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A survey of 43 countries, published on October 30th, found that people in emerging markets are within a whisker of expressing the same level of satisfaction with their lives as people in rich countries.

While Scrooge found it easy to count his riches, happiness is harder to pin down. People are prone to what philosophers call “adaptive preferences”, meaning that they may fail to report their “true” happiness

If measuring happiness is so difficult, what else could economists look at? Amartya Sen, of Harvard University, argues that “capabilities” are the way to go. The definition of a capability is a bit fuzzy: at its simplest, a capability is something that people have reason to value. The list of potential capabilities is endless: the opportunity to live a long and healthy life, the freedom to take part in political life or to be well nourished.

Capabilities, says Mr Sen, are ends that economists should strive to maximise: income is just one of the many means by which we get there.

That begs the question of which capabilities a society should maximise. Some worry that the capability approach is deeply paternalistic

Ms Nussbaum goes so far as to recommend “ten central capabilities” that are essential for a good life. For economists, who tend to be lovers of freedom, this is controversial stuff.

But the capability approach may be less illiberal than it seems. Insisting that GDP is the true measure of economic progress is itself a value-judgment. What is more, according to Mr Sen and Ms Nussbaum, people must have the freedom to select which capabilities they ultimately pursue.

Measuring capabilities may be even more difficult than measuring GDP or happiness. There are, though, decent proxies. A country with a high life expectancy probably offers its citizens things like good health care and helps to shield them from pollution, which makes it easier for them to live a long, healthy life.

Some measures of economic success use such data. The Human Development Index (HDI), which Mr Sen helped to devise in 1990, considers not only income, but also life expectancy and schooling, as elements of development

the capability approach has spawned so many measures, each more complicated than the last, that GDP starts to look appealing again. What other single number can give a decent approximation of quality of life?

they conducted eight studies in which they asked participants about their recollections of, planning for or daydreaming about various happiness-making experiences. They also checked to see what sort of things their subjects were posting about on Facebook

ordinary experiences happen often and occur in the course of everyday life while extraordinary ones are much more rare.

Once you know yourself, the deliberate pursuit of more ordinary things can then deliver that same level of happiness. It doesn’t hurt, either, that you may appreciate the ordinary much more once you’re more aware of the decreasing number of years you have left to enjoy it.

Ms. Mogilner explored some of the factors besides frequency that separate ordinary and extraordinary experiences and seized on one in particular: the tendency for extraordinary experiences to be self-defining in some way.

One way to think about this is to consider the various adventures younger people pursue to find themselves. “That sort of exploration to see what fits and feels like you may be the process by which you can start to figure out what sort of ordinary life to build,”

the older people got, the more happiness ordinary experiences delivered. In fact, the happiness-making potential of everyday pursuits eventually grows equal to that of ones that are rarer.

there ought to be much comfort in the evidence that everyday things that cost little or nothing can deliver the same amount of joy. A garden. The elaborate meal that emerges from it and the spare time to invent the recipes. A return to a neglected musical instrument. All-you-can-consume subscriptions to Netflix and Spotify, with watchlists and playlists that stretch on for years.

citing a culture in which schools "discourage students from thinking of themselves as individuals.

low work satisfaction, competitiveness, and the urban experience: "Staying emotionally neutral could be a way of coping with the stress of urban life

The Philippines is the world's most emotional country.

Post-Soviet countries are consistently among the most stoic.

. They are also the greatest consumers of cigarettes and alcohol. This could be what you call and chicken-or-egg problem: if the two trends are related, which one came first?

urope appears almost like a gradient here, with emotions increasing as you move West. 

English- and Spanish-speaking societies tend to be highly emotional and happy.

it's not clear if Spain's emotional depth has anything to do with Latin America's.

Africans are generally stoic, with some significant exceptions. The continent is among the world's least emotional, though there is wide variation, which serves as a non-definitive but interesting reminder of Africa's cultural diversity.

The Middle East is not happy

leading the world in negative daily experiences

Still, that doesn't quite fully explain the high emotions in the Levant and on the Arabian peninsula, compared to the lower emotions in Libya, Algeria, and Morocco.

We get more of what we signal we want through our dollars, clicks and votes. If our politics are too often poisonous, it is because, as a society, we are demanding too much poison.

