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 has not yet influenced my work on snails and mice, but I am only in an early point in my career and I still look forward to exploring his ideas in a molecular biological context in the future. 

That said, he has influenced my life. His friendship, advice and thoughtfulness have enriched me significantly and I look forward to many more productive interactions.

Danny Kahneman sets a nearly impossible standard for social scientists: design experiments that so perfectly (and subtly) capture two different versions of the world that you don't even need to see the results to have already learned something novel.

A change of a single word or a single number—think of "400 people will die" versus "200 people will be saved"—and readers are instantly in the shoes of people experiencing those two different versions, and immediately see how they would behave differently in those two worlds.

This ability to create two seemingly similar worlds that lead to such different behavior leads to a broader learning, a kind of counterfactual approach to the world: "Wait, if a few words can alter my own behavior, how much of what I think and do is influenced by other trivial factors? My mood? My level of sleep? What I ate for breakfast?"

In a research world in which complexity of design and analysis serves as a proxy for importance, his approach reminds us—both scientists and laypeople—that the most profound insight often derives from the most elegant design.

Kahneman and Tversky made Behavioral Economics Adjacently Possible

Even in science, timing is everything. Charles Babbage' programmable computer was, in 1837, a step—or two—too early. Influential ideas are those that are novel but just familiar enough that existing researchers can build on them. Stuart Kauffman coined the term "adjacent possible" to describe the untapped potential that sits only one step away where scientists currently sit. Scientists who open the adjacent possible deserve the research equivalent of an "assist" in sports.

The earliest work of Daniel Kahneman and Amos Tversky, the famous "heuristics and biases program" merits many such assists, for opening up an important adjacent possible for economics: it made possible behavioral economics. We, like so many others, flowed into this territory and both owe our careers to the creation of that possibility.

Kahneman and Tversky's early work opened this door exactly because it was not what most people think it was. Many think of this work as an attack on rationality (often defined in some narrow technical sense). That misconception still exists among many, and it misses the entire point of their exercise. Attacks on rationality had been around well before Kahneman and Tversky—many people recognized that the simplifying assumptions of economics were grossly over-simplifying. Of course humans do not have infinite cognitive abilities. We are also not as strong as gorillas, as fast as cheetahs, and cannot swim like sea lions. But we do not therefore say that there is something wrong with humans. That we have limited cognitive abilities is both true and no more helpful to doing good social science that to acknowledge our weakness as swimmers. Pointing it out did it open any new doors.

Kahneman and Tversky's work did not just attack rationality, it offered a constructive alternative: a better description of how humans think. People, they argued, often use simple rules of thumb to make judgments, which incidentally is a pretty smart thing to do. But this is not the insight that left us one step from doing behavioral economics. The breakthrough idea was that these rules of thumb could be catalogued. And once understood they can be used to predict where people will make systematic errors. Those two words are what made behavioral economics possible.

Consider their famous representativeness heuristic, the tendency to judge probabilities by similarity. Use of this heuristic can lead people to make forecasts that are too extreme, often based on sample sizes that are too small to offer reliable predictions. As a result, we can expect forecasters to be predictably surprised when they draw on small samples. When they are very optimistic, the outcomes will tend to be worse than they thought, and unduly pessimistic forecasts will lead to pleasant but unexpected surprises. To the great surprise to economists who had put great faith in the efficiency of markets, this simple idea led to the discovery of large mispricing in domains that vary from stock markets to the selection of players in the National Football League.

Behavioral economists have now made important contributions to many domains of economics, and have utilized the findings of numerous other social scientists along the way. But none of this would have happened if Amos and Danny had not opened up the adjacent possible, by telling us about systematic biases.

Only people of a certain age will recall that when Danny and Amos began their work on heuristics, every social and behavioral scientist knew that their job was strictly empirical: you report only what people do and think. It was absolutely forbidden to be prescriptive—to say what people ought to do or think.

In light of this training, many people were outraged that Danny and Amos were making normative assertions about the way people should reason.

It's hard for most behavioral scientists to believe today that anyone, let alone a philosopher at a prominent institution, could have written in response to Danny's work that

"Ordinary human reasoning—by which I mean the reasoning of adults who have not been systematically educated in any branch of logic or probability theory—cannot be held to be faultily programmed: it sets its own standards."

This same philosopher—and many other philosophers and behavioral scientists as well—went on to try to show how Danny's reasoning about the problems he gave his subjects was wrong, and the reasoning of ordinary people was correct.

There are two main problems with this position.

1) There is no such thing as ordinary untutored human reasoning: there is an enormous range of approaches to problems like the ones Danny presented to people. And that's just among people in developed countries who have had no special training. When you look at people in other cultures the range gets broader still.

2) When you teach "ordinary" people rules like regression, the law of large numbers, and avoidance of the conjunction fallacy—either in classrooms or in laboratory settings—they don't give you static. They accept correction and try to reason in line with those rules. Danny's critics were placed in the position of a lawyer defending a client who had already thrown himself on the mercy of the court!

Early on in the debate, an article in Behavioral and Brain Science critiquing Danny's work was sent out to people who might reply to it. Surprisingly, almost no behavioral scientists took the side of the critics. And that was largely the end of it, except for a very few psychologists who continued the critique, often with a moralistic tone, and some psychologists who were ignorant of the details of the debate. Astonishingly, I've been told by a prominent philosopher that most philosophers still believe that "ordinary human reasoning" is without blemish and can't be criticized.

By far the greatest influence on my work was that of Amos and Danny. The research continues to guide my research and thinking.

I once saw a letter Danny wrote to an institution that was considering hiring one of the psychologists critical of his work. He wrote something to the effect that he expected to end up in the ashcan of intellectual history, but not at the hands of that particular psychologist. Danny was right about the latter, but not about the former. Intellectual history has been permanently deflected by his work.

For those of who studied economics at university, as I did in the 1970s, the work of Danny Kahneman and Amos Tversky came as a most welcome corrective. My first encounter with their work on heuristics and biases, while a doctoral student in the 1980s, was a transformational experience, a moment when the pieces of a larger puzzle are re-arranged and the world never looks quite the same again. The elegance of their experiments, honed and sharpened to capture precisely the phenomenon of interest, was such that even the field they challenged—economics—had to acknowledge the power of their findings. It's a rare duo that can so fundamentally call into question the received wisdom of a field and manage to get the results published in one of its leading journals, as was the case with their 1979 classic published in Econometrica, "Prospect Theory: An Analysis of Decision under Risk."

Even so, the application of many insights about judgment and choice, so neatly distilled in laboratory settings, has been neither a smooth nor straight road. The reason has less to do with shortcoming of the cognitive psychologists and decision theorists who conducted the studies, and more to do with the way others sought to generalize the findings without careful regard to the nature of real world decisions, which often involve circumstances that can be very different. Much of my research has been about precisely this: understanding the messy world of managerial decision making. For that, the research of Danny Kahneman has been an essential and firm foundation.

For years, there were (as the old saying has it) two kinds of people: those relatively few of us who were aware of the work of Danny Kahneman and Amos Tversky, and the much more numerous who were not. Happily, the balance is now shifting, and more of the general public has been able to hear directly a voice that is in equal measures wise and modest. 

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.