The Formula: The Universal Laws of Success — Albert Barabasi
Written by a physicist best known for his work in network theory, The Formula starts with a lofty mission of quantifying the science of human accomplishment and codifying this complex phenomenon into universal laws, but it succeeds in a way that his previous books, criticized for being too repetitive or self-aggrandizing, did not.
First, success, “isn’t about you and your performance. It’s about us and how we perceive your performance. Or, to put it simply, your success is not about you, it’s about us,” which tells us that “success is a collective phenomenon rather than an individual one.”
One interesting (but hardly infallible) approach to measuring success is the number of languages a person’s Wikipedia page is published in, a MIT Media Lab project, link.
The Laws
THE FIRST LAW: Performance drives success, but when performance can’t be measured, networks drive success. As we journey from tennis courts to art galleries, we’ll see why it’s not the reputable schools we attend that makes us succeed, but our success that makes a school reputable. Most important, we’ll learn to see the largely invisible networks that shape our success.
And how the art world is the perfect evidence of this.
Consider the Mona Lisa, which “became a household name only after it was stolen in broad daylight in 1911… If it were ever to sell, it’s estimated that it would fetch an unheard-of $1.5 billion. If art has no inherent value, then where does that $1.5 billion price tag come from? Networks.”
Because “there’s no meter to gauge the quality of any given piece, no objective way to measure an object’s inherent worth… Value in art is in the network. Any work, from the Mona Lisa to Basquiat’s Untitled, is garage sale material without it.”
And on schools:
“high achievers continue to excel no matter what education a school offers… The school doesn’t ultimately matter; the student does.”
and when Princeton economists looked at income a decade post-graduation, the “the key factor determining income a decade after graduation was not the college they attended. The single determinant of long-term success was derived from the best college a kid merely applied to, even if she didn’t get in. Meaning that if she applied to Harvard, got rejected, and went to Northeastern, her success was on a par with that of Harvard graduates who matched her SATs and high school grades. In other words, it’s performance and ambition—where she thinks she belongs—that determine your daughter’s success.”
THE SECOND LAW Performance is bounded, but success is unbounded. The Second Law explains the hidden factors that shape our choices. It tells us why experts are doomed to failure when they try to select the best wines or the most accomplished violinists. The law explains why Tiger Woods’s competitors play measurably worse when he’s on the green and why the last interviewee almost always gets the job.
Great example of top quality wines (and Usain Bolt), which even professional judges have a hard time judging consistently. For example, when judges were made to test the same wines repeatedly but in a random order “they scored the identical wines as if they were different.”
Essentially there is an upper limit on performance, also why the 100m record time won’t improve by very much, but success, the dividends of being #1, has no limit/bounds.
“Woods is what economists call a superstar, someone exceptionally rewarded for exceptional performance. Superstars exist because success is unbounded. By performing even a fraction better than your competitors, your reward can easily be hundreds, sometimes thousands of times greater.”
But, “Exceptional reward only comes from talents that are easily and cheaply disseminated. To be a superstar economically, in other words, your performance must scale. Today, the head coach at the University of Texas earns over $5 million yearly, more than any other university employee, around twenty times what he made in 1991. Weinberg [world-famous physicist], who according to public records now earns in the neighborhood of $575,000, on the other hand, has merely doubled his income in the decades since his hire.”
“To reap enormous reward, the product you provide must be easily reproduced. Sherwin Rosen, as he was developing his economic theory of superstardom in 1981, predicted that advances in technology would further exaggerate the superstar phenomenon by allowing a few performers to access an even larger audience. These looming figures would, at the same time, reduce the reach of their less visible peers. The already wide gap between the band playing in the local bar and the band playing on TV would widen further.”
But, if you put a value of $100K on each scientific citation, which we know “by looking at the amount of money the nation spends on research in hopes of furthering a variety of scientific causes, from medical breakthroughs to innovative products, or new insights on the origins of our universe.” then Weinberg’s 1967 discovery should have a scientific impact worth $1.4 billion.
The idea that luck and other hidden factors play a crucial role in shaping success (and our lives), timing (referred to here in the context of immediacy bias, i.e. later performers doing better), also picked up in Pink’s When
also Israeli parole judge study showing that judges were 65% likely to grant parole after a break/lunch vs. 0% right before lunch
Barabasi uses a Spanish courtroom judge application study that showed 75% success if tested on Friday vs. 50% if early-week.
THE THIRD LAW Previous success × fitness = future success. It’s the law that shows us how a subtle phenomenon, preferential attachment, governs all success, from a petition’s popularity to reading comprehension in children. When fitness and social influence work in tandem, success has no boundaries.
preferential attachment, the richer getting richer, success breeds success
great experiment on Wikipedia where random editor rewards were given out, and subsequently the prizewinners‘ productivity jumped by 60% compared to non-rewarded editors… leading the more productive editors to receive more rewards
and with kids, teachers were given the names of students who scored in the top 20% of a fictitious Harvard Test of Inflected Acquisition, and consequently showed the greatest progress in IQ scores
THE FOURTH LAW While team success requires diversity and balance, a single individual will receive credit for the group’s achievements. It’s a law embodied by chickens and soccer players—who show us how all-star teams are bound to fail—and by jazz musicians, Broadway hit makers, and call-center representatives, who help us strategize to maximize group success. A van driver overlooked for a Nobel Prize serves as a cautionary tale: a lot can go wrong when we let the community assign credit.
“Credit for teamwork isn’t based on performance. Credit is based on perception.”
THE FIFTH LAW With persistence success can come at any time. The Fifth Law explains how it’s possible to do Nobel-winning research after retirement and why it feels like some people are playing the success game with loaded dice. We’ll encounter the Q-factor, which allows us to reduce innovation to an equation. The Fifth Law tells us that while success melts like a snowflake, creativity has no expiration date.
“The Fifth Law tells us that while success melts like a snowflake, creativity has no expiration date.” which runs counter to all the anecdotal evidence that most brilliant scientific discoveries come in a scientist’s early days, which Barabasi accounts for as deriving from effort not creativity
“Our measurements showed that research papers are like lottery tickets in a scientist’s life. Each has exactly the same odds of becoming a breakthrough. So, in the period when a researcher publishes at his or her best pace—finishing project after project in rapid-fire succession—they tend to experience their greatest success. Not because they’re more creative during this burst of activity. They succeed because they try more often.”
And on reducing innovation to an equation:
“S=Qr In other words, the success of a product or a deal, or the impact of a discovery, will be the product of a creator’s Q-factor and the value of idea r.
And how Einstein is the perfect embodiment of all Five Laws, but most especially the Fourth:
“Einstein’s story hinges on a misattribution. Those crowds of Zionists were credited to Einstein not because he was the most important advocate of the cause. He was, at best, a bit player. But because he was the most widely recognizable person in the delegation to the gentile press, he got the headlines.”
Why Einstein Became Famous in America: “When Einstein came to the United States in 1921 as part of a Zionist delegation, the warm welcome American Jews gave the delegation, and Chaim Weizmann in particular, was mistakenly described by the American press as a hero’s welcome for Einstein. This led to a complex series of interactions between the Yiddish and English language press that resulted in Einstein being considered a hero and a secular saint.”
Notebook Export
The Formula
Introduction: Success Isn’t About You. It’s About Us.
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These days I run the Center for Complex Network Research, in Boston, where my job is exploring the “why” behind topics as varied as how people or molecules interact, where and how links form, and what our interconnectedness can tell us about society or our biological origins.
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It took a few years, but after harvesting mountains of data on human accomplishment, we figured out a way to break the concept down to its constituents and study its gears. Our goal was to formulate success as a mathematical problem that computer scientists and physicists, using the unforgiving tools of quantitative science, could address in a definite fashion. It wasn’t that different from pulling apart a bike, or using thermodynamics to fathom solar heat.
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Exactly how, for instance, did we decide that this—the blurry, unremarkable photo hanging in the Museum of Modern Art—is a masterpiece? Why is Carousel, not Cats, the best musical ever? Are expensive schools worth it? Why are there a mere handful of superstars in any field? Add these to the hundreds of other questions about success, achievement, and reputation that seem, like the sun’s temperature, impossible to pin down. Is it our performance that pushes us up the corporate ladder? Do we get less or more creative over our lifetimes? Should we collaborate or compete with superstars? How do networks—social and professional—affect our access to success? Believe it or not, quantitative answers can be found for all these seemingly unquantifiable questions. By examining the patterns in the data and identifying the mechanisms that produce success, we determined that we could address each of the questions head-on. Once we began to comprehend the universal forces at work behind our individual successes and failures, fascinating findings started to emerge.
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In fact, for the purposes of this book, we’ll be defining success along these lines: it is the rewards we earn from the communities we belong to. In the case of Einstein—the “Man of the Century,” according to Time—that reward is fame. But it can be recognition if you’re a collaborator, visibility if you’re a brand, renown if you’re an artist, album or ticket sales if you’re a musician, revenue if you’re in business or sales, earnings if you’re a banker, audience if you’re a playwright, citations if you’re a scientist, endorsements if you’re an athlete, and impact if you hope to make a difference in virtually any field. These success measures all have one thing in common: they are external, not internal; collective, not individual.
