Range — Book Review & Quotes
Quotes
The push to focus early and narrowly extends well beyond sports. We are often taught that the more competitive and complicated the world gets, the more specialized we all must become (and the earlier we must start) to navigate it. Our best-known icons of success are elevated for their precocity and their head starts — Mozart at the keyboard, Facebook CEO Mark Zuckerberg at the other kind of keyboard. The response, in every field, to a ballooning library of human knowledge and an interconnected world has been to exalt increasingly narrow focus. Oncologists no longer specialize in cancer, but rather in cancer related to a single organ, and the trend advances each year. Surgeon and writer Atul Gawande pointed out that when doctors joke about left ear surgeons, “we have to check to be sure they don’t exist.”
Eventual elites typically devote less time early on to deliberate practice in the activity in which they will eventually become experts. Instead, they undergo what researchers call a “sampling period.” They play a variety of sports, usually in an unstructured or lightly structured environment; they gain a range of physical proficiencies from which they can draw; they learn about their own abilities and proclivities; and only later do they focus in and ramp up technical practice in one area.
One study showed that early career specializers jumped out to an earnings lead after college, but that later specializers made up for the head start by finding work that better fit their skills and personalities.
They were all scholarship recipients, former paratroopers and translators who were becoming teachers, scientists, engineers, and entrepreneurs. They brimmed with enthusiasm, but rippled with an undercurrent of fear. Their LinkedIn profiles didn’t show the linear progression toward a particular career they had been told employers wanted. They were anxious starting grad school alongside younger (sometimes much younger) students, or changing lanes later than their peers, all because they had been busy accumulating inimitable life and leadership experiences. Somehow, a unique advantage had morphed in their heads into a liability.
I dove into work showing that highly credentialed experts can become so narrow-minded that they actually get worse with experience, even while becoming more confident — a dangerous combination. And I was stunned when cognitive psychologists I spoke with led me to an enormous and too often ignored body of work demonstrating that learning itself is best done slowly to accumulate lasting knowledge, even when that means performing poorly on tests of immediate progress. That is, the most effective learning looks inefficient; it looks like falling behind.
Overspecialization can lead to collective tragedy even when every individual separately takes the most reasonable course of action.
The challenge we all face is how to maintain the benefits of breadth, diverse experience, interdisciplinary thinking, and delayed concentration in a world that increasingly incentivizes, even demands, hyperspecialization.
Laszlo grew up determined to have a family, and a special one. He prepped for fatherhood in college by poring over biographies of legendary thinkers, from Socrates to Einstein. He decided that traditional education was broken, and that he could make his own children into geniuses, if he just gave them the right head start. By doing so, he would prove something far greater: that any child can be molded for eminence in any discipline. He just needed a wife who would go along with the plan.
By six, Susan could read and write and was years ahead of her grade peers in math. Laszlo and Klara decided they would educate her at home and keep the day open for chess. The Hungarian police threatened to throw Laszlo in jail if he did not send his daughter to the compulsory school system. It took him months of lobbying the Ministry of Education to gain permission. Susan’s new little sister, Sofia, would be homeschooled too, as would Judit, who was coming soon, and whom Laszlo and Klara almost named Zseni, Hungarian for “genius.” All three became part of the grand experiment.”
Psychologist Gary Klein is a pioneer of the “naturalistic decision making” (NDM) model of expertise; NDM researchers observe expert performers in their natural course of work to learn how they make high-stakes decisions under time pressure. Klein has shown that experts in an array of fields are remarkably similar to chess masters in that they instinctively recognize familiar patterns.
One of Klein’s colleagues, psychologist Daniel Kahneman, studied human decision making from the “heuristics and biases” model of human judgment. His findings could hardly have been more different from Klein’s. When Kahneman probed the judgments of highly trained experts, he often found that experience had not helped at all. Even worse, it frequently bred confidence but not skill.