We should ask ourselves: What will my next click say about my desires? Will the next article about politics I read elevate me? Or will it be a pathogen that provides momentary satisfaction from an eloquent insult to my enemies, but ultimately fuels personal bitterness and increases the climate of acrimony in America?

what do we really demand of the politicians we support? Humility, optimism and flexibility? Or do we excuse our own side for its ideological rigidity, preening self-regard and blame-shifting?

The next two years are a challenge to our political leaders, yes — but also to us, to demand a climate of moral elevation as opposed to destruction of the other side.

Various companies are now trying to emulate Uber’s business model in other fields, from daily chores like grocery shopping and laundry to more upmarket products like legal services and even medicine.

“We may end up with a future in which a fraction of the work force would do a portfolio of things to generate an income — you could be an Uber driver, an Instacart shopper, an Airbnb host and a Taskrabbit,”

But the rise of such work could also make your income less predictable and your long-term employment less secure. And it may relegate the idea of establishing a lifelong career to a distant memory.

“This on-demand economy means a work life that is unpredictable, doesn’t pay very well and is terribly insecure.” After interviewing many workers in the on-demand world, Dr. Reich said he has concluded that “most would much rather have good, well-paying, regular jobs.”

Proponents of on-demand work point out that many of the tech giants that sprang up over the last decade minted billions in profits without hiring very many people; Facebook, for instance, serves more than a billion users, but employs only a few thousand highly skilled workers, most of them in California.

at the end of 2014, Uber had 160,000 drivers regularly working for it in the United States. About 40,000 new drivers signed up in December alone, and the number of sign-ups was doubling every six months.

The report found that on average, Uber’s drivers worked fewer hours and earned more per hour than traditional taxi drivers, even when you account for their expenses. That conclusion, though, has raised fierce debate among economists, because it’s not clear how much Uber drivers really are paying in expenses. Drivers on the service use their own cars and pay for their gas; taxi drivers generally do not.

A survey of Uber drivers contained in the report found that most were already employed full or part time when they found Uber, and that earning an additional income on the side was a primary benefit of driving for Uber.

The larger worry about on-demand jobs is not about benefits, but about a lack of agency — a future in which computers, rather than humans, determine what you do, when and for how much. The rise of Uber-like jobs is the logical culmination of an economic and tech system that holds efficiency as its paramount virtue.

“These services are successful because they are tapping into people’s available time more efficiently,” Dr. Sundararajan said. “You could say that people are monetizing their own downtime.”Think about that for a second; isn’t “monetizing downtime” a hellish vision of the future of work?

“I’m glad if people like working for Uber, but those subjective feelings have got to be understood in the context of there being very few alternatives,” Dr. Reich said. “Can you imagine if this turns into a Mechanical Turk economy, where everyone is doing piecework at all odd hours, and no one knows when the next job will come, and how much it will pay? What kind of private lives can we possibly have, what kind of relationships, what kind of families?”

In doing so, Nietzsche disassociates love from its other-worldly Christian-Platonic heritage, and so asserts his ethical claims concerning the value of the Earth over the other-worldly, and the truth of the body over the sacred.

Nietzsche speaks critically about the possessive or tyrannical qualities of masculine love alongside its fictionalising tendencies, stating that the natural functions of a woman’s body disgust men because they prevent him having complete access to her as a possession; they also encroach upon the conceptual perfection of love. He writes, “‘The human being under the skin’ is for all lovers a horror and unthinkable, a blasphemy against God and love.”

In other words, the experiences of both greed and love are the same drive or instinct, but depending upon the level of satisfaction one has achieved, this drive will be alternatively named ‘greed’ or ‘love’: satisfied people who feel their possessions (their lover for example) threatened by others will name other’s instinct for gain greed or avarice, whereas those who are still searching out something new to desire will impose a positive evaluation on that instinct and call it ‘love’.

Nietzsche pointedly distinguishes masculine from feminine love by the notions of devotion and fidelity. Whereas women want to surrender completely to love, to approach it as a faith, “to be taken and accepted as a possession” (363), Nietzsche claims male love hinges upon the possessive thirst to acquire more from the lover, and states that men who are inclined towards complete devotion are “not men.”

He proposes that love is close to greed and the lust for possession. Love is an instinctual force related to our biological and cultural drives, and as such, cannot be considered a moral good (GS 363).

