for girls and 5% for boys. “Kids are on their devices all the time,”

Most girls, by contrast, are drawn to “visual platforms,” Instagram and TikTok in particular. “Those are about display and performance. You post your perfect life, and then you flip through the photos of other girls who have a more perfect life, and you feel depressed.

Mr. Haidt says he has no antipathy toward the young, and he calls millennials “amazing.”

“Social media is incompatible with liberal democracy because it has moved conversation, and interaction, into the center of the Colosseum. We’re not there to talk to each other. We’re there to perform” before spectators who “want blood.”

To illustrate his point about Gen Z, Mr. Haidt challenges people to name young people today who are “really changing the world, who are doing big things that have an impact beyond their closed ecosystem.”

He can think of only two, neither of them American: Greta Thunberg, 19, the Swedish climate militant, and Malala Yousafzai, 25, the Pakistani advocate for female education

I’m predicting that they will be less effective, less impactful, than previous generations.” Why? “You should always keep your eye on whether people are in ‘discover mode’ or ‘defend mode.’ ” In the former mode, you seize opportunities to be creative. In the latter, “you’re not creative, you’re not future-thinking, you’re focused on threats in the present.”

University students who matriculated starting in 2014 or so have arrived on campus in defend mode: “Here they are in the safest, most welcoming, most inclusive, most antiracist places on the planet, but many of them were acting like they were entering some sort of dystopian, threatening, immoral world.”

56% of liberal women 18 to 29 responded affirmatively to the question: Has a doctor or other healthcare provider ever told you that you have a mental health condition? “Some of that,” Mr. Haidt says, “has to be just self-presentational,” meaning imagined.

This new ideology . . . valorizes victimhood. And if your sub-community motivates you to say you have an anxiety disorder, how is this going to affect you for the rest of your life?” He answers his own question: “You’re not going to take chances, you’re going to ask for accommodations, you’re going to play it safe, you’re not going to swing for the fences, you’re not going to start your own company.”

Whereas millennial women are doing well, “Gen-Z women, because they’re so anxious, are going to be less successful than Gen-Z men—and that’s saying a lot, because Gen-Z men are messed up, too.”

The problem, he says, is distinct to the U.S. and other English-speaking developed countries: “You don’t find it as much in Europe, and hardly at all in Asia.” Ideas that are “nurtured around American issues of race and gender spread instantly to the U.K. and Canada. But they don’t necessarily spread to France and Germany, China and Japan.”

something I hear from a lot of managers, that it’s very difficult to supervise their Gen-Z employees, that it’s very difficult to give them feedback.” That makes it hard for them to advance professionally by learning to do their jobs better.

“this could severely damage American capitalism.” When managers are “afraid to speak up honestly because they’ll be shamed on Twitter or Slack, then that organization becomes stupid.” Mr. Haidt says he’s “seen a lot of this, beginning in American universities in 2015. They all got stupid in the same way. They all implemented policies that backfire.”

Mr. Haidt, who describes himself as “a classical liberal like John Stuart Mill,” also laments the impact of social media on political discourse

Social media and selfies hit a generation that had led an overprotected childhood, in which the age at which children were allowed outside on their own by parents had risen from the norm of previous generations, 7 or 8, to between 10 and 12.

Is there a solution? “I’d raise the age of Internet adulthood to 16,” he says—“and enforce it.”

By contrast, “life went onto phone-based apps 10 years ago, and the protections we have for children are zero, absolutely zero.” The damage to Generation Z from social media “so vastly exceeds the damage from Covid that we’re going to have to act.”

Gen Z, he says, “is not in denial. They recognize that this app-based life is really bad for them.” He reports that they wish they had childhoods more like those of their parents, in which they could play outside and have adventur

Are we feeding our children a bunch of dangerous illusions about fairness and hard work and level playing fields? Are ideals a luxury only the rich can afford?

I’m reminded of the quote by John Adams: “I must study politics and war, that my sons may have the liberty to study mathematics and philosophy. My sons ought to study mathematics and philosophy, geography, natural history [and] naval architecture . . . in order to give their children a right to study painting, poetry, music, architecture, tapestry and porcelain.” For all intents and purposes, I guess I studied porcelain. The funny thing is that my parents came from a country (Peru) with a middle class so small that parents had to study business so that their children could study business. If I didn’t follow suit, it’s at least in part because I spent my childhood in the 1970s absorbing the nurturing message of a progressive pop culture that told me I could be anything I wanted, because this is America.

“When we see the contrast between the values we share and the realities we live in, that is the fundamental foundation for social change.”

“It depends on what you mean by artificial intelligence.”

