Daniel Kahneman on Behavioral Economics (Podcast)

and just so much wisdom and knowledge. If you liked Thinking Fast and Slow, which is about judgment error in humans in individuals, well, Noise is about how flaws and in judgment within broader institutions come about. And it's a totally different area and it's absolutely fascinating. I'm a big fan of behavioral finance in general, plus all of uh Danny's work historically. If you are remotely interested in this, then strap yourself. And this is another doozy with no

further ado. My conversation with Danny Koneman. This is mesters in Business with Very Results on Bloomberg Radio. My extra special guest this week is Danny Khneman. He was awarded the two thousand and two Nobel Memorial Prize in Economic Sciences, which he shared with Vernon Smith for his empirical findings the work he did with Amos Tversky. And what's so fascinating about that Nobel Prize is that Danny is a psychologist.

The work they did challenge the prevailing thoughts in economic theory by establishing a basis for common human eras his previous book, Thinking Fast and Slow, was the best seller of two thousand and eleven and one a variety of different awards, including the National Academy's Communication Award for Best Creative Work. His latest book is Just Out Noise, A Flaw in Human Judgment, which Danny Koneman wrote with Olive Simony and Cass Sunstein. Danny Kneman, welcome back to Bloomberg.

I'm delighted to be here. You always say call me Danny, and I always feel awkward and I feel like I should call you professor. But let me just get that call, all right, Danny. So let's start very basically. What is noise? How does it happen? And where does it come from? Okay, well, noise isn't accepted term in statistics. We talk about statistical noise,

which is variability, and that's where it comes from. We talk about noise a measurement, which is unreliability in uh in measurement, where measurements that should be identical turn out to vary. So that's the background in the use of the term as we use it specifically, we intend we speak about judgment noise, and this is the situation in which judgments should be identical people or the same individual judging the same object at different times, or different people

judging the same object. If they don't agree and I expected to agree, we speak about judgment noise, and in general, people are expected to agree when they're trying to be accurate. So when you have a group of people trying to make their best guess about the quantity, it could be the symptoms that somebody should should get for a crime. It could be the value of the company. It could be the premium that somebody should be charged. Oh, it

could be a diagnosis, a medical diagnosis. In all these cases, you might have several people looking at the same information making judgments. If they don't agree, there is noise, and noise is the topic of the book we wrote. So it's fascinating how we start to see noisy decision making come up over and over again in the same fields. And you just mentioned a few medicine, criminal justice, finance. Are there certain fields that are more susceptible two problems

in expert judgments than others? Or is it just that the results of those sort of noisy decisions are so much more significant than other fields. Well, we use the word judgment when there is room for reasonable disagreement, that is, you know, we don't use the word judgment for computation, and when compute station is appropriate, we wouldn't be talking of noise. We would be talking of people making mistakes. And we talked about noise when when it's a matter

of judgment. And and so the existence of noise by itself is not a surprise. Whatever the surprise is the amount of noise just a lot more then would be expected. And here, I think the best way to explain this is too to tell you the story of how I started to work on noise. Then where the whole thing began. So I was consulting in an insurance company, said than eight years ago, and I had the idea of running But today we would call a noise audit, that is underwriters.

To take one example, we had several underwriters, so some realistic cases, the same cases. They were constructed by executives of experts and underwriting, so they were completely realistic, and you might have fifty underwriters looking at the same premium. Now nobody would expect the numbers to be exactly the same. But I asked executives, if you take a pair of underwriters at random, by how much would you expect them

to differ in percentages? That is, you take the average or the pairer, you take the difference, you divide the difference by the average. What percentage looks reasonable to you? And they're answer typically with ten percent. And we have, by the way, we have surveyed hundreds of executives since then, and ten percent seems to be what we expect the reasonable difference to be, which is tolerable when two people

make judgments of a quantity. Now, the correct answer among underwriters in that company with sixty five percent more than five times as much as expected. That's the phenomenon. So we expect this agreement where judgment is involved, We just don't expect that much disagreement. And this basically was the observation that started us on that part of writing a book, because it turns out that you find astonishing amount of disagreement when you look for it, and you find it

wherever judgment is involved. So engineers who make estimates on the basis of objected data, they don't have a problem of judgment, but to the extent they do have a problem of judgment, you will expect a lot of noise. So that's the basic finding. And you know, wherever precision is important, wherever it is important to get to the right number, noise is a source of there. So where some people over estimating and others underestimating now making errors. Huh.