Around that same time, an influential book on expert judgment was published that Kahneman told me impressed him “enormously.” It was a wide-ranging review of research that rocked psychology because it showed experience simply did not create skill in a wide range of real-world scenarios, from college administrators assessing student potential to psychiatrists predicting patient performance to human resources professionals deciding who will succeed in job training. In those domains, which involved human behavior and where patterns did not clearly repeat, repetition did not cause learning. Chess, golf, and firefighting are exceptions, not the rule.
In 2009, Kahneman and Klein took the unusual step of coauthoring a paper in which they laid out their views and sought common ground. And they found it. Whether or not experience inevitably led to expertise, they agreed, depended entirely on the domain in question. Narrow experience made for better chess and poker players and firefighters, but not for better predictors of financial or political trends, or of how employees or patients would perform.
In wicked domains, the rules of the game are often unclear or incomplete, there may or may not be repetitive patterns and they may not be obvious, and feedback is often delayed, inaccurate, or both.
“Anything we can do, and we know how to do it, machines will do it better,” he said at a recent lecture. “If we can codify it, and pass it to computers, they will do it better.” Still, losing to Deep Blue gave him an idea. In playing computers, he recognized what artificial intelligence scholars call Moravec’s paradox: machines and humans frequently have opposite strengths and weaknesses.
Kasparov concluded that the humans on the winning team were the best at “coaching” multiple computers on what to examine, and then synthesizing that information for an overall strategy.
In 2019, in a limited version of StarCraft, AI beat a pro for the first time. (The pro adapted and earned a win after a string of losses.) But the game’s strategic complexity provides a lesson: the bigger the picture, the more unique the potential human contribution. Our greatest strength is the exact opposite of narrow specialization. It is the ability to integrate broadly.
“AI systems are like savants.” They need stable structures and narrow worlds.
When we know the rules and answers, and they don’t change over time — chess, golf, playing classical music — an argument can be made for savant-like hyperspecialized practice from day one. But those are poor models of most things humans want to learn.
As psychologist Ellen Winner, one of the foremost authorities on gifted children, noted, no savant has ever been known to become a “Big-C creator,” who changed their field.
Compared to other scientists, Nobel laureates are at least twenty-two times more likely to partake as an amateur actor, dancer, magician, or other type of performer. Nationally recognized scientists are much more likely than other scientists to be musicians, sculptors, painters, printmakers, woodworkers, mechanics, electronics tinkerers, glassblowers, poets, or writers, of both fiction and nonfiction. And, again, Nobel laureates are far more likely still. The most successful experts also belong to the wider world.
Connolly’s primary finding was that early in their careers, those who later made successful transitions had broader training and kept multiple “career streams” open even as they pursued a primary specialty. They “traveled on an eight-lane highway,” he wrote, rather than down a single-lane one-way street. They had range. The successful adapters were excellent at taking knowledge from one pursuit and applying it creatively to another, and at avoiding cognitive entrenchment.
To use a common metaphor, premodern people miss the forest for the trees; modern people miss the trees for the forest.
Modern work demands knowledge transfer: the ability to apply knowledge to new situations and different domains. Our most fundamental thought processes have changed to accommodate increasing complexity and the need to derive new patterns rather than rely only on familiar ones. Our conceptual classification schemes provide a scaffolding for connecting knowledge, making it accessible and flexible.
As Arab historiographer Ibn Khaldun, considered a founder of sociology, pointed out centuries ago, a city dweller traveling through the desert will be completely dependent on a nomad to keep him alive. So long as they remain in the desert, the nomad is a genius.
Flynn was bemused to find that the correlation between the test of broad conceptual thinking and GPA was about zero. In Flynn’s words, “the traits that earn good grades at [the university] do not include critical ability of any broad significance.”*
None of the majors, including psychology, understood social science methods. Science students learned the facts of their specific field without understanding how science should work in order to draw true conclusions. Neuroscience majors did not do particularly well on anything. Business majors performed very poorly across the board, including in economics. Econ majors did the best overall. Economics is a broad field by nature, and econ professors have been shown to apply the reasoning principles they’ve learned to problems outside their area.*
Flynn’s conclusion: “There is no sign that any department attempts to develop [anything] other than narrow critical competence.”
