Ralph Waldo Emerson once noted that "in every work of genius we recognize our own rejected thoughts: they come back to us with a certain alienated majesty."

We've all at some point had the thought "I'm feeling pulled in two directions." For example, we all know what it feels like to desire some immediate gratification while recognizing that it would be better in the long run to resist. Thoughts like these are noticed even early in life: very young children also notice the conflict between different "sides" of themselves. But our thoughts about this conflict are often left inchoate, or even rejected.

It took somebody with Danny Kahneman's genius to live up to Emerson's challenge: to give these kinds of thoughts their appropriate majesty.

What Danny showed the world is that our conflicting cognitive moments fit into a pattern: they are all the result of a fundamental feature of our cognitive architecture, namely, that our minds are dualistic. Danny's ingenious studies gave us insight into the processes that make us more than just our System IIs. While he wasn't the first to notice that the fast systems existed, he was the first to give us tools that could tap into the mechanisms that underlie our automatic heuristics.

And what Danny showed the world, he has also taught to the two of us as scholars. Even though we work on superficially different topics (the evolutionary origins of cognitive biases in other primates and the philosophical roots of the relation between rational and arational processing—respectively) in two different departments (Psychology and Philosophy), Danny's influence on both of our research programs has been profound. His work has defined the space in which our questions emerge.

But Danny's work has also left both our research programs with a new challenge. Novices who hear about Danny's findings for the first time sometimes get the wrong message about thinking fast or slow—they want to choose one or the other. But this misses the important point. Danny's work teaches us that we don't need to make that choice: An effective mind is one that doesn't fall prey to the easy answers it gets from System I when they mislead, but leaves the fast heuristics in charge when they're best suited to steer us in the right direction. We now know that our goal shouldn't be to get rid of one system or the other, but to get them both into an appropriate balance.

The problem is how to do this—what is the right balance and how do we achieve it? There's still a lot to learn, both in terms of mechanisms and in terms of applications. Answering this new question is the task of the generation that Danny has inspired.

When I was a 20-year-old undergraduate majoring in economics I heard Jack Knetsch, a longtime Kahneman collaborator, speak about the endowment effect. The talk was a revelation and introduced me to the experimental side of decision-making, where I then found work by Daniel Kahneman and others, who had been studying decision-making for decades. As professors were trying to convince me of behavior based on laws of supply and demand, the insights of Kahneman, Tversky, Knetsch, Thaler and others showed that the conventional economic models were deeply flawed.

After what I see as years of hard work, experiments of admirable design, lucid writing, and quiet leadership, Kahneman, a man who spent the majority of his career in departments of psychology, earned the highest prize in economics. This was a reminder that some of the best insights into economic behavior could be (and had been) gleaned outside of the discipline. Aside from amassing such a huge and influential body of work, Kahneman's revolutionary advances in economic decision-making granted everyone permission to take scholars outside the field of economics seriously and reminded us that humans who think both fast and slow, not mathematical models built on ideologies, are the bedrock of behavior.

Essential Ingredients

Kahneman's influence was to provide the essential ingredients for all of my work: a problem, and possible solutions. The problem was that people's beliefs, judgments, and choices are routinely "wrong." They may wrong because they disagree with a statistical principle, a rational principle, reality, or some combination of all three. The solution is that people beliefs, judgments, and choices are not guided simply by statistics, rationality, or reality, but instead are guided by generally intelligent, but imperfect, psychological processes that take hard problems and convert them to easy problems that normal human beings can solve. If you understand these processes that guide intuitive judgment, then you can understand why perception and reality diverge.

By extending Kahneman's problem as well as his ideas about its solution, I have built a career trying to understand how otherwise brilliant human beings can be so routinely "wrong" in their beliefs and judgments about each other. Why do people overestimate how often others agree with them? Why are people sometimes less accurate predicting their own future behavior than predicting others' behavior? Why do people overestimate how harshly they will be judged for an embarrassing blunder? Why do liberals think conservatives have more extreme views than conservatives actually do? The list of such cases where our social thinking goes wrong is long, but it is Kahneman's influence that runs through its entire length.

Asking how Kahneman's work has influenced my own is a bit like asking a doctor how oxygen influences life. My work wouldn't exist without him.

