Daniel Kahneman and Cass R. Sunstein: National Book Festival 2021

[Music] sponsored by the james madison council welcome to the 2021 library of congress national book festival i'm gaul beckerman i'm an editor at the new york times book review and i'm thrilled to be here today to talk with daniel kahneman and cass sunstein about their new book noise a flaw in human judgment hi gentlemen hello good to see you so so let's let's get right into it because we don't have a a lot of time and i want to ask first the probably the most essential question to understanding this book and your work in it um maybe cass you can take this first one and daniel you can jump in uh what is noise uh and and how does it get in the way of decision making and if you can give us some of the wonderful examples that you have in the book to help us understand that would be great thank you for that noise is unwanted variability and judgment so if you go to a doctor who says when she's let's say energetic and ready to go in the morning let's do a ton of tests i think you have a problem and when she's tired let's say at the end of the week at the end of the day she says go home i don't think there's anything to worry about that's not an egregious case of noise but it is a case of noise an egregious case of noise is when whether you get let's say five years in jail as a sentence or a month in jail as a sentence depends on a lottery who is the judge who's assigned to sentence you or noise is a case where you get hired with enthusiasm by a firm to which you've applied and someone who's just like you doesn't get hired just because that's another person who let's say has a different noisy relationship to you compared to the first so unwanted variability judgment within ourselves is all over the place and in systems it is rampant and it is a pervasive source of error and inequality now isn't it and and daniel you can take this one i um isn't it always true that there's going to be variability and consistency because we're human how do you distinguish between the things that are systemic you know so for example you know one of your examples you just mentioned it you know doctors you see doctors are more likely to order cancer screenings for patients they see early in the morning than late in the afternoon that seems to be something once you figure out that that's the case you can sort of you can you can balance that you can find a way to to solve that problem but then each doctor you know comes their work with their bias and their worldview so that seems not so much a systemic thing but just a question of subjectivity how do you distinguish you know what what is the noise that that can be sort of dealt with versus the noise that is just human beings being human daniel do you want to do you want do you want to answer that one or um there's going to be variability whenever there is judgment and you know our statement on that is that wherever there is judgment there is noise and what's most important there is more noise than you think because the reason we wrote the book is that when you look at the judgments that people make when they're fairly complex judgments not when they're elementary and by judgment i mean they can't be computed there is a reasonable answer but no expectation of perfect agreement you'll find more disagreement than expected and it's you know whether it's human or not of course it's human but that's no excuse when you have a judicial system you would want the judges who are part of the system to speak in one voice the insurance company would like its underwriters to speak in one voice and similarly the hospital wants the physicians in the er to speak to you to speak in one voice so that the individual who makes the judgment and the particular state of mind in which that individual is if these are material if these have an effect on the decision that is made that's that is undesirable that's unwanted and this is the noise that we're talking about right maybe you make a distinction in the book between occasion noise and system noise um cass can you explain that what what the differences between those yes so occasion noise might be that when you're very excited and happy about something you will say i want to take the job i want to buy the house i want to go on the date but when you are not so excited because let's say your favorite sports team lost or because the weather's terrible outside you'll say that's i shouldn't buy that house or i shouldn't go on that date or i shouldn't take that job so occasion noise means that small features of the background often like whether boston red sox won which i think everyone should care greatly about can affect your judgment about things that are unrelated to baseball like what you're going to do with your life in the next month or what your judgment is about something that really matters so that's occasion noise a system noise is the noise that goes across systems when you may have let's say one doctor who's really great and orders a ton of tests and tends to over diagnose and another doctor who's also really great but tends to under diagnose and is really worried about putting people through the wringer so then a hospital understood as a system will have the noisy if there's a lottery who's the doctor who attends to level noise is meaning as a degree of severity on the part of one set of judges hanging judges and lenient judges who were very generous to criminal defendants that can lead to great differences within the system of criminal justice and this is per pervasive in business with respect to hiring promoting recruiting etc and the problem of system noise isn't what principally concerns us though occasion noise is really fun right um i just want to remind uh viewers before i go on that you can ask questions uh in the q a function and we're going to get to that in about 15 minutes we'll take your questions um you're both sort of getting at this uh but this question that i have but maybe just to kind of pin it a little bit more clearly on the wall why is consistency