In order to be successful in love, he counsels women to “simulate a lack of love” and to enact the roles that men find attractive. Nietzsche finds love comedic because it does not consist in some attempt to know the other deeply, but rather in the confirmation of male fantasies in which women perform their constructed gender roles.

Nietzsche’s writings on love have not surprisingly been influential on many feminist reflections on sex/gender. Although he is not making moralising claims about how one should love, his discussion of the difficult impact erotic and romantic relationships have on women, as well as his commentary on the ironies both sexes face in love, force his readers of both sexes to examine the roles that they play in love. It is difficult when reading him not to question one’s own performances in romantic relationships.

Rousseau wrote about “amour-propre,” a kind of self-love based on the opinions of others. He considered it unnatural and unhealthy, and believed that arbitrary social comparison led to people wasting their lives trying to look and sound attractive to others.

Narcissus falls in love not with himself, but with his reflection. In the modern version, Narcissus would fall in love with his own Instagram feed, and starve himself to death while compulsively counting his followers.

If our egos are obese with amour-propre, social media can indeed serve up the empty emotional carbs we crave. Instagram and the like doesn’t create a narcissist, but studies suggest it acts as an accelerant — a near ideal platform to facilitate what psychologists call “grandiose exhibitionism.”

No doubt you have seen this in others, and maybe even a little of it in yourself as you posted a flattering selfie — and then checked back 20 times for “likes.”

A healthy self-love that leads to true happiness is what Rousseau called “amour de soi.” It builds up one’s intrinsic well-being, as opposed to feeding shallow cravings to be admired.

First, take the Narcissistic Personality Inventory test.

Here is an individual self-improvement strategy that combines a healthy self-love (for Valentine’s Day) with a small sacrifice (possibly for Lent).

Cultivating amour de soi requires being fully alive at this moment, as opposed to being virtually alive while wondering what others think. The soulful connection with another person, the enjoyment of a beautiful hike alone (not shared on Facebook) or a prayer of thanks over your sleeping child (absent a #blessed tweet) could be considered expressions of amour de soi.

Second, get rid of the emotional junk food that is feeding any unhealthy self-obsession. Make a list of opinions to disregard — especially those of flatterers and critics — and review the list each day. Resolve not to waste a moment trying to impress others,

Third, go on a social media fast. Post to communicate, praise and learn — never to self-promote.

As for clinically significant narcissism—along with greed, invidious prejudice, and habitual lying—it is simply another one of our anti-social behaviors that mutated from our basic genetic drives…in this case the drive to survive. The opposite of narcissism is empathy, a brain-wiring that evolved much later and in parallel with our increased reliance on social interaction as a means to improve the chances of sending our genes down the line (the drive to reproduce). There is thus a certain irony in the fact that the misnamed “social” media are encouraging a decline in empathy. Your thoughts?

Sure you're not confusing narcissism with vanity? If you've ever had the misfortune of having someone with narcissistic personality disorder in your life, you would know it's about more than selfies and seeking constant approval. They are truly sick individuals that destroy the lives of those they claim to love.I would say people's addictions to social media "likes" and posting selfies is vanity

Perhaps we need to distinguish between Narcissistic Personality Disorder (NPD) and the adjective "narcissistic." We all know lots of people with way too much self-regard. NPD on the other hand ruins lives and certainly families. People who have NPD are way beyond self centered. They see the world as black and white and all people they interact with become reflections. People with NPD go to extreme lengths to control those around them and will lie, cheat and steal to do that. They are never wrong the other person is always wrong. I have worked for Narcissists and lived with one. Let's not throw around this term without defining it, please.

The second model is Maslow’s famous hierarchy of needs. In this conception, we start out trying to satisfy our physical needs, like hunger or thirst. Once those are satisfied we move up to safety needs, economic and physical security. Once those are satisfied we can move up to belonging and love. Then when those are satisfied we can move up to self-esteem. And when that is satisfied we can move up to the pinnacle of development, self-actualization, which is experiencing autonomy and living in a way that expresses our authentic self.

Most religions and moral systems have aimed for self-quieting and, figuring that the great human problem is selfishness. But around the middle of the 20th century, Abraham Maslow, Carl Rogers and others aimed to liberate and enlarge the self. They brought us the self-esteem movement, humanistic psychology, and their thinking is still very influential today.