Computers are flexible enough to model the strange evolved convolutions of our thought, and yet responsive only to precise instructions. So if the endeavor succeeds, it will be a double victory: we will finally come to know the exact mechanics of our selves—and we’ll have made intelligent machines.

Ever since he was about 14, when he found out that his youngest sister, Molly, couldn’t understand language, because she “had something deeply wrong with her brain” (her neurological condition probably dated from birth, and was never diagnosed), he had been quietly obsessed by the relation of mind to matter.

How could consciousness be physical? How could a few pounds of gray gelatin give rise to our very thoughts and selves?

Consciousness, Hofstadter wanted to say, emerged via just the same kind of “level-crossing feedback loop.”

In 1931, the Austrian-born logician Kurt Gödel had famously shown how a mathematical system could make statements not just about numbers but about the system itself.

But then AI changed, and Hofstadter didn’t change with it, and for that he all but disappeared.

By the early 1980s, the pressure was great enough that AI, which had begun as an endeavor to answer yes to Alan Turing’s famous question, “Can machines think?,” started to mature—or mutate, depending on your point of view—into a subfield of software engineering, driven by applications.

Take Deep Blue, the IBM supercomputer that bested the chess grandmaster Garry Kasparov. Deep Blue won by brute force.

Hofstadter wanted to ask: Why conquer a task if there’s no insight to be had from the victory? “Okay,” he says, “Deep Blue plays very good chess—so what? Does that tell you something about how we play chess? No. Does it tell you about how Kasparov envisions, understands a chessboard?”

AI started working when it ditched humans as a model, because it ditched them. That’s the thrust of the analogy: Airplanes don’t flap their wings; why should computers think?

It’s a compelling point. But it loses some bite when you consider what we want: a Google that knows, in the way a human would know, what you really mean when you search for something

Cognition is recognition,” he likes to say. He describes “seeing as” as the essential cognitive act: you see some lines a

How do you make a search engine that understands if you don’t know how you understand?

s “an A,” you see a hunk of wood as “a table,” you see a meeting as “an emperor-has-no-clothes situation” and a friend’s pouting as “sour grapes”

That’s what it means to understand. But how does understanding work?

analogy is “the fuel and fire of thinking,” the bread and butter of our daily mental lives.

there’s an analogy, a mental leap so stunningly complex that it’s a computational miracle: somehow your brain is able to strip any remark of the irrelevant surface details and extract its gist, its “skeletal essence,” and retrieve, from your own repertoire of ideas and experiences, the story or remark that best relates.

in Hofstadter’s telling, the story goes like this: when everybody else in AI started building products, he and his team, as his friend, the philosopher Daniel Dennett, wrote, “patiently, systematically, brilliantly,” way out of the light of day, chipped away at the real problem. “Very few people are interested in how human intelligence works,”

For more than 30 years, Hofstadter has worked as a professor at Indiana University at Bloomington

The quick unconscious chaos of a mind can be slowed down on the computer, or rewound, paused, even edited

project out of IBM called Candide. The idea behind Candide, a machine-translation system, was to start by admitting that the rules-based approach requires too deep an understanding of how language is produced; how semantics, syntax, and morphology work; and how words commingle in sentences and combine into paragraphs—to say nothing of understanding the ideas for which those words are merely conduits.

, Hofstadter directs the Fluid Analogies Research Group, affectionately known as FARG.

Parts of a program can be selectively isolated to see how it functions without them; parameters can be changed to see how performance improves or degrades. When the computer surprises you—whether by being especially creative or especially dim-witted—you can see exactly why.

When you read Fluid Concepts and Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought, which describes in detail this architecture and the logic and mechanics of the programs that use it, you wonder whether maybe Hofstadter got famous for the wrong book.

ut very few people, even admirers of GEB, know about the book or the programs it describes. And maybe that’s because FARG’s programs are almost ostentatiously impractical. Because they operate in tiny, seemingly childish “microdomains.” Because there is no task they perform better than a human.

“The entire effort of artificial intelligence is essentially a fight against computers’ rigidity.”

“Nobody is a very reliable guide concerning activities in their mind that are, by definition, subconscious,” he once wrote. “This is what makes vast collections of errors so important. In an isolated error, the mechanisms involved yield only slight traces of themselves; however, in a large collection, vast numbers of such slight traces exist, collectively adding up to strong evidence for (and against) particular mechanisms.

So IBM threw that approach out the window. What the developers did instead was brilliant, but so straightforward,

The technique is called “machine learning.” The goal is to make a device that takes an English sentence as input and spits out a French sentence

What you do is feed the machine English sentences whose French translations you already know. (Candide, for example, used 2.2 million pairs of sentences, mostly from the bilingual proceedings of Canadian parliamentary debates.)