important why why is it and and i guess more specifically what feels of our life are consistency important because i can also imagine and we can get to this to an extreme uh of of consistency and uniformity so what so in what areas of our of our life i mean you've begun to mention the criminal justice system um maybe education where do we really need to think about sussing out uh this problem of of noise um daniel do you want to take that one yeah uh well you know the answer is in professional governance uh most when it's an organization that is making judgments there should noises truly undesirable and this is true not only in the judicial system in the medical system it's true in the patent system it's true in hiring it's doing personal evaluation it is true actually in fingerprint reading because there is noise even there and so the answer is wherever there is judgment there is noise now we do not mind diversity and we welcome diversity in many situations we don't care we don't we want variability we don't want all our all our book critics to agree we don't want all our film critics to agree we certainly want variability where creativity is required and we want variability in the process of reaching a decision so if you have a group reaching a judgment a common judgment we would want we welcome diversity in that discussion but we would want the conclusions to be in general not noisy and and that is true really when you speak about professional judgments in most professional judgments variability uh system noise is going to be noxious right right so so let's get to the kind of solution part of this uh cast i wonder if you can talk us through what it what is the notion of decision hygiene um and and give us some examples of sort of how do we how do we clean up the noise in in in these various fields let's back into that uh if we might so uh the uh the the character in this particular play we haven't mentioned yet is bias and bias is a little bit of the charismatic performer the taylor swift of decision theory whereas noise is we won't name a very uncharismatic rock star that you've never heard of because they don't get attention uh bias is systematic departure from what's right as in a scale that shows you as consistently heavier than you are that's my scale a noisy scale is one that is all over the place that sometimes shows you is lighter than you are and sometimes is heavier than you are okay with respect to decision hygiene which is the uh set of remedies we have for the problem of noise the good news is the decision hygiene helps with bias too it tends to contract bias as well as new ones and let's give a couple of examples decision hygiene is a way of kind of washing judgments so that it combats enemies that you may not be able to identify in advance and one way to do that is to have guidelines guidelines they cut noise dramatically and if they're good guidelines they'll cut bias too so when a little kid is born in the united states there's something called apgar spore it's pretty simple it's a guideline and it cuts both noise and bias simultaneously the second thing you can do is really simple and it's intuitive and in medicine we see it which is ask for a second opinion and aggregate the two now if you've got more than two to aggregate that's better so if you get a bunch of independent judgments and add them up the majority of the average will be much less noise than if you take each one as individually and under reasonably pervasive circumstances if you do that you're going to cut bias as well i was interested about in one of the sort of tools that you described daniel can you talk about decomposing a decision what is it what does it mean to decompose a decision well uh an example would be a very elementary example would be when you are hiring for a particular position then uh decomposing if you are going to interview someone decomposing would be first constructing a list of the attributes that you require for the job and then in the interview you would focus on each one of these attributes one at a time and independently of the other and you would attempt and you would delay your intuition you would delay the global judgment about the person until you have covered all the attributes now more generally when you're facing a decision with multiple options you can see any option as a candidate and options have attributes that make them more or less desirable and evaluating attributes one at a time and delaying global evaluation are recommended steps for options more generally as well as for hiring candidates and certainly similarly for evaluating candidates or evaluating people the performance of people you would want to you would want the person doing this to be thinking of separate events of separate attributes of performance and not to jump to a global journal now i'm wondering sort of the role what you see as the role of technology in solving some of this problem some of these problems of noise uh you know specifically when we think about kind of algorithmic judgment and sort of removing removing the human variability um first let's talk about sort of the the positives you know is this is is do do you actually see uh some hope in in in technology's ability to uh reduce the noise cass noticed in the last period that when one says something positive about algorithms people tend not to smile or jump for joy and one when one says something negative about algorithms people do tend to nod not merely a sense but in please descent i'm going to take a risk here and say something positive about algorithms the first thing that algorithms do is they eliminate noise they don't cut noise they eliminate it it's like a scale that always spits out if not the right answer the same answer so if you have an algorithm to predict let's say whether people are going to flee the jurisdiction if they get bail or an algorithm to predict whether someone is going to be in a job in three years it's going to give the same answer whether it's in a good mood or in a bad mood whether it's tuesday or thursday it