There's an overarching lesson I have learned from the work of Danny Kahneman, Amos Tversky, and their colleagues who collectively pioneered the modern study of judgment and decision-making: Don't trust your intuition. It's such an important lesson that it wound up in the subtitle of my book. So I am wary of consulting my intuition to try to discern a pattern of influence that may only be visible through the distorting lens of hindsight. Instead, I'll talk about the value of Kahneman's approach to behavioral research.

As I see it, the Kahneman formula for high-impact social science combines three elements:

1. Systematic human error: Kahneman and Tversky are known for discovering situations where many or most people give answers that are inconsistent with some normative theory of what is correct. Usually this theory is based on logic or basic probability or arithmetic. But those errors aren't random—they are systematic errors, also known as biases. It's easy to forget how revolutionary this approach once was. Much research in cognitive psychology compares the rate of error in different conditions, but it doesn't look at the content of those errors. Looking at the frequency of mistakes across different conditions is a perfectly valid and useful research strategy, but looking at systematic error is more powerful. For one thing, when a normative theory makes a strong prediction—that a certain answer is simply wrong and should never occur—then it is easier to find large effects. (The t-test against a theoretical value is a high-power statistical procedure.)

2. Large effects: There's nothing wrong with looking for small effects, and small effects can have great theoretical meaning. But they are harder to find, and they tend to be of less practical importance. As much as possible we should work on large effects that can be easily replicated, since those will provide a firm empirical foundation for future work. If some researchers can obtain effects that others can't, progress will be difficult. Kahneman's work shows us that there are many large effects out there to be found and understood.

3. Simple experiments: The simpler an experiment is to conduct, the more likely it is that multiple independent researchers will be able to replicate it, extend it, and exchange information about it. Kahneman's most influential studies consisted of asking research subjects just one easily-understood question, and the methodology was the simple randomized experiment.

Combining these three elements isn't the only way to do good behavioral research, by any means. And of course it doesn't automatically work. It can be dangerously seductive if it leads us to pursue what is surprising or newsworthy over what is important and true. But it has the potential to point toward deeply nonobvious facts that have large consequences. Kahneman and his colleagues discovered lots of those, including the conjunction fallacy, the neglect of base rates, the excessive pain of losses as compared to the pleasure of gains, the difference in how we value things we own and things we don't, and the surprising happiness of people who have suffered greatly. We can and should discuss whether these are all really errors (as opposed to behaviors that have or had adaptive value) and we should work on discovering the mechanisms that lie beneath them. Those pursuits are likely to bear fruit precisely because they are starting from the firm foundation Kahneman's approach established.

More than a hundred years into the modern history of psychology, I think it's fair to say that we are still a long way from fully understanding how the mind works. But it's also fair to say that nobody will come up with a compelling account of human cognition unless they wrestle seriously with Daniel Kahneman's pioneering work with Amos Tversky on heuristics and cognitive biases.

You would think that point would be obvious, but in my view, none of the currently fashionable theories of mind take Kahneman and Tversky's work seriously enough. Take, for instance, the popular notion that the mind might be a Bayesian engine of probabilistic cognition. According to one leading theorist, "over the past decade, many aspects of higher-level cognition have been illuminated by the mathematics of Bayesian statistics." Demonstration after demonstration purports to show that in certain narrowly-defined tasks, human psychology follows directly from the laws of probability. In this domain or that, people are said to make "optimal" or "near-optimal" decisions. People, for example, are very good at extrapolating how long someone might remain in the Senate, given that Senator X has already been there for a decade. But Bayesian zealots seem to forget just how lousy human beings are in other cases of extrapolation, as Kahneman and Tversky showed long ago, with their framing effect. (Take a dollar bill from your pocket, read the last three digits of the serial number, and then guess when Attila the Hun was born; most people will erroneously extrapolate from this bit of wholly irrelevant information.) In a recent review, I suggested that every alleged Bayesian success could be juxtaposed with an equally compelling cognitive error, most of which were first documented by Kahneman and Tversky. Every time I read the Bayesian literature, I wince, and think of a different cognitive error: confirmation bias. It's easy to find evidence for any old theory; good science requires considering evidence that might potentially go against one's theory. In my humble opinion, anyone who tries to understand the mind without taking seriously Kahneman's oeuvre is doomed to failure.

