2015 Personality Lecture 21: Performance Prediction

so the first thing I want to show you is this thing called a gon board now the the this was worked out by Francis gton who's actually quite an interesting person you'll you'll if you hear about Francis gton you'll often hear that he was the first person who tried to measure human intelligence but that's actually not true what golden tried to do was measure human Eminence and he thought of eminence as I suppose something like High position in the cultural dominance hierarchy and this was back in Victorian England in the late 1800s and so Eminence would have been intellectual achievement Financial achievement um cultural achievement achievement broadly speaking but really considered within the confines of a dominance hierarchy so at that point in time the English regarded themselves as the prime nation and ethnicity on the planet and then the aristocratic Englishmen regarded themselves as the prime human beings in that Prime country and ethnicity and so it was really an idea that would be more associated with with dominance hierarchy uppermost dominance hierarchy position than anything we would conceptualize as intelligence today and gton got gton was a a polymath he was a genius in many many fields and he got very interested in the idea of measuring human differences and uh he tried to determine whether the differences that he could measure and some of those things were like reaction time and height and and uh um physical fitness and um I can't remember all the other things that he attempted to measure but he wanted to see if any of those things could be used to predict Eminence um turned out that they couldn't partly because Eminence is not the kind of category that you can make into a scient ific category um and partly because his measurement instruments in many ways weren't sensitive enough he didn't make enough measurements and the statistics weren't sufficiently sophisticated but the idea that you could measure Elementary attributes and that you could use them to predict something important was still an important idea and it's one of those examples in science sort of like phenology I mean everyone makes fun of phenology now you know the idea that you could read someone's character by mapping out the protrusions and dips and so forth on the on their skull it sounds ridiculous to Modern years but there was again there was some idea behind it and that was the idea that cortical functions could be localized and that cognitive functions could be differentiated into separate functions we still do that we think of emotions and motivations and personality traits and intelligences even though intelligences is not a very good idea the idea that that you can differentiate um human psychological function and and then that would be related in some way to the underlying neurological and physical architecture that's not a stupid idea so an idea can be very intelligent at one level of analysis and not so intelligent at the other so I would say the problem with the phrenologists was actually a problem of operationalization they had the theory right in some sense but they didn't have the measures right um now one of the things gton came up with was the normal he came up with this thing called a gon board and a gon board demonstrates how a normal distribution is produced now basically what you the normal distribution is a an axiomatic it's an axiom of modern statistics and every system has to have its axioms and the Axiom of normal distribution is basically the idea that around any measurement there's going to be a set there's going to be variations in measurement and those measurements are basically going to be random and then another axum is that extreme outlying measurements are going to be rare and small outlying measurements are going to be more common so you can imagine um if I measured any one of you 100 times with a with a let's say with a tape measure I'm going to get a set of variables that are not always exactly the same and so there'll be an average and then there'll be some deviation around that average and if I looked at 100 measurements that I took of each of of one of you then I would get a normal distribution around that that distribution and that's sort of the fuzziness of the mean now that might be partly because my measurement instrument isn't very accurate it might be partly because at some point when you're being measured for the 50th time you're slouching a little bit or maybe other times you're standing up a bit straighter or maybe I measure you in the morning sometimes and in the evening at other times and you're actually taller in the morning than you are in the evening because you're spine has a chance to decompress at night so maybe that adds half an inch to your height in the morning and so there's there's going to be shifting and movement around the around the central tendency around the average now if I turns out if I measure all of you the same thing's going to happen what we're going to get is an average height which would be the average height of the people in this room and then there's going to be a distribution and basically a normal distribution those of you who are much taller than the average are going to be much fewer than those of you that are close to the average and those of you that are much shorter than the average are going to be there's going to be far fewer of you than there are going to be those of you who are just somewhat smaller than the average now generally the idea of a normal distribution is predicated on the idea that the variation around the average is actually random and that's an important thing because the variation around an average is not always random now psychologists will tell you and and so will most social scientists who use classical statistics that the normal distribution is the norm or maybe they'll say more