won't be in a mood and there won't be any system noise now i think uh the human mind does not get thrilled to hear that but it should be a bit happier than it is because cutting noise is other things being equal a really good thing it tends to cut error a lot think of a scale that's noisy it's all over the place the errors add up and cause total error in terms of joyfully misperceiving themselves as thin people and depressively misperceiving themselves as overweight people those are both errors and they add up so algorithms do eliminate noise whether that and that's very good they can also compare to human beings and i'm using the word can not will be less biased along any dimension that you care about they might be less subject to cognitive biases let's say like unrealistic optimism they might also and this is very much in a work in progress be less subject to other forms of bias which are uh very familiar these days let's say okay and and if we can talk for a second about the flip side i mean there is i imagine you can also imagine the extreme too of the sort of a dehumanizing process that that can happen if people are just turned into numbers uh daniel well in general uh people are not in favor of algorithms when they compete with humans so there was a there is a film that i recommend to everyone by the way it's called alphago and it's on netflix and it describes a competition between a program and the world champion of one of the best world players in go and all of korea was transfixed and and there was on game four the human it beat the machine it was one of five games with the human prevail and the interesting thing is everybody was joyous including by the way the team that constructed the program uh they were clearly thrilled to see the human defeat the machine so we have a very strong prejudice against algorithms there there they're going to be resisted there's going to be a lot of resistance and and there are going to be very tricky rearrangements to be made where very good algorithms there is going to be a temptation to replace judgment by algorithms and sometimes it's going to be a good thing to do and other times it's going to be a bad thing to do and one thing that you know it's going to be very complicated and this is coming those kinds of decisions and those kind of difficulties are coming and uh and clearly having algorithm replace humans is not is not problem free it's going to cause massive disruption as it occurs right um i have a i have a sort of a meta process question which you know is with my with my book reviewer my book review hat on uh somebody who looks at books every day lots and lots of them uh what was it like to work collaboratively how did that come about i was sort of surprised and thrilled when i saw the three of your names right you're just you're two of of of three authors of this book um can you can you explain to me i guess maybe a little bit how it came about but also uh what the process was of working together on this um because go ahead i think i first became interested in noise about seven years ago i think and then about five years ago olivier and i uh began collaborating on something that could become a book but we didn't really believe that there would be a book and then uh and then let's see three or four years ago now a cass became clearly interested and seemed to be interested in joining us and we were thrilled because as soon as uh we knew that cass was on board we knew that there was going to be a book because he is a force of nature and uh it was he was going to make it happen and the collaboration was you know as collaborations are uh we sometimes disagreed we mostly agreed and we found and we found a good way of working together in part by separating topics and in part by joining forces and really working cooperatively on on each chapter and we had both forms of cooperation i'm curious where the disagreements were i mean i imagine the general concept of noise was one that you all were on board with but um what's fascinating is to imagine sort of where where you actually found misalignment well the disagreements were not so much on content that they were on the level on exposition and and how many readers are we going to lose when we go into technicalities and how essential it is to have technicalities and to speak to an academic audience as well as the public audience so that was a balance that we we were constantly struggling with each of us within himself and frequently we disagreed on that and eventually we reached consensus great i think um sorry cassie did you want to add something yeah we really had fun with the book i should say and the overwhelming experience of the collaboration for me at least was fun and joy and laughter and part of the joy was danny is the master of seeing problems in his own pros and analysis and he's almost as good at seeing problems in his co-author's pros and analysis so the constant uh 5 am notes saying i don't know why i danny would write you haven't seen until now the fundamental flaw and then maybe at 5 28 am he'd say i think i might have a solution and that didn't happen a little and i'm not nearly as good at that as he is but i'm pleased to say that toward the last stages some parts of the book that were pervaded by detailed analysis of law and policy it occurred to me late that no one's interested in this except for me and so i caught them well let's get to with our remaining time let's get to some questions from the audience uh one question that i'm interested to hear if you guys did research on this specific point but on the question of law enforcement noise in the world of law enforcement was is there is there any uh insights that you were able to come to on that on those questions yes so that uh if it's not explicitly called out of the book it's implicitly there so the subject of randomness with respect to law enforcement is very much part of the background and in some places the foreground of the book where when you think about noise you might think about racial discrimination which isn't quite