So let me roll back to that insurance company, which you discuss in the book, and there were two particular areas where we're there were these broad disagreements. The first was when people were trying to estimate the risk involved with some insurance and so how you price that very much determines. If you're too expensive, meaning you think it's a high risk, you're not gonna win the business. And if it's too cheap relative to the risk, well you'll

win the business, but it won't be profitable. The costs will be higher. And then on the other end, in the appraisal of hey, what are the damages here? Figuring out how much something should be covered by insurance, what the dollar amount is, and the same situation. You can't be too stingy or you lose customers, but you can't be too generous when you give the house away. How

significant a financial issue was this for the insurance company. Well, you know, it's not easy to estimate that exactly, but I can tell you the question that I asked some executives. I said, suppose there is a correct number, say for the underwriters, and and you have somebody who overestimates the underwriting cost by fifteen, how much would you expect that to cost the company? And the same question for underestimating by fifteen. In fact, fifteen percent on either side is

much less than noise than we had discovered. But people estimated on that basis that this could be in the hundreds of millions or billions of dollars. This is a very large company, so uh underwriters have a lot of important decisions to make claims, adjustice, make important decisions which are really consequential for the company. And errors of the

magnitude that we observe are costly. The main reason that they may be less costly is that if error is present in all insurance companies, if all insurance companies are noisy, then some of the damage to each individual company will be reduced. But that's the best that we can say. Well, one would imagine the insurance company that could reduce noise would find itself at a competitive advantage. Absolutely, there was something you had written that really stood out to me.

There's an assumption when you have noisy systems and everything from criminal justice to medicine to insurance, that these errors tend to cancel out. But you found out that noisy systems have errors. Not only do they not cancel out,

they tend to add up. Explain, well, if you have two separate underwriters estimating the same risk and you average their ratings, then the average will be usually more precise than the individual judgments because errors in measuring the same object do cancel out, but errors when you're responding to different objects do not cancel out. So if you overprice one policy and you underprice the another policy that doesn't cancel out, you've made two mistakes, and you know it's

the same thing with two with two judges. If one defendant is studish too much and another defendant is spunished too little. On average, you know, punishment was right, but two cares about the average two mistakes were made. So there is some confusion because people think about canceling out. But that happens when people evaluate, or judge, or measure

the same thing there and errors do cancel out. I recall a book a couple of years ago called The End of Average that looked at that exact issue and said, you know, we we tend to look at these averages as if anyone is experiencing an average. But what you're really saying is, hey, if it averages out to be the right answer, it means you have a lot of wrong answers. That's right. Averaging out to the right answer

is not a guarantee. And that is a nice example of the phenomenon we're discussing in the book, the neglect of knowledge. People really tend to focus on bias, which is the average era. But you can have a zero bias and the very poor performance if you have a lot of over estimates, and they love about the estimate.

Quite interesting. So one of the things in the book that I was so taken by had to do with the admissions committee for a university, and they used to have all the admission officers do a blind review and get together and try and hash out who they thought would be a good fit for the school and who

wouldn't um. But it led to a problem, and they started having the first person who who reviewed the application put their review number on the corner like they would actually put their rating on the page, and then hand it off to the second person. And you described this as the illusion of agreements in organizations. Tell us about that, Well, uh, you know, this is an experience as any teacher has has had. For example, when you're looking at the test booklet,

the student has written several essay. If you score a test booklet, you score the first question, then the second, then the third, then in general you'll find that your grades do not vary very much. On the other hand, if you read the same test across all students and write the score at the back of the of the booklet so that you don't know when you read the second question where the first question was, you will often be shocked by the discrepancy between the first and the second.