In the Four Happiness frame, by contrast, marriage can be a school in joy. You might go into marriage in a fit of passion, but, if all works out, pretty soon you’re chopping vegetables side by side in the kitchen

None of us lives up to our ideals in marriage or anything else. But at least we can aim high. Maslow’s hierarchy of needs too easily devolves into self-absorption. It’s time to put it away.

Suicide rates are now at a 30-year high. Depression rates have increased tenfold since 1960, which is not only a result of greater reporting.

Most children born to mothers under 30 are born outside of marriage.

There’s been a steady 30-year decline in Americans’ satisfaction with the peer-to-peer relationships at work.

“During my years caring for patients, the most common pathology I saw was not heart disease or diabetes; it was loneliness.”

Weak social connections have health effects similar to smoking 15 cigarettes a day, and a greater negative effect than obesity, he said.

In 2012, 5.9 percent of young people suffered from severe mental health issues. By 2015 it was 8.2 percent.

More and more Americans are socially poor. And yet it is very hard for the socially wealthy to even see this fact.

Most of the questions he faced at the congressional hearings and most of the analysis in the press were about Facebook’s failure to protect privacy. That’s the sort of thing that may be uppermost on your mind if you are socially wealthy

But the big issue surrounding Facebook is not privacy. It’s that Facebook and other social media companies are feeding this epidemic of loneliness and social isolation.

heavy internet users are much less likely to have contact with their proximate neighbors to exchange favors and extend care. There’s something big happening to the social structure of neighborhoods.

The British anthropologist Robin Dunbar observes that human societies exist on three levels: the clan (your family and close friends), the village (your local community) and the tribe (your larger group)

In America today you would say that the clans have polarized, the villages have been decimated and the tribes have become weaponized.

That is, some highly educated families have helicopter parents while less fortunate families have absent parents. The middle ring cross-class associations of town and neighborhood have fallen apart. People try to compensate for the lack of intimate connection by placing their moral and emotional longings on their political, ethnic and other tribes, turning them viciously on each other.

many of us who are socially wealthy don’t really know how the other half lives.

But we’re not happier.

Harari points out that initially happiness was introduced as a check on state power.

Given how few we encounter on a regular basis, this threat detection system has been co-opted by the luxury of security, causing Harari to realize that: The most common reaction of the human mind to achievement is not satisfaction, but craving for more.

The Buddha actually warned against such a chase, deciding contentment was more worthwhile (and saner). The pursuit of pleasure is the root of suffering.

As Trentmann points out, the Latin consumere meant a “physical exhaustion of matter.” For example, the wasting disease, tuberculosis, was referred to as consumption. While we’ve redefined that word in modern times the original intention seems destined to win out.

The same technology that renders humans useless might also make it feasible to feed and support the unemployable masses through some scheme of universal basic income.

The real problem will then be to keep the masses occupied and content. People must engage in purposeful activities, or they go crazy. So what will the useless class do all day?

One answer might be computer games. Economically redundant people might spend increasing amounts of time within 3D virtual reality worlds, which would provide them with far more excitement and emotional engagement than the “real world” outside.

This, in fact, is a very old solution. For thousands of years, billions of people have found meaning in playing virtual reality games. In the past, we have called these virtual reality games “religions”.

Muslims and Christians go through life trying to gain points in their favorite virtual reality game. If you pray every day, you get points. If you forget to pray, you lose points. If by the end of your life you gain enough points, then after you die you go to the next level of the game (aka heaven).

As religions show us, the virtual reality need not be encased inside an isolated box. Rather, it can be superimposed on the physical reality. In the past this was done with the human imagination and with sacred books, and in the 21st century it can be done with smartphones.

Consumerism too is a virtual reality game. You gain points by acquiring new cars, buying expensive brands and taking vacations abroad, and if you have more points than everybody else, you tell yourself you won the game.

we saw two others kids on the street who were hunting the same Pokémon, and we almost got into a fight with them. It struck me how similar the situation was to the conflict between Jews and Muslims about the holy city of Jerusalem. When you look at the objective reality of Jerusalem, all you see are stones and buildings. There is no holiness anywhere. But when you look through the medium of smartbooks (such as the Bible and the Qur’an), you see holy places and angels everywhere.

In the end, the real action always takes place inside the human brain. Does it matter whether the neurons are stimulated by observing pixels on a computer screen, by looking outside the windows of a Caribbean resort, or by seeing heaven in our mind’s eyes?