The study he conducted at the state university convinced him that college departments rush to develop students in a narrow specialty area, while failing to sharpen the tools of thinking that can serve them in every area. This must change, he argues, if students are to capitalize on their unprecedented capacity for abstract thought. They must be taught to think before being taught what to think about.
As statistician Doug Altman put it, “Everyone is so busy doing research they don’t have time to stop and think about the way they’re doing it.”
“It’s easier for a jazz musician to learn to play classical literature than for a classical player to learn how to play jazz,” he said. “The jazz musician is a creative artist, the classical musician is a re-creative artist.”
In totality, the picture is in line with a classic research finding that is not specific to music: breadth of training predicts breadth of transfer. That is, the more contexts in which something is learned, the more the learner creates abstract models, and the less they rely on any particular example. Learners become better at applying their knowledge to a situation they’ve never seen before, which is the essence of creativity.
Rather than letting students grapple with some confusion, teachers often responded to their solicitations with hint-giving that morphed a making-connections problem into a using-procedures one.
“We’re very good, humans are, at trying to do the least amount of work that we have to in order to accomplish a task,” Richland told me. Soliciting hints toward a solution is both clever and expedient. The problem is that when it comes to learning concepts that can be broadly wielded, expedience can backfire.
In Japan, a little more than half of all problems were making-connections problems, and half of those stayed that way through the solving. An entire class period could be just one problem with many parts. When a student offered an idea for how to approach a problem, rather than engaging in multiple choice, the teacher had them come to the board and put a magnet with their name on it next to the idea. By the end of class, one problem on a blackboard the size of an entire wall served as a captain’s log of the class’s collective intellectual voyage, dead ends and all. Richland originally tried to label the videotaped lessons with a single topic of the day, “but we couldn’t do it with Japan,” she said, “because you could engage with these problems using so much different content.” (There is a specific Japanese word to describe chalkboard writing that tracks conceptual connections over the course of collective problem solving: bansho.)
If you are doing too well when you test yourself, the simple antidote is to wait longer before practicing the same material again, so that the test will be more difficult when you do. Frustration is not a sign you are not learning, but ease is.
Psychologist Robert Bjork first used the phrase “desirable difficulties” in 1994. Twenty years later, he and a coauthor concluded a book chapter on applying the science of learning like this: “Above all, the most basic message is that teachers and students must avoid interpreting current performance as learning. Good performance on a test during the learning process can indicate mastery, but learners and teachers need to be aware that such performance will often index, instead, fast but fleeting progress.”
Desirable difficulties like testing and spacing make knowledge stick. It becomes durable. Desirable difficulties like making connections and interleaving make knowledge flexible, useful for problems that never appeared in training. All slow down learning and make performance suffer, in the short term.
Teaching kids to read a little early is not a lasting advantage. Teaching them how to hunt for and connect contextual clues to understand what they read can be. As with all desirable difficulties, the trouble is that a head start comes fast, but deep learning is slow. “The slowest growth,” the researchers wrote, occurs “for the most complex skills.”
In the end, the investors estimated that the return on their own project would be about 50 percent higher than the outside projects they had identified as conceptually similar. When given the chance at the end to rethink and revise, they slashed their own initial estimate. “They were sort of shocked,” Lovallo told me, “and the senior people were the most shocked.” The investors initially judged their own projects, where they knew all the details, completely differently from similar projects to which they were outsiders.
In 2001, the Boston Consulting Group, one of the most successful in the world, created an intranet site to provide consultants with collections of material to facilitate wide-ranging analogical thinking. The interactive “exhibits” were sorted by discipline (anthropology, psychology, history, and others), concept (change, logistics, productivity, and so on), and strategic theme (competition, cooperation, unions and alliances, and more). A consultant generating strategies for a post-merger integration might have perused the exhibit on how William the Conqueror “merged” England with the Norman Kingdom in the eleventh century. An exhibit that described Sherlock Holmes’s observational strategies could have provided ideas for learning from details that experienced professionals take for granted. And a consultant working with a rapidly expanding start-up might have gleaned ideas from the writing of a Prussian military strategist who studied the fragile equilibrium between maintaining momentum after a victory and overshooting a goal by so much that it turns into a defeat. If that all sounds incredibly remote from pressing business concerns, that is exactly the point.