There is a mechanism by which people, if you gave a good grade the first time, you're going to be inclined to give the benefit of the doubt to the student if there is any ambiguos ambiguous answered. Exactly the same thing happens in deliberations. And in the example that we gave, the admissions committee used to operate in what we consider the correct manner. That is, everybody would individually make their judgments and then they would reveal all judgments

together and average them. But they changed the system so that now people spoke in sequence, and the question was asked, why do you do this, This is not optimal, and they say, well, we used to do it the other way. We used to have people prepare their judgments individually, but

there was so much disagreement that we stopped. And that's an example where people managed to avoid finding out how much noise there really is because when they when people are allowed to influence each other or influence themselves in the case of the teacher reading multiple booklets, when when judgments are not independent, they are less effective statistically, you

just have less information. Think of the example in which the first person to talk is the CEO, and then everybody agrees, then the agreement of other people is not informative. In fact, you had one person making the judgment. That's the extreme of abolishing, of eliminating the appearance of noise without eliminating the reality of not So it sounds like groups and corporations, institutions, schools, they seem to amplify noise. Is that just the nature of bigger numbers of people

working together that they're going to create additional noise? No not necessarily what happens in a group if they made their judgments individually, is not that noise is amplified. The true noise is revealed. So suppose you had underwriters. Suppose you had multiple underwriters judging routinely every every risk, then the optimal procedure would be to have them making independent drug ugments and only then then revealing the two judgments

and averaging them. That's clearly the optimal procedure, and and the optimal procedure reveals noise and then reduces it by averaging. But when a sail individual makes a judgment, that judgment will be noisy. And when individuals are allowed to influence each other, then it's more like a single judgment than it is, like having multiple judgments or the same opta. So you use the phrase naive realism, What what does

that mean relative to noise in groups? Well, what made realism means is is a statement which most of us are most of the time, that we think we're right, We think we have the right view of situation, we think we understand strengths correctly. In short, we see the

world as the world is. That's native realism. And if I see the world as it is, and you know, they are friends and colleagues looking at the same world, and I like and respect them, then naturally I assume that they see the world as I do because I see it right, and if I respect them, they see it right as well. So that's naive realism. And naive realism prevents us from becoming aware of the amount of noise that there is. We're just assume noise away. We

saw that very nicely among underwriters. You know, when you interview an underwriter, what happens to them? But how does an underwriter become expert in the absence of any feedback Because they don't they don't get any feedback from reality about their underwriting and what happens is that they become

increasingly confident, and largely because they agree with themselves. So when you agree with yourself a lot, and you think you're right, and you make judgments with increasing speed and confidence, so that makes you think that you're even rter. That's naive realism, allowing massive noise to occur with everybody convinced that they're doing the right thing, but in fact they may not be doing the right thing because as they were looking at the same problem, there would be the

food quite fascinating. So we become familiar with a particular area. That familiarity leads us to think that we're developing an expertise. We tend to make more snap judgments and without any sort of feedback loop, how can we possibly know that we're right? And yet that absence of feedback seems to strengthen people's self confidence. Do I have that right? And think of the number of situations in which exactly this whole there's a judge doesn't have feedback as to whether

judgment was correct or on bail judge. Sometimes there is feedback, but it's a symmetry. So bail judge may get feedback on somebody who was released and committed the crime, but the bail judge will never know if somebody was incarcerated would have committed the crime. So feedback is a massive problem. And many professionals at the minimum feedback, and yet they

become confident and they feel their expots. But in those cases there is a high risk of noise and a lot of that feedback seems to be only at the extreme. A bridge collapses, there are a plane crashes, somebody dies, there's someone out on bail commits a crime. What about all of the lack of a better word, near missus where there is a bad judgment, something happens. It's not quite as terrible as a as an airplane plane crash, and it it's resolved before there's damage, but it's pretty

clear the basic judgment was wrong. How does that affect a person's future judgment. Well, in situations where there are near missus, there is an opportunity to learn. And in world run you know, well run airlines and and and air traffic systems keep track very closely of near missus because those are their opportunities to learn without without tragedies. But in many situations you get no feedback at all.