In my own work, I have thought deeply about what Kahneman's work might mean for evolutionary psychology. The default assumption of evolutionary psychology is one of optimality: give evolution enough time, and eventually it will alight on a beautiful, elegant solution, like the retina, sensitive to a single photon of light. The reality of evolution is that it is a blind process, with no guarantee of alighting on optimality. Too much of evolutionary psychology, in my view, dwells on systems in which the mind is apparently optimal; the real challenge ought to be in understanding how those apparently-optimal systems live alongside other systems that sometimes confoundingly seem to do the wrong thing; until evolutionary psychology can explain anchoring, availability, and future discounting, as well as it can explain mate selection and reciprocal altruism; it will be only half a science. The ultimate goal of human psychology must be to characterize both what we do well, and what we do poorly, and how we balance the two. Kahneman and Tversky's work is, without question, the best place to start.

Danny Kahneman is responsible for so many ideas that one needs a heuristic to select among them. I am choosing the Coauthorship Heuristic.

From about 1996 until about 2007, I was privileged to work with Kahneman (and David Schkade) on a series of papers on punitive damage awards. Here are four ideas for which Kahneman is above all responsible. These ideas are hardly Kahneman’s most well-known, but they are full of implications, and we have only started to understand them.

1.     The outrage heuristic. People’s judgments about punishment are a product of outrage, which operates as a shorthand for more complex inquiries that judges and lawyers often think relevant. When people decide about appropriate punishment, they tend to ask a simple question: How outrageous was the underlying conduct? It follows that people are intuitive retributivists, and also that utilitarian thinking will often seem uncongenial and even outrageous.

2.     Scaling without a modulus. Remarkably, it turns out that people often agree on how outrageous certain misconduct is (on a scale of 1 to 8), but also remarkably, their monetary judgments are all over the map. The reason is that people do not have a good sense of how to translate their judgments of outrage onto the monetary scale. As Kahneman shows, some work in psychophysics explains the problem: People are asked to “scale without a modulus,” and that is an exceedingly challenging task. The result is uncertainty and unpredictability. These claims have implications for numerous questions in law and policy, including the award of damages for pain and suffering, administrative penalties, and criminal sentences.

3.     Rhetorical asymmetry. In our work on jury awards, we found that deliberating juries typically produce monetary awards against corporate defendants that are higher, and indeed much higher, than the median award of the individual jurors before deliberation began. Kahneman’s hypothesis is that in at least a certain category of cases, those who argue for higher awards have a rhetoric advantage over those who argue for lower awards, leading to a rhetorical asymmetry. The basic idea is that in light of social norms, one side, in certain debates, has an inherent advantage – and group judgments will shift accordingly. A similar rhetorical asymmetry can be found in groups of many kinds, in both private and public sectors, and it helps to explain why groups move.

4.     Predictably incoherent judgments. We found that when people make moral or legal judgments in isolation, they produce a pattern of outcomes that they would themselves reject, if only they could see that pattern as a whole. A major reason is that human thinking is category-bound. When people see a case in isolation, they spontaneously compare it to other cases that are mainly drawn from the same category of harms. When people are required to compare cases that involve different kinds of harms, judgments that appear sensible when the problems are considered separately often appear incoherent and arbitrary in the broader context. In my view, Kahneman’s idea of predictable coherence has yet to be adequately appreciated; it bears on both fiscal policy and on regulation.

We should be able to see the close connection between these findings and  many themes in Kahneman’s work, and indeed the distinction between System 1 and System 2 helps to illuminate all of them. That distinction, and Kahneman’s findings about how risk-related intuitions can go wrong, very much influenced my work in the Obama Administration, where I was privileged to serve as the Administrator of the White House Office of Information and Regulatory Affairs. It is clear that in an increasing number of nations, public policy is a lot better than it would be because of Kahneman’s research. Both System 1 and System 2 concur: The influence of that research will grow significantly in the future.

Loss aversion was, of course, widely understood by the advertising industry long before it was adopted by economists. The slogan "Nobody ever got fired for buying IBM" suggests that people might be willing to pay a significant premium to avoid the small chance of a disastrous outcome.

The advertising industry seems to have known about anchoring, too. It was a copywriter for the agency N W Ayer (possibly Frances Gerety, one of the models for Peggy In Mad Men) who wrote the headline for De Beers to promote engagement rings: "How else can a month's salary last a lifetime?".