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So while I recognize the importance of personal fulfillment, it’s not a factor I can consider in my work as a researcher. Respecting that boundary has been oddly freeing. The popular definition of success reinforces the perception that “success” is as loose a concept as “love.” The topic’s vagueness kept scientists away—they assumed that it couldn’t be studied. Realizing that success is a collective phenomenon throws that perception out the window. Once we defined success along external lines, a whole new set of possibilities opened up. We could measure and quantify it, using tools of scientific inquiry. And once we did that, we could unveil the laws that govern our success.
1 The Red Baron and the Forgotten Ace
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Altogether, the 392 German aces claimed a total of 5,050 victories. The eighty Allied planes that von Richthofen personally sent careening out of the sky is a stunning individual record. But it’s a mere 1.6 percent of the total. Pocket change in the larger scheme. Yet he generated 27 percent of the German aces’ Google hits. He takes up far more room in our collective consciousness than any of his compatriots.
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Except then there’s René Fonck. René who? you’ll probably ask, echoing the befuddlement I felt when I came across a little-read article about him. His obscurity is downright bizarre. Fighting for the Allies in the same theater as the Red Baron, Fonck, a skilled French pilot, claimed to have gunned down 127 German planes. Seventy-five of these victories have been independently confirmed, making him, at the very least, the second most accomplished pilot of the war. If we add to his tally the most probable of his unproven claims, we arrive at a total of one hundred or more. That means that for all intents and purposes, Fonck was the Red Baron’s equal in aerial warfare, and most likely his superior.
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Take other examples: Claudette Colvin, an African-American teenager in Montgomery, Alabama, who refused to give up her bus seat to a white passenger in 1955. Her gesture presaged Rosa Parks’s by nine months. Same action, same city, same time frame. Yet no one mentions Colvin when students are taught about the heroes of the U.S. civil rights movement. Edison gets credit for X-ray photography, moving pictures, recorded audio, and the light bulb, when in fact all were discovered by other scientists or inventors. And then there are the Wright brothers, the inventors of the airplane according to the schoolbooks. Never mind that the first powered flight was executed nine months prior to theirs, by a New Zealander named Richard Pearse. Seemingly, it’s the last person who makes a discovery that really matters, not the first.
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The Red Baron’s and René Fonck’s vast difference in lasting renown, despite their indistinguishable performances, speaks to the most fundamental principle of the Science of Success, and our definition of the term “success” moving forward. Your success isn’t about you and your performance. It’s about us and how we perceive your performance. Or, to put it simply, your success is not about you, it’s about us.
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Performance—or what you do, whether it’s your bike-racing record or the number of cars you’ve sold or your score on a multiple-choice exam—is certainly a variable where you have some control. You can perfect your performance by honing your skills, practicing, preparing, and strategizing. You can even compare your performance against that of others and determine where you stand. Success, however, is a whole different category. It’s a collective measure, capturing how people respond to our performance. In other words, if we want to measure our success or figure out how we’ll ultimately be rewarded, we can’t look at our performances or accomplishments in isolation. Instead, we need to study our community and examine its response to our contributions.
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Our new definition of success is foundational to the rest of the book. It tells us that success is a collective phenomenon rather than an individual one.
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A baron by birth and a warrior in death, he was enshrined as an enduring symbol of patriotism and heroism. The same factors on the other side of the front line should have also pushed Fonck to prominence. And in many ways they did, at least at first. During the war, he received all the honors an ace pilot could hope for. His notoriety even got him elected to the French Parliament. But then the public turned on him. His first mistake was that he wasn’t killed. Surviving World War I, he landed in politically murky waters during the Nazi occupation of France during World War II. He also failed as a demonstration pilot, crash-landing on takeoff while attempting the first flight from Paris to New York. But details aside, the key distinction between the two men is that one was useful to his network and the other was not. The Red Baron’s success was about what was happening politically and socially during the war, not only about how many planes he shot down, or how vain he was, or how he felt about his accomplishments. We remember him today because he was once vital to the German propaganda machine. His reputation was left in the hands of those desperate for a hero to galvanize their spirit. The broad public, responding to the Red Baron’s performance, created a myth about him that served its purposes. In other words, the network found him useful and chose to amplify his success.
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But as I invoke Snoopy as an arbiter of success, it is important to clarify that the Red Baron was not only successful. He was also famous. His unlikely appearance in an American cartoon decades after his death is proof positive. Which raises an important question. Can we separate success from fame? Do we have to?
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There are multitudes of Nabokovs and Einsteins out there. They accumulate success through their performance, and then their success brings them recognition, radiating far beyond their professional networks. And once people become recognizable names outside of their professional networks, to the point that their future performances are secondary to our appreciation of them, we bestow the mantle of fame. Fame is the rare side effect of exceptional success.
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If you want to know who’s more famous than Jesus (hint: it’s not the Beatles), you can search the Pantheon Project, an online tool created by César Hidalgo, my brilliant former student, now a professor at MIT’s Media Lab. According to César, the truly famous are those known beyond their local spheres. Instead of measuring fame using Google hits, as in the ace pilots study, he uses Wikipedia pages—or to be precise, the number of languages a person’s Wikipedia page is published in. To be included in the pantheon, a person’s renown has to have crossed national and linguistic barriers, and must be represented on Wikipedia in at least twenty-five languages. This single requirement narrows the famous down from practically any minor celebrity or vaguely notable person to 11,341 individuals, members of a fascinating and motley crew.
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Interesting but subjective
The First Law: Performance drives success, but when performance can’t be measured, networks drive success.
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THE FIRST LAW Performance drives success, but when performance can’t be measured, networks drive success. As we journey from tennis courts to art galleries, we’ll see why it’s not the reputable schools we attend that makes us succeed, but our success that makes a school reputable. Most important, we’ll learn to see the largely invisible networks that shape our success.
2 Grand Slams and College Diplomas
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As Burcu crunched the numbers, a pattern emerged. No matter if a player was top-ranked—Roger Federer, Novak Djokovic, Andy Murray, Rafael Nadal—or an up-and-coming novice on the circuit, there was a remarkable synchronicity between her predictions and the athlete’s true visibility. Performance was so married to success that Burcu could accurately anticipate the number of people likely to flock to a player’s Wiki page on any given day following his performance on court. She could foresee periods of low Wiki traffic for poorer-performing players, along with the valleys created by injuries. But she could also predict the huge peaks of attention for an unexpected win against a prominent player. And once she had the performance data, Burcu could predict success. There was only one way to interpret her results: Success in tennis is determined by a single factor—strong athleticism. At least on the court, the dogma of the classic, hard-work-yields-reward strategy holds true. Performance drives success.
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We’ve established that, as a group, the Boston Latin kids do perform better when compared to their counterparts at Latin Academy. Their SAT scores are higher. No one disputes that. What the data tells us, though, is that the difference—despite what parents think, teachers suggest, and principals claim—is not because the school enhances their performance. It’s because high achievers continue to excel no matter what education a school offers. The Boston Latin students have that superior collective SAT score at graduation because the entrance exam selected the top performers to begin with. And they simply carried those abilities through high school. In other words, Boston Latin doesn’t make your daughter a better student. It’s your daughter who makes Boston Latin into the elite school it is. The message is clear. The school doesn’t ultimately matter; the student does.
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two Princeton economists took great pains to sort out which factors determine the long-term success of college graduates. They started by comparing students who applied to elite universities but ended up, for various reasons, at less prestigious ones. Remember that the data is pretty clear on this score: the median annual income of Ivy League graduates is about $70,000 ten years later, twice the pay of non-Ivy grads. Yet to the surprise of the researchers, the graduates of humbler schools that snubbed the Ivy League were earning just as much as Ivy League alums.
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But the most unforeseen conclusion of the Princeton study came when the researchers looked at those who weren’t accepted into Ivy League colleges. After accounting for all performance measures for students, such as SAT scores and rankings in their high school class, the key factor determining income a decade after graduation was not the college they attended. The single determinant of long-term success was derived from the best college a kid merely applied to, even if she didn’t get in. Meaning that if she applied to Harvard, got rejected, and went to Northeastern, her success was on a par with that of Harvard graduates who matched her SATs and high school grades. In other words, it’s performance and ambition—where she thinks she belongs—that determine your daughter’s success.
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Self selection... correlation not causation
3 The $2 Million Urinal
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Their careers began at the same time and place. Their work was initially indistinguishable. But Diaz has made art in relative obscurity ever since. Basquiat, on the other hand, was a sensation as a living artist and a rampant success as a dead one. So how do we explain Diaz’s and Basquiat’s divergent trajectories? They differed in one essential aspect: Diaz was a loner. Basquiat, on the other hand, was an unapologetic networker. This was evident even during their adolescent SAMO phase, when Diaz insisted that they keep their shared identity a secret. Basquiat? He outed the partnership to the Village Voice for one hundred dollars.