In the idea of having senses in bridges that gives you a sensitive measurement of how much stress there is that necessarily recent there used to be very poor feedback on whether a bride would collapse or not, and in many situations that professionals make jugment on, there's no feedback at all. Quite interesting. So let's talk about this book,

which was a collaboration. What was it like working with those two gentlemen versus thinking Fast and Slow, which I kind of get the sense was you sitting down and putting a lot of your previous work into a context for public consumption. Well, writing Fast and Slow was mostly a very lonely experience, and writing with collaborators was really a pleasure. So it was it was a relief to be able to count on people to find mistakes to correct them, and and a lot of the text UH

was actually written by Olivier and by Kass. I had a lot to do with outlining and with critiquing and with rejecting drafts. But I was spared much of the things that I'm mostly traid of in writing. So it was a very good collaboration. And by the way, we benefited a lot from from COVID because that forced stuff into quite an efficient way of collaborating. We used to visit.

Olivier would come to New York from Paris, and I would visit Paris for a few days every month, and we had a very good time, but it wasn't productive. Zooming one or two hours a day turned out to be a much better way of writing the book. And this is what happened. Uh, it sounds like it was just a good excuse to get together in New York

in Paris and have a little bit of fun. Well, I mean, you know, we didn't think of it as a good excuse, but it turned out that would waste a lot of time and the fair amount of money. So you you mentioned you reviewed a lot of manuscript from Olivier and Cass and rejected stuff. You and Amos very famously would agonize over every sentence in all of your publications. You seem to have spent a lot of time writing meticulously and very thoughtfully. How has that evolved

over time? Is this a little easier to sort of be the orchestrator and the editor as opposed to, you know, just agonizingly putting down every single word. No, it isn't. I mean, my this is part of sort of my intellectual personality of character that I think most clearly when I find flaws in existing text, and I'm not good

at anticipating the flaws. So I see a flow and I correct it, and then there is new text, and then I discover a new flow, and and I tend to work that way, which is infuriating to make collaborators and wish for a lot of time and efforts, but that's the way I. On the other hand, I do tend to be very critical, and most of the flaws that I find do exist, so it tends to lead to a good project in a very inefficient way. So despite that perfectionism, you know, we all evolve over or time.

As you were preparing Noise, did you find any of your previous writings or research that you either disagree with or see from a different perspective or light when you're putting this book together? No, not really. I mean, in the book, we actually relied on ideas from Thinking Fast

and Slow, But the book is really fundamentally different. Thinking Fast and Slow is a book about individuals and about how and it was a book about the average or a typical individual and how the average or typical mind works. Noise is about individual differences. It's about the way that the different people think differently, and so this is a really different cut about thinking. It's a different way of

looking and thinking. So we did use some of the material, but the Noise is not a revision of Thinking Fast show. It is about the truly different topics that we didn't even touch and thinking it clearly, it goes in a very different direction, and it looks at some systems and some organizations that I don't believe you touched on in Thinking Fast. It's kind of interesting because we've already talked

about medicine and criminal justice and finance. There was one section I was fascinated by where you discussed hiring and promotions and how I don't want to use the word random, but how much noise is in that system and how unreliable many organizations hiring processes are. Tell us a little bit about that. Well, it terms that people like hiring by interviewing people and for me, a general image of

the individual that they're thinking of hiring. And it turns out this is not a good way of doing it. A much better way of doing it is what it's called the structured interview, the structured process where you accumulate information systematically about different characteristics of the person. That is less pleasant, it's it's less enjoyable, but much better. And better yet is having several in several people do the hiring, each of them forming an independence impression, and then they discuss,

then they average, and then they discuss the average. And this is the procedure for example, and and it's about state of view. But many places are way short of a state of the art. I should add that state of the art. Hiring doesn't mean that you're guaranteed the perfect sit There's so much there's so much luck in the world. There's so much uncertainty that the person to how it may be very good but may run into difficulties with the boss doesn't like her or something like that.

And by chance alone you can get a lot of variety. Chance, by the way, is not noise. Chance is something that happens in the real world. Noise is differences among judgments. So hiring is buy and love really very poorly done. And it's very poorly done because it doesn't control noise. Quite fascinating. So the book goes over how noise affects judgment and how it introduces a variety of errors into our institutional decision making process. What can we do to

improve that process? Well, in the book, we we introduce a concept that we call deci isn't hygiene And you know the word is that particularly appealing. It's intended to drink to mind the image of washing your hands. And there is a contrast between the biasing and the certain hygiene. The bias thing is like medication or like vaccination. It's specific to a particular disease. When you wash your hands, you don't know what germs you're killing, and if you're successful,