than that is that it's the standard case that whatever set of variables you measure is going to come out in a normal distribution and that means that you can apply all of the stats that you're going to learn to decompose the world and reconstruct it to your data sets because the data sets will fit the assumptions of the of the um statistics the problem with that is that it's often wrong now I ask you guys to buy the Black Swan and we haven't talked about the Black Swan much yet and I'm not going to test you on it but I would recommend that you that you read it especially if you're interested in Psychology because you got to watch your axioms and the idea that the normal distribution might not always be correct and certainly might not be a correct description of your data is a fundamentally important idea because if it's wrong if your data isn't normally distributed then the phenomena that you're looking at isn't what you think it is and the statistics that you're going to use aren't going to work now this came as quite a puzzle to me at one point because it turned out that when we when we produced The Creative achievement questionnaire um you know and you'd think I would have known better by this time because it was it wasn't that long ago it was like 15 years ago the data never came back normally distributed it came back distributed in a in a what they call a Paro distribution which is everyone Stacks up on the left hand side and the the curve drifts off to the right and that's one of those curves where almost everyone has zero or one and a small minority of people have very high scores now when you get a Pito distribution there's something going on that's not random and that doesn't mean you can ignore it now one of the things that psychologists will do sometimes if they get a distribution that's not random is they'll do a mathematical transformation like a logarithmic transformation to pull in the outliers and to try to make the data fit the normal distribution again assuming that there's some kind of measurement error or that the scale's got a logarithmic function um it isn't always obvious that that's a useful and appropriate thing to do because it's the case in many situations where the extremeness of the distribution is actually an accurate representation of of the way that that phenomena behaves in nature now I want to show you how a normal distribution works Works um have any have you ever have any of you ever seen a demonstration of why a normal distribution is normal and why it takes the shape it takes has anybody ever seen that no it's weird e because you'd think that given its unbelievable importance especially in Psychology where everything we do is measured and has an average and a standard deviation that there'd be some investigation into why we make that presupposition there's a great book one book I would recommend for those of you who might be interested in psychology as a career there's a book called a history of statistical thinking now you'd think Jesus if there's not anything more boring than statistics it's got to be the history of Statistics but it turns out that that's really wrong I mean the history of Statistics is actually the history of the social sciences and not only of the social sciences because statistics became not statistics was actually initially um invented by cosmologists who were measuring planetary position and then the statistical processes that have been used to underpin modern science have been tossed back and forth weirdly enough between social scientists and physicists over about the last 200 years because the physicists ended up having to describe the world from a statistical perspective right because if you go down into the realm of the atomic and subatomic particles what you find is that things behave statistically down there they don't behave deterministically and it's the same at the level of the of the human scale you know we behave statistically not deterministically so you know sometimes you hear this old idea that that psychology has physics Envy or something like that but it turned out that for much of the history of the development of statistical ideas physics had psychology Envy which I think is quite funny so anyways if you look at this book histo history of statistical thinking you can see how the idea that populations could have behavior and that populations could be measured and that the idea that states like political States could be measured and that human behavior could be measured and I didn't realize until I wrote I read this book that um how revolutionary the idea of Statistics actually was because of course it's part of the idea of measurement so I had this very interesting client at one point he was an old guy and he'd been a psychologist and a financier and a statistician and he was in love with mathematics you know he was one of these guys who who for whom mathematical um equations had this immediate glimmer of Beauty and he made this very interesting little gold sculpture that I have up at my office that that's a representation of what people claim to be the most perfect mathematical equation ever constructed and he so he made this little Gadget it's like a religious icon almost and and that's really what he thought of it and um uh I'm afraid foolishly enough that I can't tell you the name of the equation some of you might know it it it relates i e and Pi is there anybody in here who yes what is that e to the I pi yes and what is that equation okay and do you do do you know why it's such a remarkable equation says e in one equation right so he thought that that in some sense this equation summed up the magnificence of the mathematical universe and so was so funny because he also made little pins that people could wear of this of this equation that were also made out of gold and there was another person in our apartment at that point and uh