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Since success is a collective phenomenon, measured by how our community reacts to a performance, it’s impossible to understand the phenomenon of success without also observing the network it takes place within. But networks are singularly important in areas like art, where performance and quality are hard to measure. In fact, an interconnected web of relationships determines success in art to a degree that even I, a network scientist, find stunning. How do networks perform this predictive magic? How do we create value when there is none?
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Today, many art historians consider Fountain to be the single most important work of modern art. To give you a sense of its importance, Dimitri Daskalopoulos, a Greek collector, forked over almost $2 million in 1997, and not even for the discarded original. He paid that sum for one of seventeen replicas released fifty years later by Duchamp’s dealer. “For me,” Daskalopoulos said, “it represents the origins of contemporary art.” I agree with him. You could argue that Fountain is a deeply serious practical joke or an irreverent work of serious art. It’s probably both. It’s also, of course, just an ordinary, factory-made urinal, nothing more, nothing less. The object becomes art not because it’s handmade or aesthetically pleasing but because it embodies an idea.
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There’s the painting of Christ by Leonardo da Vinci, one of only twenty or so paintings attributed to him, which in 2017 sold for a record-shattering $450 million. The last time it had changed ownership was in 2005, when a consortium of art dealers purchased it . . . for less than $10,000. What explains the Everest-sized leap in its value? Back in 2005 it was thought to have been painted by one of da Vinci’s disciples, not by the master himself. It’s the same painting, just as remarkable or unremarkable as it ever was. Nothing changed but its context.
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Open up any art history book, and you’re bound to find pages justifying its reign over the art world: the subject’s enigmatic smile, the unique techniques da Vinci employed, the painting’s robust composition. The truth is, though, that up until a century ago, the Mona Lisa was just one of many valuable paintings at the Louvre. It became a household name only after it was stolen in broad daylight in 1911, creating an international hunt for the thief. It turned into a worldwide mystery playing out in major cities, like New York, Paris, and Rome, trailed by stranger-than-fiction anecdotes—at one time Picasso was wrongfully arrested as an accomplice in the crime. The drama that surrounded the Mona Lisa’s two-year disappearance is what made the painting such a quintessential treasure. If it were ever to sell, it’s estimated that it would fetch an unheard-of $1.5 billion. If art has no inherent value, then where does that $1.5 billion price tag come from? Networks.
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Multiple dependencies govern how the art world places value on an individual work. Artists derive prestige from their affiliations with specific galleries and museums; in turn, the prestige of these institutions stems from the perceived importance of the artists they represent and exhibit. In other words, there’s a symbiotic relationship between artists and institutions, and it’s based on little more than mutual belief in one another.
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The Magnus data set offered us a way to observe and analyze the millions of tacit transactions that determine the emergence of influence in art. By reconstructing the exhibition histories of half a million artists, we could unveil the network that offered access to coveted institutions. We did so by mapping the unseen links that shape how artists move among galleries and museums. Two institutions—say, Museum A and Gallery B—are linked if an artist who exhibited at Museum A moves next to Gallery B. Why is this a meaningful way to link institutions? Because curators look to one another to validate their decisions. If a gallery sees that an artist is exhibited by other institutions whose instincts it trusts, it’s far more likely to take that artist on. So moving your artwork from A to B is not merely a transaction. It’s preceded by a lot of research, consideration, and valuation on the part of gallerists and curators.
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The outcome of our effort was a map that captured how art moves around the world. There were a few major hubs, which represented the few institutions that were linked to an exceptional number of other institutions. The network’s hubs were, without exception, the art world’s most influential galleries and museums—New York’s MoMA, Guggenheim, and Gagosian Gallery, trailed closely by the Pace Gallery, Metropolitan Museum of Art, Art Institute of Chicago, and National Gallery of Art in Washington, D.C.—all American exhibition spaces. These were densely linked to European institutions like the Tate, Centre Pompidou, and Reina Sofia. If your work is exhibited at one of these hubs, it’s as if you hop onto a merry-go-round of success, looping around and around to other major institutions with ease. Your sales and the skyrocketing price tags on your work also become a foregone conclusion. These hubs are the conduits of artistic success. By showing at major galleries or museums, you’re guaranteed to be a superstar in the art world.
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And if no one’s interested in buying your art? In a normal economy, galleries would drop the prices on your work, hoping to recoup some costs. Not in art. You never see a “30 Percent Off Sale” sign hanging in the window of a gallery, or “EVERYTHING MUST GO” painted across a display window. Galleries will dig their heels in and simply hang on to pieces they can’t sell. It’s a pyramid scheme that pushes prices in only one direction. It works because there’s no meter to gauge the quality of any given piece, no objective way to measure an object’s inherent worth. The emperor is never without some article of clothing. Forget talent, creativity, or aesthetics. Insiders shrug those attributes off as quickly as they forgot the The Man with the Golden Helmet and his beautifully downcast eyes. Value in art is in the network. Any work, from the Mona Lisa to Basquiat’s Untitled, is garage sale material without it.
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While the art world may lack performance metrics, it doesn’t lack order. In fact, the patterns that characterize the art market allowed us to do something that even I didn’t think would be possible at the start of the project. Like fortune-tellers, we could predict the fate of almost any artist whether she was starting out at the periphery of the network or at its heart. If we used her first five exhibits as input, the patterns of where she would show next were so predictive that we could map out her trajectory decades into the future. In our simulations, just as in the real data, elite artists continued to exhibit at high-prestige institutions. For artists starting at the periphery, success was largely local and painfully incremental.
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Networks brim with opportunity, partially because they’re held together by powerful hubs, people who are, well . . . really good at networking. These connectors are eager to utilize their relationships to support people and causes they find value in. They’re especially good at seeing opportunities in the social fabric that other people miss. Connect with them.
The Second Law: Performance is bounded, but success is unbounded.
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THE SECOND LAW Performance is bounded, but success is unbounded. The Second Law explains the hidden factors that shape our choices. It tells us why experts are doomed to failure when they try to select the best wines or the most accomplished violinists. The law explains why Tiger Woods’s competitors play measurably worse when he’s on the green and why the last interviewee almost always gets the job.
4 How Much Is a Bottle of Wine Worth?
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Hodgson was curious: Just how accurate and consistent were judges at detecting superior bottles? Since he served on the competition’s advisory board, he asked his fellow board members to let him conduct an experiment. When the 2005 panel convened for that year’s round of judging, on the surface everyone followed the same, time-honored protocols: evaluating each wine’s sweetness, acidity, tannins, fruit, and body; taking thorough notes; sniffing, sipping, swishing, spitting. But there was something different about the day’s deliberations. For the purpose of Hodgson’s experiment, the judges were tasting the same wines repeatedly. Meaning that over the course of a tasting, they encountered the same wines served back to them three times in random order. Suddenly, the flaws in the system—inconsistencies that Hodgson had previously only suspected—became glaringly apparent. One judge gave an 80, the lowest grade, to a wine in the first sipping. A moment later, tasting it again, he assigned it a decent 90. When presented with the same wine a third time, it didn’t ring a bell, so he awarded it a 96, a rating worthy of a gold award. “They scored the identical wines as if they were different,” Hodgson recalled. He concluded that chance has a great deal to do with the awards that wines win.
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The problem Hodgson detected stems from a simple issue. The wines that judges encounter in competition are, generally speaking, exceptional performers. And that’s precisely the case in most competitions. Usain Bolt and Hodgson’s wine competitions both provide evidence that performance is bounded.
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Does the bounded nature of performance mean that all wine competitions are flawed? Hodgson’s data certainly suggests so. After repeating his experiment for four consecutive years, he came to the startling conclusion that any given judge was consistent in scoring the same wine only 18 percent of the time.
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Chia-Jung Tsay, a researcher at University College London, asked both professional musicians and novices to predict which of three finalists would win a classical music competition. One group listened only to the audio of each performance. Another was given both audio and video. Finally, some were shown only the video without the sound, a ridiculous proposition if you’re asking them to select the best musician. Prior to the experiment, both experts and novices felt strongly that the audio alone would offer the best chance at predicting the winner. This was, after all, a music competition. But Tsay found that the groups that relied on the sound only could select the winner from among the musicians just about 25 percent of the time. Given that there were only three choices, that’s worse than if you were guessing on a multiple-choice test! Both the experts and novices relying on the sound disagreed with the jury, picking someone else for the top spot. Surprisingly, the group best able to pinpoint the winner consisted of those who watched the video with the sound off, making their choice among performers who were passionately executing music that couldn’t be heard. In that group, novices and experts alike guessed correctly about 50 percent of the time. In other words, those who couldn’t actually hear the music did twice as well as those who did. The experts did no better than the novices at identifying the most deserving performer, and in some cases they did worse. Now, stop for a second and absorb what that means: The original, expert jurists must have also selected a winner based on what they saw and not what they heard. If we follow the data, we arrive at the astounding conclusion that the Lang Langs of the music world are revered, but it’s not because they produce markedly better music than their competitors, the performers who don’t draw a sold-out crowd. They’re revered because they’re excellent musicians who also look the part.