Gentner and colleagues gave the Ambiguous Sorting Task to Northwestern University students from an array of majors and found that all of the students figured out how to group phenomena by domains. But fewer could come up with groupings based on causal structure. There was a group of students, however, who were particularly good at finding common deep structures: students who had taken classes in a range of domains, like those in the Integrated Science Program.
A professor I asked about the Integrated Science Program told me that specific academic departments are generally not big fans. They want students to take more specialized classes in a single department. They are concerned about the students falling behind. They would rather rush them to specialization than equip them with ideas from what Gentner referred to as a “variety of base domains,” which foster analogical thinking and conceptual connections that can help students categorize the type of problem they are facing. That is precisely a skill that sets the most adept problem solvers apart.
As education pioneer John Dewey put it in Logic, The Theory of Inquiry, “a problem well put is half-solved.”
In the lone lab that did not make any new findings during Dunbar’s project, everyone had similar and highly specialized backgrounds, and analogies were almost never used. “When all the members of the laboratory have the same knowledge at their disposal, then when a problem arises, a group of similar minded individuals will not provide more information to make analogies than a single individual,” Dunbar concluded. “It’s sort of like the stock market,” he told me. “You need a mixture of strategies.”
His new goal was to get accepted to a university so that he could later train as a pastor. Again, he unleashed his tireless passion. He worked with a tutor, and copied by hand the text of entire books. “I must sit up as long as I can keep my eyes open,” he told his brother. He reminded himself that “practice makes perfect,” but Latin and Greek did not come easily to him. He moved in with an uncle, a stern war hero who urged him simply, “push on.” The young man resolved to begin work before his peers rose and finish after they slept. His uncle would find him reading in the wee morning hours.
“Match quality” is a term economists use to describe the degree of fit between the work someone does and who they are — their abilities and proclivities.
Learning stuff was less important than learning about oneself. Exploration is not just a whimsical luxury of education; it is a central benefit.
Winston Churchill’s “never give in, never, never, never, never” is an oft-quoted trope. The end of the sentence is always left out: “except to convictions of honor and good sense.”
Very young people often have their goals set for them, or at least have a limited menu to choose from, and pursuing them with passion and resilience is the main challenge.
“Oh, don’t ask me what my training was,” she replied with a dismissing hand wave. She explained that she just did whatever seemed like it would teach her something and allow her to be of service at each moment, and somehow that added up to training. As Steven Naifeh said regarding Van Gogh’s life, some “undefinable process of digestion” occurred as diverse experiences accumulated. “I was unaware that I was being prepared,” she told me. “I did not intend to become a leader, I just learned by doing what was needed at the time.”
At the first ever Girl Scout training event Hesselbein attended, she heard another new troop leader complain that she was getting nothing from the session. Hesselbein mentioned it to a dress-factory worker who was also volunteering, and the woman told her, “You have to carry a big basket to bring something home.” She repeats that phrase today, to mean that a mind kept wide open will take something from every new experience.
The precise person you are now is fleeting, just like all the other people you’ve been. That feels like the most unexpected result, but it is also the most well documented.
The most momentous personality changes occur between age eighteen and one’s late twenties, so specializing early is a task of predicting match quality for a person who does not yet exist. It could work, but it makes for worse odds. Plus, while personality change slows, it does not stop at any age. Sometimes it can actually happen instantly.
Instead of asking whether someone is gritty, we should ask when they are. “If you get someone into a context that suits them,” Ogas said, “they’ll more likely work hard and it will look like grit from the outside.”
we learn who we are only by living, and not before.
He started with an idea, tested it, changed it, and readily abandoned it for a better project fit. Michelangelo might have fit well in Silicon Valley; he was a relentless iterator. He worked according to Ibarra’s new aphorism: “I know who I am when I see what I do.”
“To be frank, I don’t think we can benefit from domain expertise too much. . . . It’s very hard to win a competition just by using [well-known] methods,” he replied. “We need more creative solutions.”