I was wearing this pen around and she said oh well she told me what the equation was she pointed at that and she got all excited because it was this perfect equation that related all these fundamental constants to one and zero so anyways he taught me a lot of this while he was a client of mine he was very much obsessed with ma mathematical ideas he couldn't get them out of his head and it was a true Obsession and a useful one but he had he taught me a lot of things about statistics that I just never knew at all and uh one of them was the Pito distribution which which just it just staggered me that I didn't know it I only learned this stuff about five or six years ago and it just made me feel like a complete because I'd you know gone through me immen immense amount of psychological training and I was measuring things like prefrontal ability and intelligence and personality and creativity and then in the creativity measurement I stumbled into these Pito distributions I thought they were bloody mistake I didn't know what the hell they were you know and I've also found economists who didn't know about the Pito distribution which is pretty bizarre thing like it really is a strange thing so anyways one of the things he showed me is how the normal distribution comes about and uh I had a student who did a PhD thesis on gton so I knew something about gton at this point he was he was doing a part of his thesis on the history of the measurement of intelligence and uh gton was a key figure in the establishment of that sort of measurement so um but this is one of the things gton invented so let's take a look at it [Music] here okay so that's your basic rock and roll go gon board apparently so let me show you a picture of of a gon board so you get a better sense of exactly what it is doing so there's a good one right there uh let's see if we can make that a little bigger Yeah so basically all that happens with a gon board is that you have a you have a bunch of pegs on a board it's board is horizontal and you drop balls down at marbles or whatever it is and the marbles come at in one place and then they Bounce Down the pins and they distribute themselves in a normal distribution now the reason they do that is because there are far more ways of getting down the middle than there are of getting to either side now see see because to get to the only to the right side say the ball has to go right right right right right right right right and then fall in the little cup and then on the left it has to do exactly the same thing there's only one way it can do that right right but to get into the middle it can go left right left right left right right left Etc there's a there's a far greater number of Pathways for the ball to go from the middle down to the middle than there is for the ball to go to the sides and so all that means fundamentally is that the probability that a given ball is going to land in the middle or near the middle is much higher than the probability that a ball is going to land in the extremes and so it's just a description of how random processes lay themselves out around a mean okay so that's a normal distribution now if you have normal distribution one and you have normal distribution two you can overlap them right and if they overlap perfectly the mean and the standard deviation are the same then there's nothing different about them you know because one thing you want to ask is well are how do you know if two means are different and the answer is you don't know unless you know what the means are measuring and you don't know what the means are measuring unless you know the variation of the measurement and so you have to know the mean and the standard deviation so the standard deviation is like the width of the mean in some sense and all statistics do generally speaking is take one normal distribution and another and slap them on top of each other and then measure how far apart they are how much they overlap and correct that for the sample size and then you can tell if the two distributions are different significantly different so what you would do is you'd run a whole bunch of normal distribution processes by chance and you're going to get a distribution of distributions in some sense and then you can tell what the probability is that the normal distribution that you drew for group a is the same as the normal distribution that you drew for Group B and that's all there is basically to to Standard Group comparison statistics and that's how we decide when we have a significant effect if there's a significant difference between two groups or better still if you randomly assign people to two groups and then you do an experimental manipulation and then you measure the outcome the means then you can test to see whether your experimental manipulation produced an effect that would be greater than chance and then you infer that well the there's a low probability one in 20 that you would you would um produce that effect if you were just running random simulations and therefore you can say with some certainty that there's an actual causal effect that's with an experimental model now you know why do you think all right let's say let me give you a conundrum so I used to study people who were sons of male alcoholics okay now there was a reason for that we didn't have women why might that be we didn't use women why confounding why why would they be confounding apart from the fact that they're women that's supposed to be a joke you know Jesus it's a better joke than that okay well it could be that there's some sort of like a emotional cing mechanism that differ between the way that men deal with father daughter relationship is different than father okay so that that's a POS that's not a bad answer but it's it's not the answer that was appropriate for our research and I'll tell you why because you couldn't infer it from the question