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No competition, regardless of its prestige, is immune to these biases. Take, for example, the Queen Elisabeth International Music Competition, which is to classical music what American Idol is to pop music. The contest has been making stars since 1937, first in violin, then in piano, voice, cello, and composition. The violin prize consists not only of a large check but also a four-year loan of a coveted Stradivarius. Most important, it brings prestige, opening doors to elite concert halls and lucrative recording contracts around the world. The competition has a long reputation for fairness, observing a number of protocols to prevent bias. Each year, eighty-five hopeful performers are invited to compete from all over the world. Once they arrive in Brussels, they’re whittled down to twelve finalists who are then given the same concerto, composed specifically for the competition. Asking everyone to play the same new score ensures that no one can win on the strength of her personal selection honed over time. Additionally, a random draw determines when finalists perform, and distribution of the concerto is staggered, ensuring that musicians have exactly a week to practice prior to their final performance. Nightly throughout the week of the finals, two candidates play for the judges during assigned slots and are evaluated on the spot. Judges can’t change their scores after they submit them, and they don’t confer during the evaluation process. In its attention to detail, the competition is the best attempt by classical music to select and reward the most talented performers.
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But two economists put the competition under their statistical microscope and came to the resounding conclusion that chance couldn’t explain this outcome. Instead, those who were asked to perform during the first day of finals indeed had a much lower probability of winning, systematically ranking almost three positions below those who performed on the fifth day. The order during a given night also mattered. Those who performed second tended to be ranked one position higher than the opening act. Gender also played a role. All things being, as it were, equal, men were routinely ranked about two positions above women. A female performer who opens the finals will be ranked about six positions lower than a male performer with identical talent who performs second on the fifth day.
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But no matter where we look, the same flaw in protocol—researchers call it an “immediacy bias”—effectively determines competition outcomes. We saw it in the Queen Elisabeth competition. The later performers, those with the highest immediacy in our brains, come out ahead. This is also true of Europe’s famous, long-standing pop song competition, the Eurovision Song Contest. The later a singer performs in the evening, the higher the chances that he or she will take home the trophy. And it’s true in figure skating, where the skaters perform in random order during the first round, sliding out onto the ice hopefully and, one by one, executing dances that include maneuvers or figures similar to those of competitors. Laden with bouquets and waving regally from the sidelines, they are judged immediately after their performances. We watch them on TV as they wait in suspense. When they hear each judge’s verdict booming from the intercom, their faces relax into relieved laughter or grimace in pain. The camera briefly showcases the drama. Then it pans to the next skater in a spangled leotard who poses in the center of the ice. It seems transparent and fair, but it isn’t. Scores systematically increase according to a skater’s position in the roster. Those who compete later appear—miraculously, consistently—to skate much better. Once again, fates are determined by the order of performances.
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Another example I find particularly startling is the manner in which aspiring courtroom judges in Spain are evaluated. Many hopeful, well-prepared applicants appear before a panel of experienced justices, to be quizzed in three categories: “general culture,” “languages,” and “history, law, culture, and economy.” It’s not hard to imagine the anxiety that taking an oral exam before an array of revered experts might induce, especially when your career hinges on the results. It’s the stuff of cold-sweat nightmares: authority figures peering down, asking probing questions on a wide range of topics. All you can do is cross your fingers and rack your brain. But in this case the moment of truth doesn’t come in the hot seat. Rather, your chance of success is predetermined several weeks prior to the exam. That’s because if you happen to draw a Monday slot—and you could draw a slot on any day of the week—the deck is already stacked against you. As a brave, early-week pioneer, you have about a 50 percent chance of passing your law exam. Whereas if you draw a Friday slot? Lucky you! You now have a roughly 75 percent chance of becoming a judge. The vast discrepancy in outcomes has little to do with your relative performance, which we assume would vary based on your knowledge, preparation, and expertise regardless of the day of the week. Isn’t that why we conduct the exams to begin with? And yet the judge deciding the verdict in Spain’s next major case may have benefited from the same immediacy bias witnessed in a slew of other contexts.
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When? Also Israeli judges granting parole
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But then another example sneaks up on me: the way the FDA approves new medical devices. At approval meetings, a chairperson seats people around a conference table according to her discretion. After initial presentations by the device manufacturer and two formal reviewers, the chairperson asks the committee member seated nearest to the reviewers to comment. The chairperson then guides the discussion, going clockwise or counterclockwise in the room, allowing each member to raise issues. In theory, this gives everyone a fair shot at voicing potential concerns. But it doesn’t. The device’s approval is typically determined by those who are asked to speak first. They’re the ones who get to frame the key questions. The later speakers are unable to raise new issues effectively; the concerns of the first speakers have been posed and set the tone. In other words, where people sit in a meeting, and the order in which they speak, can impact whether a medical device is approved for public use.
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Performers can’t all compete at the same time. By definition, judges almost always have to, as the phrase goes, use their judgment. But then we see—with FDA approval processes, judging protocols for figure-skating contests, qualifying exams, violin competitions, and wine tasting—that the mechanisms designed to ensure evenhanded competition in a range of fields often have the opposite effect.
5 Superstars and Power Laws
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Despite his talent, Woods, like the rest of the competition, has bounded performance. If he wins, he does so by a fraction of a swing, eked out by masterfully combining different skills. No matter what criteria we look at, he’s not that much better than his competitors. But while Woods’s performance is clearly bounded, his success is limitless. In 2009, Woods became the first athlete to net more than a billion dollars over the course of his career. That same year, he became the second-richest African-American, trailing only Oprah Winfrey. Even in 2015, when he no longer dominated golf, he was ranked ninth on the Forbes list of highest-paid athletes. An enormous amount of his wealth comes from endorsement deals, which run the gamut from golf paraphernalia to sports drinks to razors to cars. The five-year deal Woods negotiated with Nike in 2000 for $105 million was, at the time, the largest contract an athlete had ever signed. As part of the agreement, he received a percentage of the sales of Nike’s golf apparel and equipment. He was so central to the brand that for years he collected royalties on it, pocketing a profit with every swoosh-emblazoned fleece vest sold at a pro shop.
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Woods is what economists call a superstar, someone exceptionally rewarded for exceptional performance. Superstars exist because success is unbounded. By performing even a fraction better than your competitors, your reward can easily be hundreds, sometimes thousands of times greater. Economist Sherwin Rosen described superstars as “relatively small numbers of people who earn enormous amounts of money and dominate the activities in which they engage.” The obvious examples include movie stars, pop singers, high-profile CEOs, and investors. Think George Clooney, Jennifer Lawrence, Will Smith. Katy Perry, Lorde, Bruno Mars. Bill Gates, Richard Branson, Warren Buffet, George Soros.