Like the TV game-show industry, we managed to discover all this stuff on our own because, by a happy accident, almost no-one in our industry knew anything about economics at all. And therefore it never occurred to us to try to understand human behaviour by first pretending that emotions such as regret, embarrassment or mistrust do not exist.

By assuming "perfect rationality, perfect information and perfect trust", bad economics has created a world where efficiency is all that matters; where ethics, psychology and perception can be ignored; and (possible self-serving bias here) where brands, marketing and advertising should not exist. As one climate scientist said to me recently, if you ask any conventional economist "How do we get people to do X?" the answer, though dressed up in fancier language, always amounts to the same thing: "By bribing them to do it or fining them for not doing it." That is a very limited toolkit with which to work.

This being Edge, there are people far more qualified than I am to write about his work on Prospect Theory, Base-Rate Neglect, Hedonics, Affect Forecasting or—a personal favourite—on his "failure to disagree" with Gary Klein. What I can add from my own field is a note about another really important achievement of his last few years: the fact that he has become a brand. This is more important than you may think.

When I met Danny in London in 2009 he diffidently said that the only hope he had for his work was that "it might lead to a better kind of gossip"—where people discuss each other's motivations and behaviour in slightly more intelligent terms. To someone from an industry where a new flavour-variant of toothpaste is presented as being an earth-changing event, this seemed an incredibly modest aspiration for such important work.

However, if this was his aim, he has surely succeeded. When I meet people, I now use what I call "the Kahneman heuristic". You simply ask people "Have you read Danny Kahneman's book?" If the answer is yes, you know (p>0.95) that the conversation will be more interesting, wide-ranging and open-minded than otherwise.

And it then occurred to me that his aim—for better conversations—was perhaps not modest at all. Multiplied a millionfold it may very important indeed. In the social sciences, I think it is fair to say, the good ideas are not always influential and the influential ideas are not always good. Kahneman's work is now both good and influential.

We now think it perfectly normal that physical objects are designed to work with the way our bodies have evolved; this is why the keyboard on which I am typing this is reasonably congruent with the size of my hands, and why I do not steer my car with my nose. Yet many programmes and services are designed not for the brains of humans but of Vulcans. Thanks in large part to Kahneman and his many collaborators pupils and acolytes, this can and will change. 

As many Edge readers know, my recent work has involved presenting copious data indicating that rates of violence have fallen over the years, decades, and centuries, including the number of annual deaths in war, terrorism, and homicide. Most people find this claim incredible on the face of it. Why the discrepancy between data and belief? The answer comes right out of Danny's work with Amos Tversky on the Availability Heuristic. People estimate the probability of an event by the ease of recovering vivid examples from memory. As I explained, "Scenes of carnage are more likely to be beamed into our homes and burned into our memories than footage of people dying of old age. No matter how small the percentage of violent deaths may be, in absolute numbers there will always be enough of them to fill the evening news, so people's impressions of violence will be disconnected from the actual proportions."

The availability heuristic also explains a paradox in people's perception of the risks of terrorism. The world was turned upside-down in response to the terrorist attacks on 9/11. But putting aside the entirely hypothetical scenario of nuclear terrorism, even the worst terrorist attacks kill a trifling number of people compared to other causes of violent death such as war, genocide, and homicide, to say nothing of other risks of death. Terrorists know this, and draw disproportionate attention to their grievances by killing a relatively small number of innocent people in the most attention-getting ways they can think of.

Even the perceived probability of nuclear terrorism is almost certainly exaggerated by the imaginability of the scenario (predicted at various times to be near-certain by 1990, 2000, 2005, and 2010, and notoriously justifying the 2003 invasion of Iraq). I did an internet survey which showed that people judge it more probable that "a nuclear bomb would be set off in the United States or Israel by a terrorist group that obtained it from Iran" than that "a nuclear bomb would be set off." It's an excellent example of Kahneman and Tversky's Conjunction Fallacy, which they famously illustrated with the articulate activist Linda, who was judged more likely to be feminist bank teller than a bank teller.

"Answering an Easier Question"

I have known Danny Kahneman for more than 40 years, and am taking the liberty of bypassing the editorial instructions to avoid the personal and will mention how we first met. From my brief time as chair of the Stanford Psychology Department in the 1970s I recall two achievements: a new paint job and hiring Amos Tversky. Amos in turn brought Danny often into the Tversky's campus home. Some of my most treasured memories of that time were watching them thinking, talking, and laughing when they were at the height of their collaboration. Since then, I have avidly absorbed Danny's work, and enjoyed every conversation we have had. It has all influenced my own thinking, not only about our science, but about how to try to do it right.