By repeating this process with millions of pairs of sentences, you will gradually calibrate your machine, to the point where you’ll be able to enter a sentence whose translation you don’t know and get a reasonable resul

Google Translate team can be made up of people who don’t speak most of the languages their application translates. “It’s a bang-for-your-buck argument,” Estelle says. “You probably want to hire more engineers instead” of native speakers.

But the need to serve 1 billion customers has a way of forcing the company to trade understanding for expediency. You don’t have to push Google Translate very far to see the compromises its developers have made for coverage, and speed, and ease of engineering. Although Google Translate captures, in its way, the products of human intelligence, it isn’t intelligent itself.

“Did we sit down when we built Watson and try to model human cognition?” Dave Ferrucci, who led the Watson team at IBM, pauses for emphasis. “Absolutely not. We just tried to create a machine that could win at Jeopardy.”

For Ferrucci, the definition of intelligence is simple: it’s what a program can do. Deep Blue was intelligent because it could beat Garry Kasparov at chess. Watson was intelligent because it could beat Ken Jennings at Jeopardy.

“There’s a limited number of things you can do as an individual, and I think when you dedicate your life to something, you’ve got to ask yourself the question: To what end? And I think at some point I asked myself that question, and what it came out to was, I’m fascinated by how the human mind works, it would be fantastic to understand cognition, I love to read books on it, I love to get a grip on it”—he called Hofstadter’s work inspiring—“but where am I going to go with it? Really what I want to do is build computer systems that do something.

Peter Norvig, one of Google’s directors of research, echoes Ferrucci almost exactly. “I thought he was tackling a really hard problem,” he told me about Hofstadter’s work. “And I guess I wanted to do an easier problem.”

Of course, the folly of being above the fray is that you’re also not a part of it

As our machines get faster and ingest more data, we allow ourselves to be dumber. Instead of wrestling with our hardest problems in earnest, we can just plug in billions of examples of them.

Hofstadter hasn’t been to an artificial-intelligence conference in 30 years. “There’s no communication between me and these people,” he says of his AI peers. “None. Zero. I don’t want to talk to colleagues that I find very, very intransigent and hard to convince of anything

Everything from plate tectonics to evolution—all those ideas, someone had to fight for them, because people didn’t agree with those ideas.

Academia is not an environment where you just sit in your bath and have ideas and expect everyone to run around getting excited. It’s possible that in 50 years’ time we’ll say, ‘We really should have listened more to Doug Hofstadter.’ But it’s incumbent on every scientist to at least think about what is needed to get people to understand the ideas.”

This discovery is new, and further research is needed, but what is certain is that exciting developments await.

Technology has rewarded sprinters (people who can recognize and alertly post a message on Twitter about some interesting immediate event) and marathoners (people who can write large conceptual stories), but it has hurt middle-distance runners (people who write 800-word summaries of yesterday’s news conference).

Technology has rewarded graphic artists who can visualize data, but it has punished those who can’t turn written reporting into video presentations.

More generally, the age of brilliant machines seems to reward a few traits.

First, it rewards enthusiasm. The amount of information in front of us is practically infinite; so is that amount of data that can be collected with new tools. The people who seem to do best possess a voracious explanatory drive, an almost obsessive need to follow their curiosity.

Second, the era seems to reward people with extended time horizons and strategic discipline.

a human can provide an overall sense of direction and a conceptual frame. In a world of online distractions, the person who can maintain a long obedience toward a single goal, and who can filter out what is irrelevant to that goal, will obviously have enormous worth.

Third, the age seems to reward procedural architects. The giant Internet celebrities didn’t so much come up with ideas, they came up with systems in which other people could express ideas: Facebook, Twitter, Wikipedia, etc.

One of the oddities of collaboration is that tightly knit teams are not the most creative. Loosely bonded teams are, teams without a few domineering presences, teams that allow people to think alone before they share results with the group. So a manager who can organize a decentralized network around a clear question, without letting it dissipate or clump, will have enormous value.

Fifth, essentialists will probably be rewarded.

creativity can be described as the ability to grasp the essence of one thing, and then the essence of some very different thing, and smash them together to create some entirely new thing.

In the 1950s, the bureaucracy was the computer. People were organized into technocratic systems in order to perform routinized information processing.

now the computer is the computer. The role of the human is not to be dispassionate, depersonalized or neutral. It is precisely the emotive traits that are rewarded: the voracious lust for understanding, the enthusiasm for work, the ability to grasp the gist, the empathetic sensitivity to what will attract attention and linger in the mind.

Unable to compete when it comes to calculation, the best workers will come with heart in hand.