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Steven Weinberg is the best-paid physics professor in history. As a physicist who developed a theory that unified electromagnetism and the weak forces affecting subatomic particles—the same concept that Einstein pursued unsuccessfully for much of his professional life—Weinberg’s contributions to science are stunning. His theory led to groundbreaking work by other researchers, including the discovery of the “God particle,” or the Higgs boson. As you might imagine, he was exceptionally rewarded for his exceptional performance. He earned a Harvard professorship and received the Nobel Prize in 1979. It turns out that Weinberg’s not just a whip-smart scientist; he’s also a skilled negotiator. The University of Texas at Austin attempted to woo him from Harvard in 1982, offering a salary that matched their president’s. Weinberg turned it down. Instead, he asked for pay on a par with the university’s football coach. This was Texas, remember, and Weinberg knew where the priorities lay. When he left the Ivy League and moved west, his salary did match the coach’s. In 1991, he made around a quarter of a million dollars. That was an unheard-of sum for an ivory-tower academic, especially at the time. As stunning as Weinberg’s salary was, though, it was only five times the average physics professor’s. Contrast that with today’s business world, where a typical CEO rakes in roughly 271 times the salary of his average employee. If Weinberg were a superstar by an economist’s definition, his income should have exceeded $200 million. Weinberg’s inability to rake in money at that egregious level helps us understand a key feature of superstardom. Exceptional reward only comes from talents that are easily and cheaply disseminated. As Rosen put it, “A performer or author must put out roughly the same effort whether ten or a thousand people show up in the audience or buy the book.” To be a superstar economically, in other words, your performance must scale. Think of the university’s football coach, a position that pulled in a quarter of a million dollars in 1991. His talents, which helped secure wins for the Longhorns, do scale: broadcasts of the games reach millions of fans. So when the popularity of college football exploded over the past two decades, the income of the coach also exploded, without any additional effort on his part. He became a superstar. Today, the head coach at the University of Texas earns over $5 million yearly, more than any other university employee, around twenty times what he made in 1991. Weinberg, who according to public records now earns in the neighborhood of $575,000, on the other hand, has merely doubled his income in the decades since his hire. More than half a million dollars is nothing to thumb your nose at. It’s an exceptional salary. But there’s a reason why Weinberg’s pay doesn’t mimic the football coach’s. He’s there to teach, and his salary is derived from tuition dollars. His classes aren’t taken by vast multitudes. In 2011, he began teaching an undergraduate course on the history of physics, from the Greeks to string theory, so an audience of maybe several hundred students, tops, benefits from his talent. He has no enthusiastic fans decked out in bright jerseys swarming his lectures, no one tailgating his presentations, no one cheerleading from the sidelines during his classes. If we’re measuring Weinberg’s success in economic terms, his impact just doesn’t scale. Yet Weinberg’s case perfectly illustrates how limited the economic definition of superstardom can be. The truth is, Weinberg is a superstar if the metric we use is scientific impact. That’s a currency, like dollars, that can actually be measured. Take his paper that introduced electroweak interaction and, decades later, earned him the Nobel Prize. It inspired thousands of other papers, spawning related groundbreaking work and pushing the discipline forward. We know this because it was cited 14,000 times by other scientists, implying that 14,000 research papers were influenced by Weinberg’s work. Because citations in science also conform to power laws, measuring success in science isn’t much different from doing so in business. Success is unbounded in academia as well. But our success metric isn’t money; it’s scientific impact, measured in citations. Just as most wage earners don’t rake in astounding sums, most papers—however laboriously and passionately researched—get few or no citations. That means that the majority of research projects fly almost completely under the radar. Papers like Weinberg’s are the rare exceptions, earning outsized scientific attention and superstar status. Such outliers remind us that academic success is like success in any other realm. It sees no boundaries. Citations, then, serve as currency in the scientific world. I don’t mean that as a figure of speech. Rather, it’s possible to put actual monetary value on each citation a paper receives. We can, in other words, calculate exactly how much a single citation is worth. Any guesses? Shockingly, in the United States each citation is worth a whopping $100,000. We know this by looking at the amount of money the nation spends on research in hopes of furthering a variety of scientific causes, from medical breakthroughs to innovative products, or new insights on the origins of our universe. If we then divide that figure by the total number of citations collectively generated by the papers these funds paid for, we can estimate the cost of a single citation. With that we can translate Weinberg’s superstardom into numbers even an economist can understand. His seminal paper, which has received 14,000 citations over the years, has had a scientific impact worth an extraordinary $1.4 billion! That is the approximate total cost of all the research inspired by Weinberg’s 1967 discovery.
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To reap enormous reward, the product you provide must be easily reproduced. Sherwin Rosen, as he was developing his economic theory of superstardom in 1981, predicted that advances in technology would further exaggerate the superstar phenomenon by allowing a few performers to access an even larger audience. These looming figures would, at the same time, reduce the reach of their less visible peers. The already wide gap between the band playing in the local bar and the band playing on TV would widen further. Rosen was right. In 1982, when Timberlake was just another infant mewling in a crib somewhere, the top 1 percent of pop stars took home roughly a quarter of concert ticket revenue. Now Timberlake and his fellow top twenty hit makers rake in more than half of that purse. Not only do people spend more on entertainment than in previous decades, the rise of MTV, DVD players, MP3s, and Internet streaming during Timberlake’s lifetime has expanded his audience and rewards. And as he and his cohort of outsized names and personalities—Taylor Swift, Justin Bieber, Lady Gaga among them—take up more room in our collective eardrums, the would-be up-and-comers share less and less.
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The math behind the unbounded nature of success is cemented in inequality. In the collective consciousness, superstars belong to a separate society peopled by the rare and lucky. They tower over us, untouchable. And when we do find ourselves in their presence, we handle them with kid gloves. If we happen to see them in a coffee shop or bump into them on the street, we make note of the occurrence as if we’d witnessed a miracle. We name-drop our association to them. And as we angle for their attention, we change our own behavior in subtle ways, which affects our ability to succeed.
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In order to qualify for tournaments, golfers have to make the cut in the first round. Highly ranked players’ first-round scores were, on average, 0.6 stroke higher (meaning weaker playing) when Woods was present than they were when he was absent. Given that first-round scores often vary only slightly among top golfers, this alone was a striking finding. But the effect only got stronger in regular and major tournaments. Golfers competing against Woods tallied final scores between 0.7 and 1.3 strokes higher. Since fewer than two strokes often separate first-and second-place golfers, the effect essentially determined the winner. The outcome was so striking it even has a name—the Tiger Woods effect—a phrase that captures how our performance suffers in the presence of superstars.
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When Woods was recovering from surgery, his top-ranked competitors were playing not just subtly but remarkably better: 4.6 strokes better on average. This time, even lower-ranked and unranked players improved. And when Woods took a break to get his personal life back on track, his fellow golfers’ tournament scores were roughly 3.5 strokes better.
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Yes, healthy competition is good; it improves our performance. But competing against superstars is a whole different story. How often have we stumbled in the presence of a hero or mentor, our confidence imploding like a tire meeting a nail? How often do we defer to the judgment of superiors, convinced their wisdom and competence usurps ours? And how often do we let admiration interfere with objective assessment of our abilities? Our intimidation in the presence of luminaries is so deeply embedded that we probably don’t notice how much it skews outcomes in favor of those whose success is already rampant.
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individual team members to managers and hiring committees. Superstars suppress you if you compete against them, but they may boost you if you cooperate with them. For example, research shows that when a university hires a scientific superstar of Weinberg’s ilk, he improves the productivity of the entire department by an astonishing 54 percent. Strikingly, this is not just because of the outsized contributions of the superstar himself, which account for, on average, only about a quarter of the department’s total productivity increase. The remaining upswing is by others—an indirect result of his or her presence.
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That’s good news for the rest of us. If we know that performance is bounded, we can remind ourselves that we’re capable of besting superstars. The subtle psychological factors that diminish our performance in the presence of luminaries are less powerful if we’re aware of them and if we remember that superstars are just as capable of failure as the rest of us. In moments of intimidation, we can bring our heroes down to our level.
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One of the most fascinating aspects of Brown’s research is how clearly it showed scores in professional golf fluctuating during Tiger Woods’s “hot” and “cold” periods. When Woods was performing exceptionally well, his superstar effect loomed particularly large, influencing the scores of the top-ranked players by roughly two strokes. Conversely, when Woods experienced a cold period, his poor performance seemed to boost other players’ confidence. During these slumps, the adverse effect his presence usually caused disappeared. Suddenly Woods was fallible. It was no longer a foregone conclusion that he’d win.
The Third Law: Previous success x fitness = future success.
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THE THIRD LAW Previous success × fitness = future success. It’s the law that shows us how a subtle phenomenon, preferential attachment, governs all success, from a petition’s popularity to reading comprehension in children. When fitness and social influence work in tandem, success has no boundaries.
6 Exploding Kittens and Sock Puppets
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Preferential attachment is responsible for the kind of snowballing success experienced by superstars like Tiger Woods or Justin Timberlake, turning them into the hubs in their respective networks. If success were as bounded as performance, there’d be a limit to their fan base. But as Elan Lee’s deluge of donations confirms, there isn’t. The runaway popularity of Exploding Kittens reflected the unbounded nature of success captured in the Second Law. But the engine that makes these hubs or superstars is preferential attachment—success breeds success.
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Awards that Wikipedia calls “Barnstars” can be given to anyone by anyone who’s logged in as an editor. There are no judges deciding who deserves a prize. Because the Barnstars are meant to honor editors who are frequent and significant contributors, Arnout first identified the most active 1 percent of all editors, about twenty-four hundred altogether. Then he randomly selected two hundred of these dedicated Wiki nerds and split them, again randomly, into two groups of one hundred editors each. For all practical purposes, the two groups were indistinguishable, consisting of editors with equal commitment to Wikipedia. That is, until Arnout’s experiment began. Unknown to the editors, Arnout destined one group for success and let the other group flounder. He did so by arbitrarily awarding each person in one group a Barnstar. He withheld the awards from those in the second group. In essence, he created two parallel universes that differed in only one respect: one group was openly recognized for their contributions to the site, the other was not. Then Arnout sat on the sidelines and watched what happened. The prizewinners got really busy. In fact, their productivity in editing sites jumped by 60 percent compared to the non-starred control group. They were also more motivated. But perhaps the most striking finding was this: Twelve of those whom Arnout rewarded with a Barnstar were subsequently granted one or several more Barnstars by other Wiki users over the three months he monitored the groups. Compare that with only two editors in the control group who received further awards. The people who received a first, random Barnstar from Arnout became “awardable.” They were far more likely to receive a second or third one from somebody else. It’s tempting to reduce all of this to a simple reward dynamic. The Barnstar increases effort, which in turn brings success. Winning an award bestows confidence, teaches us how to win, enhances our recognition, and increases the resources that breed further success. But the beauty of Arnout’s experiments is that he was able to rule out this possibility. True, as a group the Barn-star recipients showed increased effort, but the twelve doubly or triply awarded editors exhibited no greater productivity than their fellow group members. Most important, the results told Arnout that it’s not talent, quality, or commitment that drives the accumulating pile of prizes to the already rewarded. By randomly selecting the groups, he avoided that possibility. The experiment took effort and talent out of the equation, demonstrating that regardless of performance, reward led to further reward. Success bred success. It was really as simple as that.