My second violation of Edge's request to stick to ideas, not books, is to note that an extraordinary moment is occurring that is good news not just for Danny but for our science: Danny's serious book about how we think is being celebrated in London on the sale of its one millionth's copy in the UK alone. So, benefitting from his Thinking Fast and Slow, specifically Chapter 9, "Ansering an Easier Question", I will use my space here not to address the question Edge asked, but to ask instead: How did he do it? Not just the book, but all of it.

Kahneman himself gives us some good hints in a short 2007 autobiography buried in volume IX of A History of Psychology in Autobiography in an APA series that may have more authors than readers. He tells us that he has consistently tried to use an approach he calls the "psychology of single questions" which he contrasts with the typical approach in psychological science in which "concepts are commonly associated with procedures that can be described only by long lists or by convoluted paragraphs of prose."

In one wonderful year he and Amos focused completely on the 1974 Science article that catapulted them into the history books. Danny attributes its remarkable impact (that ultimately also led to the Nobel Prize), to the medium as much as to the message. He notes that in the 1974 article he and Amos continued to practice the psychology of single questions. He believes that citing those questions verbatim in the text of the article: "personally engaged readers and convinced them that we were concerned not with the stupidity of Joe Public but with a much more interesting issue: the susceptibility to erroneous intuitions of intelligent, sophisticated, and perceptive individuals such as themselves." Forty years later the same voice and practice surely underlie the success of Thinking Fast and Slow.

Reflecting further on his collaboration with Tversky and the genesis of that1974 article, Danny says they went through 30-odd versions of prospect theory. What kept them going was Amos' often-used phrase "Let's do it right." They did, and in what Kahneman has kept on doing, he keeps showing us how to get it right.

It's hard to overstate Kahneman's influence on my work. What I have done, essentially, is to look at moral thinking through the lenses ground by Daniel Kahneman. In my first year of college I was introduced to the field of "heuristics and biases" and was struck by the power of these ideas—that some of the most important decisions we make are deeply myopic. Soon after, I was introduced to contemporary debates in ethics, much of which center around moral dilemmas such as the Trolley Problem. These dilemmas boil the great debate in modern moral philosophy—Kant vs. Mill—down to its essentials, eliciting strong judgments favoring both sides. I felt that if I could understand the thinking behind those judgments I could understand a lot. Looking at these problems through my new Kahnemanian lens, I saw modern moral debate as a battle between morality "fast and slow," both in the brain and out in the world. I've been chasing down the implications of that idea ever since.

The Problem of Multiple Counterfactuals

Here is an insight Danny K. triggered and changed the course of my work. I figured out a nontrivial problem in randomness and its underestimation a decade ago while reading the following sentence in a paper by Kahneman and Miller of 1986:

A spectator at a weight lifting event, for example, will find it easier to imagine the same athlete lifting a different weight than to keep the achievement constant and vary the athlete's physique.

This idea of varying one side, not the other also applies to mental simulations of future (random) events, when people engage in projections of different counterfactuals (we treat alternative past and future histories in exactly the same analytical manner).

It hit me that the mathematical consequence is vastly more severe than it appears. Kahneman and colleagues focused on the bias that variable of choice is not random. But the paper set off in my mind the following realization: now what if we were to go one step beyond and perturbate both? The response would be nonlinear. I had never considered the effect of such nonlinearity earlier nor seen it explicitly made in the literature on risk and counterfactuals. And you never encounter one single random variable in real life; there are many things moving together.

Increasing the number of random variables compounds the number of counterfactuals and causes more extremes—particularly in fat-tailed environments (i.e., Extremistan): imagine perturbating by producing a lot of scenarios and, in one of the scenarios, increasing the weights of the barbell and decreasing the bodyweight of the weightlifter. This compounding would produce an extreme event of sorts. Extreme, or tail events (Black Swans) are therefore more likely to be produced when both variables are random, that is real life. Simple.

Now, in the real world we never face one variable without something else with it. In academic experiments, we do. This sets the serious difference between laboratory (or the casino's "ludic" setup), and the difference between academia and real life. And such difference is, sort of, tractable.