“Knowledge is a double-edged sword. It allows you to do some things, but it also makes you blind to other things that you could do.”
“The disparity between the total quantity of recorded knowledge . . . and the limited human capacity to assimilate it, is not only enormous now but grows unremittingly,” he once said. How can frontiers be pushed, Swanson wondered, if one day it will take a lifetime just to reach them in each specialized domain?
Swanson wanted to show that areas of specialist literature that never normally overlapped were rife with hidden interdisciplinary treasures waiting to be connected. He created a computer system, Arrowsmith, that helped other users do what he did — devise searches that might turn up distant but relevant sets of scientific articles, and ignited a field of information science that grapples with connecting diverse areas of knowledge, as specialties that can inform one another drift apart.
The more information specialists create, the more opportunity exists for curious dilettantes to contribute by merging strands of widely available but disparate information — undiscovered public knowledge, as Don Swanson called it. The larger and more easily accessible the library of human knowledge, the more chances for inquisitive patrons to make connections at the cutting edge.
There is, to be sure, no comprehensive theory of creativity. But there is a well-documented tendency people have to consider only familiar uses for objects, an instinct known as functional fixedness. The most famous example is the “candle problem,” in which participants are given a candle, a box of tacks, and a book of matches and told to attach the candle to the wall such that wax doesn’t drip on the table below. Solvers try to melt the candle to the wall or tack it up somehow, neither of which work. When the problem is presented with the tacks outside of their box, solvers are more likely to view the empty box as a potential candle holder, and to solve the problem by tacking it to the wall and placing the candle inside.
Yokoi was the first to admit it. “I don’t have any particular specialist skills,” he once said. “I have a sort of vague knowledge of everything.” He advised young employees not just to play with technology for its own sake, but to play with ideas. Do not be an engineer, he said, be a producer. “The producer knows that there’s such a thing as a semiconductor, but doesn’t need to know its inner workings. . . . That can be left to the experts.” He argued, “Everyone takes the approach of learning detailed, complex skills. If no one did this then there wouldn’t be people who shine as engineers. . . . Looking at me, from the engineer’s perspective, it’s like, ‘Look at this idiot,’ but once you’ve got a couple hit products under your belt, this word ‘idiot’ seems to slip away somewhere.”
As the company grew, he worried that young engineers would be too concerned about looking stupid to share ideas for novel uses of old technology, so he began intentionally blurting out crazy ideas at meetings to set the tone. “Once a young person starts saying things like, ‘Well, it’s not really my place to say . . .’ then it’s all over,” he said.
Eminent physicist and mathematician Freeman Dyson styled it this way: we need both focused frogs and visionary birds. “Birds fly high in the air and survey broad vistas of mathematics out to the far horizon,” Dyson wrote in 2009. “They delight in concepts that unify our thinking and bring together diverse problems from different parts of the landscape. Frogs live in the mud below and see only the flowers that grow nearby. They delight in the details of particular objects, and they solve problems one at a time.” As a mathematician, Dyson labeled himself a frog, but contended, “It is stupid to claim that birds are better than frogs because they see farther, or that frogs are better than birds because they see deeper.” The world, he wrote, is both broad and deep. “We need birds and frogs working together to explore it.” Dyson’s concern was that science is increasingly overflowing with frogs, trained only in a narrow specialty and unable to change as science itself does. “This is a hazardous situation,” he warned, “for the young people and also for the future of science.”
Specialization is obvious: keep going straight. Breadth is trickier to grow. A subsidiary of PricewaterhouseCoopers that studied technological innovation over a decade found that there was no statistically significant relationship between R& D spending and performance.* (Save for the bottom 10 percent of spenders, which did perform worse than their peer companies.) Seeding the soil for generalists and polymaths who integrate knowledge takes more than money. It takes opportunity.
She is a “T-shaped person,” she said, one who has breadth, compared to an “I-shaped person,” who only goes deep, an analog to Dyson’s birds and frogs. “T-people like myself can happily go to the I-people with questions to create the trunk for the T,” she told me. “My inclination is to attack a problem by building a narrative. I figure out the fundamental questions to ask, and if you ask those questions of the people who actually do know their stuff, you are still exactly where you would be if you had all this other knowledge inherently. It’s mosaic building. I just keep putting those tiles together. Imagine me in a network where I didn’t have the ability to access all these people. That really wouldn’t work well.”