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When the Second Law’s dictates about performance make one contender difficult to distinguish from another, remember, factors such as “awardability” can drive our success. I can’t help but think that the judges of the Queen Elisabeth competition aren’t really that different from Arnout. After selecting twelve finalists, they pick one performer virtually at random and give him a prize, launching his or her trajectory of success. I’m not just speculating here. The same team of researchers that discovered the competition’s biases has proven how crucial winning is for a musician’s career. They did so by analyzing the number of recordings each performer makes in the years after the competition. They also checked who was listed in the British Gramophone Classical Catalogue, or the Diapason, its French counterpart, which signals that someone has made it in classical music. And they examined the critics’ perceptions of each musician’s post-competition accomplishments. The researchers also asked music critics to rank each participant on a scale of 0 to 4, encapsulating expert opinion about every competitor’s subsequent career. Their findings show that a high ranking in the Queen Elisabeth competition, and the early visibility that came with it, kick-started musicians’ success. Once that happened, preferential attachment started a chain reaction, pushing them to new heights. The musicians with high competition scores were more likely not only to get their work recorded but also to be listed in catalogues and earn the praise of critics. This of course makes sense if you believe that talent alone determined their competition ranking. But we know they all had talent; the performances of the finalists are virtually indistinguishable to the judges. It’s chance and bias, plain and simple, that shape the final ranking. Yet those who are singled out and rewarded, even arbitrarily, experience major, long-lasting benefits as a result of the preferential attachment. An awarded musician becomes “awardable.”
7 The Ear of the Beholder
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Kate Mills, publishing director at Orion Books in the UK, pulled The Cuckoo’s Calling from a stack of manuscripts gathering dust on her desk. The crime novel, written by a former military police officer named Robert Galbraith, had some compelling qualities—beautiful language, an assured voice, and an intriguing main character with a prosthetic leg. She perused the book, deemed it “perfectly decent, but quiet,” and promptly passed on it. Galbraith, however, eventually found a publisher willing to take a chance. When the book was released in April 2013, Geoffrey Wansell praised The Cuckoo’s Calling in the Daily Mail as an “auspicious debut.” Despite Wansell’s enthusiasm, though, the book didn’t have much luck developing a readership and initially sold only a paltry five hundred copies, hardly an omen of good things to come. But then a rumor started circulating that Galbraith had the same agent and editor as J. K. Rowling, of Harry Potter fame. Galbraith also had an uncanny ability for describing women’s clothes, an idiosyncrasy that raised eyebrows. The rumor prompted London’s Sunday Times to consult a computer scientist who did indeed detect puzzling linguistic similarities between Rowling’s Casual Vacancy and Galbraith’s debut. Backed into a corner, Rowling eventually admitted that the buzz was true. She—Britain’s most famous living author—was Robert Galbraith, the unheard-of military police officer whose name graced the glossy front cover. The next day, The Cuckoo’s Calling instantly became an international best seller.
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Success in the MusicLab became, in other words, a self-fulfilling prophecy, a term Robert Merton coined in 1948 to describe gaps in educational achievement. African-Americans, Latinx, and students of other underrepresented groups, he argued, were disadvantaged from the get-go by a “false definition of the situation evoking a new behavior that makes the originally false conception come true.”
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All the children from first to sixth grade were given what was called the Harvard Test of Inflected Acquisition, an official-sounding name, and each teacher received the names of the students in their classrooms who had scored in the top 20 percent on the test. These were the students expected to show special progress in the coming year. At the end of the school year, the children took the test again. Sure enough, the 20 percent for whom the test predicted the greatest intellectual growth did exceptionally well, improving their IQ scores far more than the children who did not make the list. The Harvard Test was an astounding success, accurately pinpointing the students who would excel. Except . . . there is no such a thing as the Harvard Test. Sure, the kids did take a test at the beginning of the year, but it was a standard IQ test. Most important, the researchers never used the results of the IQ test. Rather, the kids on the lists provided to the teachers were chosen entirely at random. The Harvard Test was bogus. But what wasn’t bogus was this: The 20 percent of first and second graders who’d been labeled “gifted” did indeed excel spectacularly on the IQ test they took at the end of the school year. By claiming that a child showed greater aptitude on an assessment test than his or her classmates, the researchers changed the teachers’ perception of that child’s abilities. The students had no clue of their gifted status and went about their school day as always. They raised their hands eagerly or stared off into space in the back of the classroom. They handed in homework or offered excuses. They loved school or hated it. But the perception of their hidden abilities, based on false test scores, created a self-fulfilling prophecy, resulting in higher teacher expectation.
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There are, however, prominent exceptions to this rule, and Google is one. When it arrived on the scene in 1997, most people were using search engines like Altavista and Yahoo’s Inktomi. Within three years, though, Google was far ahead. Its success was so swift and absolute that its brand became a verb. So we kept wondering—how did Google start from the bottom and defy the considerable early-bird advantage of its gigantic competitors? Looking more closely at the data, we soon discovered that there are many Google-like rampantly successful latecomers in any market, companies that acquire market share at an exceptional rate in spite of their relative youth. There’s Boeing, for example, an upstart that disrupted the airline industry. There’s Zantac, an ulcer drug that obliterated its competition. Sam Adams brought a microbrew taste to beer drinkers everywhere. How did each defy the first movers already turbocharged by preferential attachment? Well, it was simple, really. They found success because their products had unique, intrinsic qualities that helped them overcome the handicap of their obscurity. A search engine that pointed users to more relevant sites. A more efficient and reliable plane. A superior drug. A better-tasting beer.
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The model showed us that if two nodes have the same fitness, the older nodes still have an advantage, just as a doctor with decades in practice will garner more patients than an equally good newbie. Yet, if two nodes have the same visibility, their fitness difference alone will decide who will collect more links.
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The MusicLab experiment shows just how malleable our tastes are. That’s not to say that we can’t see the thoroughly lousy product in front of our noses, but when we’re faced with multiple “good enough” options, since quality is bounded, we tend to concede our own judgment in favor of the crowd’s. And once that happens, what becomes popular doesn’t necessarily reflect a product’s true superiority. And that’s where the real problem lies. I want an enjoyable book, not a merely popular one. I want a good hotel, not a crowded one. Yet, as a consumer scrolling through Amazon, Hotels.com, or any other forum that relies on public opinion to rank products, popularity is usually all I have to go on. In the end, popularity and excellence become hopelessly intermingled, one masking the other. Could we ever hope to disentangle the two?
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When songs were ordered by popularity, the teenagers downloaded about five thousand altogether. The same number of participants shown a list ranked by fitness, though, downloaded as many as seven thousand songs. That’s a 40 percent increase, reflecting just how compelling excellence is. Teenagers who encountered a list that was relevant to them were far more likely to click the download button than those shown the merely popular songs.
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His findings are deeply counterintuitive. The more ratings a product had, the more its final rating differed from its true fitness. That doesn’t really make sense, right? You’d think that as each new person offers an evaluation, he or she would balance out both angry voices and overly positive paid ones, pushing us closer to an honest “average” response. You know that game where kids are asked to guess how many M&M’s are in a jar? The more people we query, the closer we get to the true number—but only if you don’t tell the kids what others guessed. The same is true on Amazon. The more people who weigh in on the ratings, the less the result will reflect the true fitness of the product. It’s social influence all over again. You bought that coffeemaker, and you have your reservations. If you’re fair, it’s a three-star machine. You sit down to write your review, and you see only five stars before you. Fine; it’s really four stars, you decide. In an odd way, on Amazon the consensus is irrelevant. Often, it’s the first reviews, unaffected by peers, that best capture a product’s true fitness.
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This conclusion makes complete sense if we take the MusicLab experiments at face value. After all, in each of the eight universes, a different Harry Potter–like runaway success floated to the top and reinforced the dictates of preferential attachment: it’s success that breeds success; fitness and performance are missing from the picture. The Third Law forces us to refine The Formula: when a product has fitness and previous success, its long-term success is determined by fitness alone. To put it in Darwinian terms, the fittest survives—the best song, the most reliable company, the superior technology. In other words, the Third Law echoes the First Law: when performance is measurable or discernible, it drives success. Working in tandem with popularity, fitness guides our choices in the long run.
The Fourth Law: While team success requires diversity and balance, a single individual will receive credit for the group’s achievements.