I rushed to change a section for the 2003 printing of one of my books. Say you are the manager of a fertilizer plant. You try to issue various projections of the sales of your product—like the weights in the weightlifter's story. But you also need to keep in mind that there is a second variable to perturbate: what happens to the competition—you do not want them to be lucky, invent better products, or cheaper technologies. So not only you need to predict your fate (with errors) but also that of the competition (also with errors). And the variance from these errors add arithmetically when one focuses on differences. There was a serious error made by financial analysts. When comparing strategy A and strategy B, people in finance compare the Sharpe ratio (that is, the mean divided by the standard deviation of a stream of returns) of A to the Sharpe ratio of B and look at the difference between the two. It is very different than the correct method of looking at the Sharpe ratio of the difference, A-B, which requires a full distribution.

Now, the bad news: the misunderstanding of the problem is general. Because scientists (not just financial analysts) use statistical methods blindly and mechanistically, like cooking recipes, they tend to make the mistake when consciously comparing two variables. About a decade after I exposed the Sharpe ratio problem, Nieuwenhuis et al. in 2011 found that 50% of neuroscience papers (peer-reviewed in "prestigious journals") that compared variables got it wrong, using the single variable methodology.

In theory, a comparison of two experimental effects requires a statistical test on their difference. In practice, this comparison is often based on an incorrect procedure involving two separate tests in which researchers conclude that effects differ when one effect is significant (P < 0.05) but the other is not (P > 0.05). We reviewed 513 behavioral, systems and cognitive neuroscience articles in five top-ranking journals (Science, Nature, Nature Neuroscience, Neuron and The Journal of Neuroscience) and found that 78 used the correct procedure and 79 used the incorrect procedure. An additional analysis suggests that incorrect analyses of interactions are even more common in cellular and molecular neuroscience.

Sadly, ten years after I reported the problem to investment professionals; the mistake is still being made. Ten years from now, they will still be making the same mistake.

Now that was the mild problem. There is worse. We were discussing two variables. Now assume the entire environment is random, and you will see that standard analyses of future events are doomed to underestimate tails. In risk studies, a severe blindness to multivariate tails prevails. The discussions on the systemic risks of genetically modified organisms (GMOs) by "experts" falls for such butchering of risk management, invoking some biological mechanism and missing on the properties of the joint distribution of tails.

Daniel Kahneman is undoubtedly one of the most astounding minds in our field. His influence is equally vast and far-reaching as it is deep and sound. Yet his influence stretches beyond even science itself to cultivate two essential qualities in the humans behind the science: humility and receptiveness.

For humility, Einstein couldn't have said it better: "A true genius admits that he/she knows nothing." Kahneman's approach to science and interacting with others was one of great knowledge, yet emphasizing instead what he didn't know. Take his contributions to the study of happiness and well-being. As someone who pioneered our understanding of the nature of happiness—from what we mean by happiness, to overestimating the weight of factors such as geography that determine our happiness, to our two competing selves in searching for happiness—he once said that he "did not understand what happiness was." He emphasized how complex happiness was, how much we have yet to understand. Not once did he brag about his own work or insights, or make simplified sound-bite statements about happiness that often plague the field. Instead, he created a common ground where scientists could stand in the face of the unknown, and that happiness was one of these great unknowns sitting right in front of is. Part of his rich understanding of happiness and well-being is his noting what he didn't know and that we are very small in the vast space of knowing things.

For receptiveness, Kahneman is both an advocate for, and model of, civilized discourse in scientific debate. Cultivating respectful and productive approaches to resolving scientific disagreements is so important to him that he has named it as perhaps the most important legacy he hopes to leave. His work on adversarial collaboration offers one route through which scholars who disagree on a particular theoretical issue might attempt to resolve their dispute —by first identifying what they agree on, and then trying to "trade" the differences. But his contribution here goes far beyond the formal construct of adversarial collaboration. He continues to be guided by this principle, encouraging scholars who disagree about ideas to do so in a way that is respectful and productive. This is not an easy stance to take. In fact, psychological science shows us that it's actually quite difficult to be gracious and dignified when your ideas (or you) are being attacked. It's much easier to give in—to quickly fire back which serves neither the scholars nor the science. "You have to be willing to not win," he said. It sounds so simple—like advice we might give to our kids. But it is not easy, even for scientists with the best intentions, to live by that advice. Nonetheless, he does.