you'll never know. So the certain hygiene is oriented to improving decision making an avoiding errors, specifically avoiding noise, but incidentally also avoiding bias without knowing precisely what biases you're trying to control. And we have a variety of procedures that we think of as the certain HyG Give us a few examples. What what are some of the procedures. Well, I'll give you an example that has to do with

the certain making. So suppose you are making a decision and so step you one will tell you is you have to consider your options and have the best possible set of options. But now you come to evaluate options, how do you do that? And here actually our advice, we have a slogan we say options are like candidates. You should think of options in the same way that organizations are in our advised to operate when they hire candidates.

And we were talking about that earlier structure, the thinking, break up the each option, look at the various aspects of it, make assess these aspects in the fact based way, to the equivalent of interviewing somebody about different aspects with her character or her experience, and then create a profile of all the information you have about that option, and

only then invoke intuition. That there is a key principle of decision hygiene is not to avoid intuition altogether, but to delay it, because intuition is way more effective if it is preceded by a period in which you accumulate information systematically. So that's an example. I have many others, but this is when and there were quite a few in the book. There were some things that really surprised me about that decision making process. How people's moods affect

their decisions, even the weather affects decision making. We are essentially different people at different times. Oh, yes, that is there are different sources of noise that we talk about. So there are three of them. Are truly that one of them is what we call within person noise, and that is that the individual is indeed making different judgments depending on circumstances that irrelevant. So it's true. There is

evidence that mood really affects the way that people think. Uh, people tend to be more creative when they're in a good mood, but they tend to be also more gullible and they are more critical when they're in a bad mood. So mood affects the way we think, and it also affects we're more prone to see good things when we're in a good mood. Mood is important. There is evidence that judges who pass sentences on criminals are more severe on hot days, and they are more severe if their

football team lost the game last Sunday. So there are a lot of irrelevant events or circumstances that influence our judgment. This is one of the three major sources of judgment. Let's get to the other two. What are the other two sources well, and one other which is easy to think about. It's very easy to think about it. In terms of judges. Some judges are more severe than others, so their sentences on average are more severe than the sentences of other judges. That's one aspect, and the same

as to by the web underwriters. Some underwriters write large premiums on average, and other underwriters write small premiums on average, So there are differences of that kind. But it turns out that the biggest source of noise, and that came

as a surprise to us. The biggest source of noise is that people really don't see the world in the same way, so that different judges have different tastes in crimes and some tastes in criminals, so they somebody may be particularly severe about repeat offenders that somebody else might be with extremely lenient, say about white color crime, but really upset by violence. And it turns out that there

is we call that a pattern noise. That is, each judge, each individual has a pattern of judgments which are this is different from the pattern of judgments of other people, and that is the major source of noise. And people who are consistent in that way. So for example, suppose you're a judge and somebody reminds you of your daughter, whether that makes you more lenient or more severe, probably more lenient. Now on another day, that same person would

also remind you of your daughter. So this is not noisy within the individual. This is a characteristic of the individual, but no other judge shares it. And it turns out that this highly case specific distances in attitudes that are difficult to pin down. They are noise. Judges have personalities and judgments differ as much as personalities too. And then what is the third source of noise that you identified

in the post? But those are the three are differences in average level for judge's severity, differences in taste what we call pattern noise, and within subjects, within person variability, we call that occasion noise because on different occasions you make different judgements and it's to some of these three sources of noise that that creates. That's the noise that we observe in the system. All three operate on any

particular judgment. So I'm gonna ask the question I was thinking about a little differently based on what you just said, what fields seem to manage reducing noise better than others. And are there any fields that are especially susceptible the noise. That's a very good question to which I do not have a very good answer, because in our work we we found noise wherever we looked for it. Indeed, our summary conclusion is wherever there is judgment, there is noise,

and more of it than you think. You know this is this has been our conclusion. So we haven't found places that control noise very efficiently. The only way, by the way to get rid of noise, and that's really quite important is average judgments. Take multiple judgments of the case and average them, and this mechanically eliminates noise if you have enough judgments the average. It may be biased because averaging there's nothing to reduce bias, but it eliminates noise.