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THE FOURTH LAW While team success requires diversity and balance, a single individual will receive credit for the group’s achievements. It’s a law embodied by chickens and soccer players—who show us how all-star teams are bound to fail—and by jazz musicians, Broadway hit makers, and call-center representatives, who help us strategize to maximize group success. A van driver overlooked for a Nobel Prize serves as a cautionary tale: a lot can go wrong when we let the community assign credit.
8 Kind of Conventional, Kind of Innovative, Kind of Blue
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Most important, in the case of GitHub, Jim found that the degree to which leaders were engaged with their team played a key role in the team’s success. Those rare, runaway successes all had something in common, regardless of their programming aims: The more they were dominated by a single leader, the more successful they were.
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So Muir decided to use the most straightforward method he could think of: Take the best-laying hen from each cage, group them together, and breed them. In a few generations, he presumed, he’d have a coop full of super chickens sitting on mountains of eggs. He’d have, in other words, an all-star team. For the sake of comparison, Muir also identified his most productive cage. Not every chicken clucking away in this particular cage was a standout layer, but as a team they produced an impressive number of eggs. He put these hens in a cage side by side with his super chickens, and bred and rebred each group. The question was, of course, over time how much better would the super chickens do than the control group? So Muir let the hens breed for six generations—a standard procedure in animal science—and then, using the great-great-great-grandchildren of the original groups, he began tallying eggs. When Muir presented his results at a scientific conference for the first time, he started with the control group. Six generations in, they were thriving. The chickens were not only plump and healthy, their collective egg production had increased by 160 percent. The experiment, in other words, was already a success: Muir demonstrated that by isolating and selectively breeding his best accidental team, that is, his most productive cage, he could dramatically increase the number of eggs his chickens laid. It also meant that his super chickens, selected for individual productivity, had a really high bar to reach. When he got to the slides showing his superstar cage, though, the audience gasped. Six generations in, the descendants of his superstar hens didn’t look at all like superstars. They looked like they’d been through hell and back. For starters, out of nine hens, only three remained. The missing six had been murdered by their surviving cage mates. And the three survivors were certainly not thriving, either. They were missing most of their feathers. Their tails were bouquets of broken quills. The exposed skin on their wings was pocked with scars. The cage had become a war zone. As far as Muir’s experiment went, egg production was the least of their worries. Maimed and in distress from constant in-fighting, these hens didn’t lay eggs.
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Anita Williams Woolley, from Carnegie Mellon University, along with several colleagues at MIT, set out to answer a simple question: Could they measure the intelligence of a group of people working together? That is, can we measure “collective intelligence,” capturing a team’s ability to perform tasks as a whole? They asked triads of strangers to complete simple tasks together: Generate a list of uses for a brick. Plan a trip to a grocery store. Play video checkers. Researchers recorded each member’s actions as the groups searched for solutions. And their results debunked many expectations. For one, team members with high IQs didn’t do any better on collective intelligence tests than their lower-IQ peers. In fact, individual intelligence didn’t seem to matter much in the context of group performance. Neither did factors like the motivation level of group members or their individual satisfaction. What did matter was how the test takers communicated. First, teams tended to do well if individuals in the group had higher-than-average ability to read emotional cues. Second, groups where a few people dominated the conversation had a lower collective intelligence than those with more equality among group members. In other words, the best teams were those whose members shared discussion time and listened to one another. The third key factor was a fascinating offshoot of the other two: teams with female members had higher collective intelligence.
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My colleague Sandy Pentland, a researcher at MIT’s Media Lab, proved this unequivocally when, for six weeks, he turned a bank’s call center into his laboratory. In addition to their regular headsets, employees now donned Sandy’s specially designed electronic badges, which gathered information on everything from an employee’s tone of voice to how often he or she spoke. The content of the conversations wasn’t the point—in fact, it didn’t matter at all. What the badges captured were underlying patterns of communication that aren’t easy to measure otherwise. The data showed that face-to-face communication between team members mattered tremendously to team performance. We’re talking about the old-fashioned, informal kind of chitchat, when people make eye contact, speak animatedly, share stories, take the time to laugh or ask questions or listen. E-mails and quick announcements at the start of a shift might be “to the point,” but they provided little opportunity for discourse, gossip, and spontaneous problem solving. In fact, e-mail turned out to be the least valuable form of communication. It was too efficient. Chatting by the proverbial water-cooler—lost time from the manager’s perspective—is what really mattered. What looked like wasted time was actually employees doing important work, strengthening rapport through fluid communication. Odder still, given the strict edicts of middle school teachers everywhere, the research suggested that managers should encourage side chat and back-channel conversations during meetings. This helped build harmony between team members, helped people quickly clarify issues, and created space for creativity to flourish. Remember, this is a call center where we, the impatient callers-in, demand extreme efficiency. If a team could successfully shave even thirty seconds off every call, those of us on the other side of the line would greatly appreciate it. And the saved time would mean huge savings for the bank, too. So, on Sandy’s recommendation, the bank manager defied his industry’s practice of staggering breaks to maximize efficiency. Instead, he provided collective breaks hoping that, while sipping coffee and chatting, employees would increase the number of interactions they had with other team members, diversify whom they communicated with, and feed off one another’s energy and experience. The idea worked, spectacularly. These more “human” person-to-person moments improved the average call-handling time by 8 percent overall and by an astonishing 20 percent in lower-performing teams.
9 The Algorithm That Found the Overlooked Scientist
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Harry Truman once said, “It’s amazing how much can be accomplished if no one cares who gets the credit.” Shane Battier is certainly living proof of this assertion, demonstrating on the court just how far an unselfish and unassuming attitude can go. It’s apt, then, that Truman’s famous quote is also attributed to the UCLA basketball coach John Wooden, who understood well before Battier how valuable true team players were to the sport. But then again, maybe the idea actually belongs to Charles Montague, the English novelist, who wrote: “There is no limit to what a man can do as long as he cares not a straw who gets the credit for it.” Ironic that a quote about shared credit can be attributed to so many different people, huh?
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The mechanism behind Hua-Wei’s algorithm was simple: citation patterns—not only for the paper of interest but for all papers written by the coauthors—leave a trail of impact. We can map this trail to gauge a community’s perception of who deserves credit for a discovery. If a scientist was key to an insight, his prior work was likely connected to it. And having struck gold, he typically continued to explore ideas in the same vein. Hua-Wei and I found that if we traced the career path of each member of a scientific team, we could accurately pinpoint the presumptive “owner” of a given discovery, which was almost always the researcher with the most consistent track record in that area.
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For me, the most unexpected message behind the algorithm was this: when we allocate credit to the members of a team, who gets the credit has nothing to do with who actually did the work. We don’t dole out reward on the basis of who came up with the idea in the first place, who slaved for weeks, who showed up to meetings to graze the coffee and doughnuts, who jumped in at the last minute with a crucial suggestion, who had the eureka moment, or who yammered on and on but didn’t really contribute anything. The algorithm accurately selected Nobel Prize winners and handed them the win not by figuring out who did what. Rather, it did so by detecting how peers in the discipline paid attention to the work of some of the coauthors and ignored the work of the others. The predictive accuracy of the algorithm led us to our next insight about teams: Credit for teamwork isn’t based on performance. Credit is based on perception.
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Hoping that his work would not be in vain, Prasher mailed the cloned GFP gene—the product of his lonely toil—to two other researchers. The gesture was a genuinely friendly and unselfish one. The names scrawled on the padded envelopes belonged to the only scientists who had contacted him expressing interest in his research. Sixteen years later, the recipients of Prasher’s envelopes, Martin Chalfie and Roger Tsien, were in Stockholm accepting the Nobel Prize. Using the gene Prasher cloned, Chalfie had gone on to show the medical world that Prasher’s vision had been correct all along: you could indeed use GFP in living organisms. By inserting GFP into a roundworm, Chalfie made proteins fluoresce, a trick now used by thousands of biologists. This was what Prasher planned to do next, had he had the money and opportunity to pursue the research. Tsien took Prasher’s gene and mutated it, creating new strains of GFP that glowed in an array of vivid colors. And so, by utilizing Prasher’s discovery in innovative ways, the inheritors of his gene published a series of groundbreaking papers about its application, slowly stepping into the role of “discoverers.” With Prasher gone, they became the face of GFP.
The Fifth Law: With persistence success can come at any time.
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THE FIFTH LAW With persistence success can come at any time. The Fifth Law explains how it’s possible to do Nobel-winning research after retirement and why it feels like some people are playing the success game with loaded dice. We’ll encounter the Q-factor, which allows us to reduce innovation to an equation. The Fifth Law tells us that while success melts like a snowflake, creativity has no expiration date.
10 Einstein’s Error
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as the data tells us that scientists tend to publish their breakthrough work at the beginning of their careers. Studies of recognized geniuses show this. Psychologist Dean Keith Simonton, for instance, analyzed the career trajectories of more than two thousand scientists and inventors from ancient times to today, everyone from da Vinci to Newton to Edison. Most of them made their mark on history before or around the age of thirty-nine, he found, evidence that underscores the widely held assumption that creativity is the purview of youth, or at least early middle age. And when Simonton turned to the careers of artists and writers, he found that they, too, experienced early primes. Regardless of field or genre, innovation, the fuel that gets me out of bed and into the lab each day, appears to be less potent for those of us who, being creakier and more jaded, probably need it most.