Indeed, one particularly interesting section of Israeli society provides a unique laboratory for how to live a contented life in a post-work world. In Israel, a significant percentage of ultra-orthodox Jewish men never work. They spend their entire lives studying holy scriptures and performing religion rituals. They and their families don’t starve to death partly because the wives often work, and partly because the government provides them with generous subsidies. Though they usually live in poverty, government support means that they never lack for the basic necessities of life.

That’s universal basic income in action. Though they are poor and never work, in survey after survey these ultra-orthodox Jewish men report higher levels of life-satisfaction than any other section of Israeli society.

Hence virtual realities are likely to be key to providing meaning to the useless class of the post-work world. Maybe these virtual realities will be generated inside computers. Maybe they will be generated outside computers, in the shape of new religions and ideologies. Maybe it will be a combination of the two. The possibilities are endless

In any case, the end of work will not necessarily mean the end of meaning, because meaning is generated by imagining rather than by working.

People in 2050 will probably be able to play deeper games and to construct more complex virtual worlds than in any previous time in history.

But what about truth? What about reality? Do we really want to live in a world in which billions of people are immersed in fantasies, pursuing make-believe goals and obeying imaginary laws? Well, like it or not, that’s the world we have been living in for thousands of years already.

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.

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.

“What I’ve found throughout life is doing those things which you know are right, which respond to the promptings of your conscience, allows you to have a greater degree of happiness and satisfaction than if you just do things to try and get ahead,”

The ‘urge’ to check one’s social media may be linked to both instant gratification (the need to experience fast, short term pleasure) and dopamine production (the chemical in the brain associated with reward and pleasure).

The popular concept of Fear of Missing Out (FOMO) refers to ‘a pervasive apprehension that others might be having rewarding experiences from which one is absent’ and is ‘characterised by the desire to stay continually connected with what others are doing’.

Data from qualitative studies has shown that using social media compulsively can damage sleeping patterns, having an adverse effect on young people’s performance in school

The University of Glasgow found that young people found it difficult to relax following night time social media use, reducing their brain’s ability to prepare for sleep. Sleep loss works in a vicious cycle of reinforcement with mental health; that is, that loss of sleep due to night time social media use can lead to poorer mental health, and poor mental health can lead to intense night time use and sleep loss

What is dangerous about this compulsive use is that, if gratification is not experienced, users may internalise beliefs that this is due to being ‘unpopular’, ‘unfunny’ etc. A lack of ‘likes’ on a status update may cause negative self-reflection, prompting continual ‘refreshing’ of the page in the hope of seeing that another person has ‘enjoyed’ the post, thus helping to achieve personal validation.

FOMO has been linked to intensive social media use and is associated with lower mood and life satisfaction.

Social media has been linked to poor self-esteem and self-image through the advent of image manipulation on photo sharing platforms. In particular, the notion of the ‘idealised body image’ has arguably been detrimental to self-esteem and image, especially that of young women. The 24/7 circulation of easily viewable manipulated images promotes and entrenches unrealistic expectations of how young people should look and behave.

The evidence suggests that social media use is strongly associated with anxiety, loneliness and depression

And studies have confirmed that people tend to undergo a kind of withdrawal: A study a few years ago from Swansea University found that people experienced the psychological symptoms of withdrawal when they stopped using (this went for all internet use, not just social media).

It triggers more sadness, less well-being

The more we use social media, the less happy we seem to be. One study a few years ago found that Facebook use was linked to both less moment-to-moment happiness and less life satisfaction—the more people used Facebook in a day, the more these two variables dropped off

In fact, another study found that social media use is linked to greater feelings of social isolation.

Comparing our lives with others is mentally unhealthy

One study looked at how we make comparisons to others posts, in “upward” or “downward” directions—that is, feeling that we’re either better or worse off than our friends.

It can lead to jealousy—and a vicious cycle

Studies have certainly shown that social media use triggers feelings of jealousy. The authors of one study, looking at jealousy and other negative feelings while using Facebook, wrote that “This magnitude of envy incidents taking place on FB alone is astounding, providing evidence that FB offers a breeding ground for invidious feelings."

We get caught in the delusion of thinking it will help

Part of the unhealthy cycle is that we keep coming back to social media, even though it doesn’t make us feel very good.