So that's a pure far way of eliminating noise. Is averaging multiple case very interesting. Let's let me throw a curveball at you. If you were designing a system to introduce noise to short circuit human judgment, what would you create to make judgment less effective noisier. I don't think I would do things very differently from the way that they have done in many institutions. Now I would I

would let people make individual judgments without feedback. That's that's all that's needed, make their individual decisions without feedback, which is a situation that's very common, and that will create a lot of noise eventually. And noise is reduced by feedback. Sometimes it's the feedback of other people. So case conferences can be arranged to some extent control noise. But you know, you you don't have to try very hard to create a lot of noise. I think the existing organizations do

very little to control noise. So let's talk a little bit about ways to control noise. And you describe a difference between rules and standards. Tell us about that. Well, Standards is a way of when you say, for example, that your obscenity is something that you recognize, so there is a standard to avoid obscenity that you do not define it. That's a standard. A rule is more precise than that, and it does you specifically what you have

to do, and rules, if followed, they're like computations. The computation is a is a rule, and rules tend to eliminate noise. Standards sometimes reduce noise, but standards do not eliminates because they so the seven words you can say on television is a rule, but pornography, I know when I see it is a standard. Is that the difference

nicely pre firstly quite quite interesting. So so, given all of the work you've done over the years, all of your research, you seem to have continually identified flaws incognition, flaws in human judgment. Has this affected the way you view other people? Do you? Do you turn around and say, wow, these this species is a terrible decision making apparatus or

is it something less comprehensive than that? No, I've actually for my career, I've been interested in intuition and intuitive thinking, and I've been interested in that's A lecturer used to give many years intuitions marvels and flaws, because intuitions is marvelous. Intuition is marvelous, but it's also flawed. And it's true that I have found it more interesting to study the flaws of intuition than it's marvels. And there a lot

to do to correct the flaws of intuition. But to say that this has turned me into a pessimist, or that they dislike people because their minds are flawed I think the minds are pretty marvelous, but they are certainly far from perfect, right, So, so you're focusing on the small bits that we get wrong. But overall we managed to navigate through life pretty effectively. Well, we certainly managed to navigate through life. And you know it's it would be absurd to focus on the floors of the human

beings when you can see what they're capable of. On the other hand, if you want to do things better, then you'd better focus on the floors rather than on what is going well. You know, one of the things you said when we spoke last about Thinking Fast and Slow, I asked you about your own investing process, and you said, despite knowing everything that you know about you human decision making, you still catch yourself making the same sort of mistakes

that everybody makes. Is that still the case? Do you still feel that way? Oh? Yes, I mean, you know, I've been at it for more than sixty years, and

I'm really not better than I was. In general, my thinking has been And it was true when I wrote Thinking Fast and Slow to just focused on the individuals, that the hope of improving thinking is in organizations, because organizations think slowly and they have procedures, and it's by imposing procedures, by adopting procedures, that you can improve things. And in the case of noise, we have a procedure that we recommend to get started, and that's measured knowledge.

If you're in an organization where you have multiple people making the same judgment and no very good feedback, conduct what we call the noise audit, give them the same problem and look at their solution. We predict that you'll find more noise then than you think you will. That's that's our prediction, and that's some that's a recommendation to organizations. It's not something that you can recommend to an individual.

Quite interesting, I have to ask you before we get to our favorite questions, what's the next project, what's the next book look like? What is tickling your curiosity these days? Well, that's actually back to a topic that I was working on but years ago, and I have almost by accident and back studying well being, and I'm involved in several research projects. None of them is as big or ambitious as Noise was, or thinking fast and slow, but all

of them are quite interesting. So I'm not bored. I can't picture you board because you always seem to have a lot of different things going on. Let me ask my favorite questions that I ask all of our guests, and let's start with, what are you doing to stay entertained during this pandemic lockdown? In addition to working on the book? What are you streaming? What are you watching on Netflix of anything? Oh, I've been watching several series,

several very good series. Let's see the last ones. There is a political series on Netflix, Le Bon Wi, which is a French political thriller that is very good. There is a Danish political series Borgan, that is very good. I am now watching Babylon Berlin about Berlin in the nineteen twenties, which is excellent. And so I do mind watching me will I exercise? And I exercise a fair amount. But so I've seen a lot of series since, well, from for the last few years. So baum Noir was