that but it certainly if understood a certain way in the same family and the fact that and this does come up for the end of the book whether under a vague law people have an unfriendly encounter with the police it's often like a lottery as well as it having a racial and class dimension there's a lottery and and that's a problem and a big question is what are we going to do about it right right um i have a question here uh somebody named julie staple we are at a moment when a significant portion of the population is proud to make decisions based on no information at all can your analysis speak to that daniel you want to take that one really i don't think that we have really had to that issue we are really focused on professional judgments and the judgments of professionals who do what we assume that they do want to reach the correct decision this is the assumption we're making and we assume they want to use information and noise happens in spite of their intentions to do a good job when people prefer to be led by their emotions and and are just as accepting of misinformation as a valid information we have very little to say about that let's just say about that right right um another question here noise sounds a lot like statistical quality control how does your work differ from or go beyond and then the questioner mentions deming jiren uh i think other social scientists um is that is there something to that that you're looking at at things that are adjacent to statistical quality control daniel you can answer that if you yeah yes yes the answer is absolutely uh what we deal with is effectively quality control that is uh and and the basic idea of decision hygiene is to achieve discipline thinking and to discipline judgment which is the asset that is quality control and and it's a reduction of noise by the way in quality control because when you eliminate noise you can see bias and then you can correct bias but eliminating noise by achieving some uniformity is actually an early step in quality control and an essential move um we have a question about whether uh suggestions for for readings for those interested in reducing and identifying and reducing noise do either of you have any good reading suggestions for for people who are want to dig deeper into this topic well there's a book called noise that has something called producing noise and that book i'm pleased to be able to say has a lot of footnotes that deal with particular areas so if you're interested in recruitment and hiring or if you're interested in medicine something that we're not keenly interested in or if you're interested in law and policy we have something like a bibliography that can be extracted from the footnotes um let me ask a question of my another question of my own here i'm curious about bias training and and sort of the whole this whole world of of which a lot of institutions have sort of taken on over the last year uh with a lot more intensity how do you see that sort of converge or diverge from from this world of identifying noise i mean that seems fairly subjective as opposed to kind of the systemic kind of thing that you're looking at but is there is is it a is it a is it a good development that that institutions are making us more aware of of things like our own bias with regards to this big question that you're looking at certainly but the bias that we talk about in the in this context is a particular kind of bias it's a bias against social groups or ethnic groups or genders and those are not the cognitive biases when we talk of controlling noise we're talking mostly of the cognitive side not about the emotional side which is the one that's being tackled in bias training right um another question here what happens when one of the noisy decisions is actually the correct judgment so they give us an example the 2008 stock crash where the majority expert opinions were wrong can you speak to that cast well if you have a scale and you weigh yourself a thousand times and one of the times it's right that's uh a positive thing but you'd be lucky to be able to figure it out maybe you have an accurate skill that can tell you about the noisy scale uh the problem with the noisy system or a noisy individual like an astrologer who sometimes is right is that it's very hard to identify in real time which of the assortment of judgments is the accurate one and that's why decision hygiene is so crucial as a noise dampener that can typically get you closer to what's accurate right um okay i'll just i'll ask one more just to kind of close this out here which which you know as you look out on the various fields that you were examining where noise is a factor um i guess which in which where is it most consequential you think uh in our world today that we figure out how to eliminate noise or just most a question of sort of if not life or death but you know um really could make an impact on the way on the way people and the way people live you can daniel you can start well i mean clearly there is noise in policy making so for example you can see that different jurisdictions different countries different regions have different rules about covid and they're essentially the same situation and that's noise and it's not necessarily a good thing although in this case perhaps there is something to be learned from it i would say the life and death is clearly medicine and and law are obviously urgent problems and in both of those we would want to make rapid progress add anything to that hse health safety and the environment taking them as a unit would be the area number one for noise reduction and decision hygiene okay well thank you both um it's a fascinating book uh i enjoyed reading it and um i think there are a lot of important lessons for how or how society can function better than it is i appreciate you joining us thank you all for your questions thank you [Music] so you

Channel: Library of Congress

Views: 59

Rating: 0 out of 5

Keywords: Library of Congress

Id: afgt5tPJ1LY

Length: 30min 13sec (1813 seconds)

Published: Tue Sep 21 2021