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As fascinating as Simonton’s studies are, I have a fundamental problem with them. His work focuses on geniuses, a tiny segment of the creative population—admirable, exalted people who, come to think of it, are pretty rare birds.
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Sometimes simple questions are the hardest to answer. Ours was like that. It required us to reconstruct accurately the careers of tens of thousands of researchers, determining which papers belonged to which scientist from a list of roughly 40 million publications. The process took about two years, with quite a bit of help from Pierre Deville, a computer scientist working on our team. But when we were finally done and could analyze each individual career, a consistent pattern emerged. Successful research typically came relatively early in a career—within the first two decades after starting out in the field. To be precise, it appeared that a scientist had roughly a 13 percent chance of publishing her highest-impact work in the first three years of her career. And about the same odds held true for the following three years. In fact, each year for two decades, she had a similar chance of hitting the jackpot. But, after twenty years, something changed, and her odds sank dramatically. The chance she’d publish her most-cited paper in year twenty-five was only 5 percent. And her odds continued to free-fall after that. I’m nearing the thirtieth year of my career—and according to our graph, the likelihood I’ll now make a discovery that will overshadow my previous “best”? Less than 1 percent. In other words, I might as well stop trying. A quick glance at the data tells me I’m dead-wood. Forget tenure; my provost should send me off into the sunset. So Simonton was right, and his findings do apply to nongeniuses, too, those of us who follow our love of science and keep at it, day in and day out, without expecting accolades. What we found was simple: When it comes to their patterns of creativity, geniuses are no different from us. We, too, peak out early in our careers. We, too, let our guard down once that wave of creativity wanes. Geniuses or not, we mostly conform to the same fundamental patterns.
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Instead of tagging each paper with the age of the scientist who authored it, we simply labeled each paper as the first, second, or twentieth in a career. By doing this, we saw each paper for what it was: another in a series of attempts at a breakthrough. We expected to see that a scientist’s highest-impact paper would be among the first of her career. That’s what decades of research on geniuses insinuated. To our bafflement, though, this wasn’t the case. Rather, each paper—whether it was the first, the second, or the last on the list—had exactly the same chance of being her most important. When we saw the data arranged this way, we were stunned. Age didn’t seem to matter. Which created another puzzle. If my creativity has no age, and each of my papers has the same chance of being a breakthrough, then why do all of us—geniuses and everyday Joes and Janes—peak out early in our careers? Productivity. There’s a simple analogy to explain this seemingly contradictory find. Let’s say that for thirty years you buy a lottery ticket annually, always on your birthday. Your chance of winning a prize doesn’t improve as you age. Nor does it decline. It’s the same now as it was five years ago, and it will be the same ten years from now. But if you buy thirty lottery tickets on your thirtieth birthday? Well, if you’re ever going to win the lottery, the odds are it’ll be in your thirtieth year. Our measurements showed that research papers are like lottery tickets in a scientist’s life. Each has exactly the same odds of becoming a breakthrough. So, in the period when a researcher publishes at his or her best pace—finishing project after project in rapid-fire succession—they tend to experience their greatest success. Not because they’re more creative during this burst of activity. They succeed because they try more often.
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New projects always start with an idea. It’s the same no matter what creative field you’re in: a light bulb goes off, and you start thinking about how to bring your bright idea into the world. But the inherent importance or novelty of the idea isn’t something we always know in advance. So let’s call it r for “random idea,” letting r stand for a number that captures its value. Opening another fast-food joint in a strip mall where five are already fighting for survival? Give it an r close to zero. Building a working teleportation machine? That could have a huge r . . . if you can pull it off. Of course, the better the idea—that is, the larger its r value—the more likely it is to have a strong impact. But a good starting seed isn’t the only factor. Ideas are cheap, a truism often parroted by venture capitalists. Your ability to take that idea and turn it into a useful product determines the size of the check an investor is willing to cut for you. The same is true in any occupation: A terrific idea in clumsy hands rarely leads to an important outcome. Your ability to turn an idea into a discovery is equally important, and that varies dramatically from person to person. We called this ability a person’s Q-factor, which allowed us to reduce the process of innovation down to an equation. Each of us takes a random idea, with value r, and using our skill, we turn it into a discovery or “success” S, which captures its impact on the world. If we want to predict this impact, we need to establish how these two factors—the as-yet-unknown merit of the idea, or its r, and one’s Q-factor—work in tandem to determine a project’s ultimate success, or S. The simplest model we could think of also ended up being the most accurate. Multiply your Q-factor by the value of your next idea, r, and you get a formula to predict its success. Or, written as a formula: S=Qr In other words, the success of a product or a deal, or the impact of a discovery, will be the product of a creator’s Q-factor and the value of idea r.
Conclusion
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If my success is determined by the collective and not by individuals, then it’s tempting to assume that my performance, talent, or passion doesn’t matter. All I need to do is reach the right people and tell them exactly what they want to hear, and my success will snowball. But as the Third Law reminds us, that approach will only get you so far. If you want to win over the long term, performance is unavoidable. Your product needs to be highly fit and competitive. You may outsmart your competitors with marketing, or be such a good networker that people fail to notice your questionable performance. But the real engine of success is the Third Law—fitness times preferential attachment. The two multiply, building on each other. Hoping for success with only one of those two factors is like multiplying any number by zero. It gets you exactly . . . nothing.
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People came in droves to meet the hero of Zionism. The physicist accompanying him didn’t even register. We know this thanks to the Jewish press, which also covered the event on their front pages. The Forward’s headline proclaimed, “Great Parade for the Zionist Delegates in New York.” The article describes the same parade that the New York Times and the Washington Post also covered as headline news—except that the Forward and the rest of the Jewish press knew precisely why the crowd had gathered. Not only was Einstein absent from the headline, he was only mentioned in passing in the article itself, as a member of Weizmann’s entourage. Compare that with the opening line of the Washington Post article: “Several thousand persons gathered on the pier today to greet Prof. Albert Einstein, noted scientist and originator of the Einstein theory of relativity.” While the article is quick to note that Einstein “landed here as a member of a party of distinguished Jews,” it places about as much emphasis on the other arrivals as it does on Mrs. Einstein’s response to the theory of relativity. Only after quoting her—“He has told me the theory a great many times, but it is all dim to me now”—does the write-up finally reveal, in a perfunctory nod, that Weizmann headed the Zionist delegation. By erroneously handing Einstein the headlines, the gentile press did for Einstein what Arnout did for Kickstarter projects and Wikipedia editors—they gave him a huge kick, putting him on a path to success. From that day forward, Einstein was a celebrity, drawing crowds everywhere he went.
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Einstein’s narrative encapsulates the Laws of Success discussed in this book to an almost eerie degree. Our definition of success—that success isn’t about you, it’s about us? That’s absolutely the case for Einstein, whose outsized recognition beyond the scientific community has a lot more to do with a left-field community’s response than his performance. The First Law—Performance drives success, but when performance is immeasurable, networks determine success? Einstein had already distinguished himself as a remarkable scientist, which was why the journalists were eager to interview him in the first place. But it was Einstein’s network, his links to causes and key hubs outside the scientific community, that put him on that steamship to begin with. That network is largely invisible in the success story we commonly tell about Einstein. The Second Law—Performance is bounded, but success is unbounded? As much as we lionize Einstein, several scientists—Newton, Bohr, Planck, Heisenberg—had just as deep an impact on physics as Einstein. His outsized presence in our collective memory is hard to justify based on performance alone. His unbounded success, however, goes without saying. Just show a picture of Einstein to anyone on the street. The Third Law—Previous success × fitness = future success? Both of these factors played a role in Einstein’s narrative. By handing him the headlines, the journalists provided the nudge that set his snowballing, widespread success in motion. But Einstein’s excellence as a physicist was essential to the equation. And while the first part of the Fourth Law, which says that team success requires diversity and balance, doesn’t appear to apply to Einstein, since most of his papers were solo authored, the second half certainly does. Credit isn’t about performance, it’s about perception? Einstein’s story hinges on a misattribution. Those crowds of Zionists were credited to Einstein not because he was the most important advocate of the cause. He was, at best, a bit player. But because he was the most widely recognizable person in the delegation to the gentile press, he got the headlines. And finally, the Fifth Law, which says that success can come at any time as long as we’re persistent? Remember Einstein’s late-career paper about quantum entanglement? It was ultimately his most cited contribution to science, surpassing the impact of each of the five papers he wrote in 1905, a miracle year when he produced such blockbuster discoveries as the theory of relativity and quantum mechanics. And he never stopped trying, churning out idea after idea, paper after paper, until his death. That’s a reminder of just how far persistence can take us when it’s combined with a high Q-factor.