This is probably because of what’s known as a forecasting error: Like a drug, we think getting a fix will help, but it actually makes us feel worse, which comes down to an error in our ability to predict our own response.

More friends on social doesn’t mean you’re more social

A couple of years ago, a study found that more friends on social media doesn’t necessarily mean you have a better social life—there seems to be a cap on the number of friends a person’s brain can handle, and it takes actual social interaction (not virtual) to keep up these friendships

All of this is not to say that there’s no benefit to social media—obviously it keeps us connected across great distances, and helps us find people we’d lost touch with years ago

mental framing can not only impact our mental health but also result in physiological differences — for instance, changes in blood pressure and heart rate — and our capacity to recover more quickly after a challenging situation.

“Happiness is hard to know. It’s hard to measure,”

“We don’t have a lot of great data about how people are doing.”

to parse our national mental health through the prism of our online life.

that stockpile of information towered as high as it does now, in the summer of 2020

, Twitter reported a 34 percent increase in daily average user growth.

has gathered a random 10 percent of all public tweets, every day, across a dozen languages.

Twitter included “terrorist,” “violence” and “racist.” This was about a week after George Floyd was killed, near the start of the protests that would last all summe

the pandemic, the Hedonometer’s sadness readings have set multiple records. This year, “there was a full month — and we never see this — there was a full month of days that the Hedonometer was reading sadder than the Boston Marathon day,”

“These digital traces are markers that we’re not aware of, but they leave marks that tell us the degree to which you are avoiding things, the degree to which you are connected to people,”

one of the challenges of this line of research is that language itself is always evolving — and algorithms are notoriously bad at discerning context.

they were able to help predict which ones might develop postpartum depression, based on their posts before the birth of their babies.

Using data from social media for the study of mental health also helps address the WEIRD problem:

psychology research is often exclusively composed of subjects who are Western, Educated, and from Industrialized, Rich, and Democratic countries.

We’re now able to look at a much more diverse variety of mental health experiences.”

but also anxiety, depression, stress and suicidal thoughts. Unsurprisingly, she found that all these levels were significantly higher than during the same months of 2019.

is really a representative place to check the state of the general population’s mental health.

argues that in the rush to embrace data, many researchers ignore the distorting effects of the platforms themselves.

emotionally invested in the content we are presented with, coaxed toward remaining in a certain mental state.

The closest we get to looking at national mental health otherwise is through surveys like the one Gallup performs

the lowest rates of life satisfaction this year in over a decade, including during the 2008 recession

I have never been more exhausted at the end of the day than I am now,” said Michael Garfinkle, a psychoanalyst in New York.

There are so many contenders to consider: was it Thursday, March 12, the day after Tom Hanks announced he was sick and the N.B.A. announced it was canceled? Was it Monday, June 1, the day peaceful protesters were tear gassed so that President Trump could comfortably stroll to his Bible-wielding photo op?

When what we’re doing feels fascinating, such as reading a book we can’t put down, we’re propelled by intrinsic motivation; when we pay attention in a class or meeting by promising ourselves 10 minutes of online shopping for seeing it through, we’re summoning extrinsic motivation.

Should adults be cheerleaders for our teenagers? Opinion is split. Some researchers contend that praise helps to cultivate intrinsic motivation, while others say that it undermines it by introducing an extrinsic reward

It’s also true that intrinsic and extrinsic motivation aren’t mutually exclusive

In practice, this means that young people should be given as much say over their learning as possible, such as giving them options for how to solve problems, approach unfamiliar topics or practice new skills.

Young people may find themselves intrinsically motivated on Mondays, but not Fridays, or at the start of an evening study session but not as the night wears o

There is, however, an area of consensus: the utility of praise depends on how it’s done. Specifically, praise fosters intrinsic motivation when it’s sincere, celebrates effort rather than talent (“you worked really hard,” vs. “you’re so smart”) and communicates encouragement, not pressure (“you’re doing really well,” vs. “you’re doing really well, as I hoped you would”)

Adults should be ready to stand back and admire the fantastic solutions that young people land upon themselves.

I recently learned of a 10th-grader who makes time-lapse videos of herself while she does her homework. Knowing that she’s on camera keeps her focused, and having a record of her efforts (and the amusing faces she makes while concentrating) turns out to be a powerful reward. While intrinsic motivation has its upsides, there should be no shame in the external motivation game. It’s about getting the work done.