“If you’re working on well-defined and well-understood problems, specialists work very, very well,” he told me. “As ambiguity and uncertainty increases, which is the norm with systems problems, breadth becomes increasingly important.”
University of Utah professor Abbie Griffin has made it her work to study modern Thomas Edisons — “ serial innovators,” she and two colleagues termed them. Their findings about who these people are should sound familiar by now: “high tolerance for ambiguity”; “systems thinkers”; “additional technical knowledge from peripheral domains”; “repurposing what is already available”; “adept at using analogous domains for finding inputs to the invention process”; “ability to connect disparate pieces of information in new ways”; “synthesizing information from many different sources”; “they appear to flit among ideas”; “broad range of interests”; “they read more (and more broadly) than other technologists and have a wider range of outside interests”; “need to learn significantly across multiple domains”; “Serial innovators also need to communicate with various individuals with technical expertise outside of their own domain.” Get the picture?
Toward the end of their book Serial Innovators, Abbie Griffin and her coauthors depart from stoically sharing their data and observations and offer advice to human resources managers. They are concerned that HR policies at mature companies have such well-defined, specialized slots for employees that potential serial innovators will look like “round pegs to the square holes” and get screened out. Their breadth of interests do not neatly fit a rubric. They are “π-shaped people” who dive in and out of multiple specialties. “Look for wide-ranging interests,” they advised. “Look for multiple hobbies and avocations. . . . When the candidate describes his or her work, does he or she tend to focus on the boundaries and the interfaces with other systems?” One serial innovator described his network of enterprise as “a bunch of bobbers hanging in the water that have little thoughts attached to them.” Hamilton creator Lin-Manuel Miranda painted the same idea elegantly: “I have a lot of apps open in my brain right now.”
“There is often a curiously inverse relationship,” Tetlock concluded, “between how well forecasters thought they were doing and how well they did.”
There was also a “perverse inverse relationship” between fame and accuracy. The more likely an expert was to have his or her predictions featured on op-ed pages and television, the more likely they were always wrong. Or, not always wrong. Rather, as Tetlock and his coauthor succinctly put it in their book Superforecasting, “roughly as accurate as a dart-throwing chimpanzee.”
He learned that specializing in a topic frequently did not bear fruit in the forecasts. “So if I know somebody [on the team] is a subject area expert, I am very, very happy to have access to them, in terms of asking questions and seeing what they dig up. But I’m not going to just say, ‘Okay, the biochemist said a certain drug is likely to come to market, so he must be right.’ Often if you’re too much of an insider, it’s hard to get good perspective.” Eastman described the core trait of the best forecasters to me as: “genuinely curious about, well, really everything.”
Narrow experts are an invaluable resource, she told me, “but you have to understand that they may have blinders on. So what I try to do is take facts from them, not opinions.” Like polymath inventors, Eastman and Cousins take ravenously from specialists and integrate.
In contrast to politicians, the most adept predictors flip-flop like crazy.
Researchers in Canada and the United States began a 2017 study by asking a politically diverse and well-educated group of adults to read arguments confirming their beliefs about controversial issues. When participants were then given a chance to get paid if they read contrary arguments, two-thirds decided they would rather not even look at the counterarguments, never mind seriously entertain them. The aversion to contrary ideas is not a simple artifact of stupidity or ignorance. Yale law and psychology professor Dan Kahan has shown that more scientifically literate adults are actually more likely to become dogmatic about politically polarizing topics in science. Kahan thinks it could be because they are better at finding evidence to confirm their feelings: the more time they spend on the topic, the more hedgehog-like they become.
Charles Darwin must have been one of the most curious and actively open-minded human beings in history. His first four models of evolution were forms of creationism or intelligent design. (The fifth model treated creation as a separate question.) He made a point of copying into his notes any fact or observation he encountered that ran contrary to a theory he was working on. He relentlessly attacked his own ideas, dispensing with one model after another, until he arrived at a theory that fit the totality of the evidence.