the French one. What was the Danish one? Borgan? Borgan is Bridge Actually the Danish one. Bogan is a thriller. It's a Scandinavian swiller. There is a Danish one about a woman prime minister, and it's not Borgan, and I not block on its name, but it would be easy to find, and I really recommended it, is sup all right, I will I will check that out. So let's talk about your early mentors who helped to shape your career. And I guess we have to include collaborators in that

as well. Well, I mean there were There's been one major influence on my career, and it was in Sisk. The collaboration with him completely changed my life. And uh, and it changed the way I do things, but and it gave me Yeah, it changed my life and it was the best period of my life too. Professionally. The thing I recall from Michael Lewis is undoing project is that people said, you guys would lock yourself into an office or a classroom and all they would hear all

day long is peals of laughter coming from within. Is that true? Is that an exaggeration or did you guys know that's really not an exaggeration. Amos and I worked very closely together for about twelve years, and we spent many hours a day together. And he was very funny. He had an excellent sense of humor and he loved laughing,

and in his presence I also became funny. So we were amusing each other and the field that we studied, uh, was was one that was ducive to luster because we were looking for mistakes in our own thinking and to trap ourselves or to see that you are attempted to make a stupid error. That is quite funny. And that's the game that we engaged in in studying judgment and in studying decision making, was looking for errors in our own thinking. And that was very amusing, I can imagine.

So let's talk about books. What are some of your all time favorites and what are you reading right now? Well, I would say my all time favorites of recent years with Sapiens. I think it's many people's favorite book by Valli. I read it twice, which is something that I really do. And right now while I'm reading the new edition of Nudge, which is coming out I think in August, and it's

called Nudge. The final edition by Dick Taylor and Catherine Steam was in and it's quite different from the original note which appeared I think indo peplem an eight uh and it's what but it it had the same characteristic that not said. It's wise and it's funny, right, Dick said, it's about two thirds new and I think that's August four that comes out the other's right. I happened to be reading that right now, August three. I'm looking at a message from him. He's um. He's a very amusing

person to begin with. And if if you're telling me the book is funny, then I am really looking forward the book is. You know, he just he can't help himself. He's funny all the time. He's my best friend, my best living friend. Let me ask you this question. If a recent college graduate asked you for some advice, if he was interested in a career in either psychology or

behavioral finance, what sort of advice might you give him? Well, you know, I tend to refrain from advice because I don't believe I have a crystal ball into the future. I can tell you what I would have been doing if I was starting today. The fields that are very exciting from my perspective are neuroscience, including neuroeconomics, which is the neuroscience of decision making, and artificial intelligence. I mean, in those two areas right now, there are extraordinary developments,

very exciting. And so when you see that and they're attracting massive talent both areas so you know that for the next decade or so they'll be cooking A lot is going to happen. And what happens after that, I have no idea. And in our final question, what do you know about the world of psychology, g behavioral finance economics today that you wish you knew fifty or so years ago when you were first getting started? Oh? Well,

so much has been learned. I you know, if I I can't say that I wish I had known earlier. Has been so much fun to find out over the years, both in my work and in the work of others. So I can't think of thinking that would have made me act de simply. But all I can say you to you is, oh, yes, things have really changed and so have been in that field. Huh quite fascinating. Thank you,

Danny for being so generous with your time. We have been speaking with Danny Kahneman, whose new book Noise, A Flawing Human Judgment, was co authored with Olivier Simone and Cass Sunstein. If you enjoy this conversation, check out any of our previous four hundred such interviews. You can find those at iTunes, Spotify, Google, Bloomberg dot Com, wherever you get your podcast each week. We love your comments, feedback and suggestions. Write to us at m IB podcast at

Bloomberg dot net. You can sign up for my Daily Reads at Ridholts dot com. Check out my weekly column on Bloomberg dot com slash Opinion. Follow me on Twitter at Ridholts. I would be remiss if I did not think the crack staff that helps put together this conversation each week. Tim Harrow is my audio engineer. Alatico val Bron is my project manager. Michael Boyle is my producer. Michael Batnick is my head of research. I'm Barry Riholts.

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