God does not play dice with the universe, Einstein asserted, figuratively. Niels Bohr, his contemporary who illuminated the structure of atoms (using analogies to Saturn’s rings and the solar system), replied that Einstein should keep an open mind and not tell God how to run the universe.
Basically, forecasters can improve by generating a list of separate events with deep structural similarities, rather than focusing only on internal details of the specific event in question. Few events are 100 percent novel — uniqueness is a matter of degree, as Tetlock puts it — and creating the list forces a forecaster implicitly to think like a statistician.
In Tetlock’s twenty-year study, both foxes and hedgehogs were quick to update their beliefs after successful predictions, by reinforcing them even more strongly. When an outcome took them by surprise, however, foxes were much more likely to adjust their ideas. Hedgehogs barely budged. Some hedgehogs made authoritative predictions that turned out wildly wrong, and then updated their theories in the wrong direction. They became even more convinced of the original beliefs that led them astray. “Good judges are good belief updaters,” according to Tetlock. If they make a bet and lose, they embrace the logic of a loss just as they would the reinforcement of a win. That is called, in a word: learning. Sometimes, it involves putting experience aside entirely.
But it’s often the case in group meetings where the person who made the PowerPoint slides puts data in front of you, and we often just use the data people put in front of us. I would argue we don’t do a good job of saying, ‘Is this the data that we want to make the decision we need to make?’”
Reason without numbers was not accepted. In the face of an unfamiliar challenge, NASA managers failed to drop their familiar tools.
“Dropping one’s tools is a proxy for unlearning, for adaptation, for flexibility,” Weick wrote. “It is the very unwillingness of people to drop their tools that turns some of these dramas into tragedies.”
“If I make a decision, it is a possession, I take pride in it, I tend to defend it and not listen to those who question it,” Gleason explained. “If I make sense, then this is more dynamic and I listen and I can change it.” He employed what Weick called “hunches held lightly.” Gleason gave decisive directions to his crew, but with transparent rationale and the addendum that the plan was ripe for revision as the team collectively made sense of a fire.
Gene Kranz, the flight director when Apollo 11 first landed on the moon, lived by that same mantra, the valorized process — “ In God We Trust, All Others Bring Data” — but he also made a habit of seeking out opinions of technicians and engineers at every level of the hierarchy. If he heard the same hunch twice, it didn’t take data for him to interrupt the usual process and investigate.
“I would argue, at least in medicine and basic science where we fill people up with facts from courses, that what is needed is just some background, and then the tools for thinking,” Casadevall told me. Currently, “everything is configuring in the wrong way.”
There is a growth industry of conferences that invite only scientists who work on a single specific microorganism. Meanwhile, a complete understanding of the body’s response to a paper cut was hampered because hyperspecialists in hematology and immunology focus on pieces of the puzzle in isolation, even though the immune response is an integrated system.
A separate, international team analyzed more than a half million research articles, and classified a paper as “novel” if it cited two other journals that had never before appeared together. Just one in ten papers made a new combination, and only one in twenty made multiple new combinations. The group tracked the impact of research papers over time. They saw that papers with new knowledge combinations were more likely to be published in less prestigious journals, and also much more likely to be ignored upon publication.
A single conversation with him is liable to include Anna Karenina, the Federalist Papers, the fact that Isaac Newton and Gottfried Leibniz were philosophers as well as scientists, why the Roman Empire wasn’t more innovative, and a point about mentoring in the form of a description of the character Mentor from Homer’s Odyssey. “I work at it,” he said, smirking. “I always advise my people to read outside your field, everyday something. And most people say, ‘Well, I don’t have time to read outside my field.’ I say, ‘No, you do have time, it’s far more important.’ Your world becomes a bigger world, and maybe there’s a moment in which you make connections.”
Compare yourself to yourself yesterday, not to younger people who aren’t you. Everyone progresses at a different rate, so don’t let anyone else make you feel behind. You probably don’t even know where exactly you’re going, so feeling behind doesn’t help.