we were interested in what factors made alcoholism hereditary because it does seem to have a strong hereditary component and what we basically concluded after doing a tremendous amount of research was that people who are prone to alcoholism at least one type of person who's prone to one type of alcoholism got a very very powerful stimulant effect from the alcohol during the time that their blood alcohol level was ascending in the 10 or 15 minutes after they took a drink especially if they took a large drink fast or multiple large drinks fast you can probably tell by the way if you're one of these people if you want to go do this in the bar the next time you go go on an empty stomach take your pulse write it down drink three or four shots fast wait 10 minutes take your pulse again if it's gone up 10 or 15 beats a minute look out because that means alcohol is working as a psychom motor stimulant for you and we found that for many of these people that was an opiate effect what seemed to happen was that when they drank alcohol fast they produced probably beta Endor although we were never sure we it can be blocked with an El treone which is an opiate antagonist anyways the other characteristic of that pattern of of of alcohol consumption is that the the real kick only occurs when you're on the ascending limb of the blood alcohol curve so you know first of all your blood alcohol goes up and then it goes down and generally when it goes down it's not pleasant that's when you start to feel hung over and a hangover is actually alcohol withdrawal by by the way so it's like heroin withdrawal except it's alcohol withdrawal and it's generally not pleasant for people so they usually sleep through it or it puts them to sleep but if you're one of these people who get a real kick on the ascending limb of the blood alcohol curve then you can just keep pounding back the alcohol and it'll keep hitting you and keep you in that position where you're you know on the on the ascending part of the blood alcohol curve and you can probably tell if you're one of these people if you can't stop once you get started you know so if it's like you have four drinks quick and it's like man you're gone until the alcohol runs out or until it's 400 in the morning or till you spent all your money or you've been at the last bar in town or that you're sitting on your friend's bed after everybody's gone home from the party and you're still drinking you might suspect that you're one of those people and if you are one of those people well then you should watch the hell out because um alcohol is a vicious drug and it it gets people in its grasp hard and it's hard for them to escape once they do people also drink to to quell anxiety so now the problem because we were looking at the genetics of alcoholism it wasn't easy to study offspring of alcoholic mothers and the reason for that is they might have consumed alcohol during pregnancy in which case and that's a bad idea especially there's certain key times in pregnancy where even a few drinks are not good and that's turns out if I remember correctly that turns out to be the times when the fetus is producing the bulk of its hippoc cample tissue and so anyway so if it turned out that daughters or sons of female alcoholics were markedly different from the general population in some manner we wouldn't be able to tell if that was a consequence of alcohol consumption during pregnancy or if it was it was a genetic reflection so what we wanted to do was study sons of male alcoholics and so their mothers actually couldn't be alcoholic and we wanted their fathers the best subjects had an alcoholic father and an alcoholic grandfather and at least one or more alcoholic first or second degree male relatives and they couldn't be alcoholic and they had to be young so because obviously if they were 40 and they still weren't alcoholic then they probably weren't going to be alcoholic so we wanted to catch them you know between say Well it had to be 18 which was it was in Quebec that was even a little late probably but you know that's best we could do from an ethical perspective so we used to bring these guys into the lab and get them quite drunk um the National Institute of alcoholism and alcohol abuse pretty much put a stop to that research because we used to bring them in and you know we'd get their blood alcohol level up to 0.12 or 0.10 it had to be pretty high actually looked like the real physiological effects seemed to kick in when uh when people hit legal intoxication so you don't really get the opiate effect till you pop yourself up about 008 which was the legal limit for driving at that point um so we used to get some of these guys were pretty big they'd come in there maybe 230 lb guys and to get them up to you know 0.1 or02 you had or 0.12 you had to give them quite a bit quite a whack of alcohol and then what we usually do is we let them sober up till 06 about that and then we'd send them home in a cab well when the niaa got all ethical on the whole situation they wouldn't let us send them home until they hit 002 was like well if you're 240 lb and we've just nailed you with alcohol so that your blood alcohol level hit 0.12 you're going to be sitting in our bloody lab bored to death feeling horrible for like 6 hours or 7 hours and You' be pretty damn irritated about it it's like wasn't obvious how we were supposed to keep the people there it's like well can I leave no I'm not paying you if you leave it's like that's going to produce real positive outcomes with like drunk people in the lab that's going to work really well of course then they'd never come back either because it was such a bloody awful experience so I stopped doing that research partly because because it became impossible anyways we did find a lot we found out that there was this one particular pattern of of alcohol abuse that seemed to be hereditary so and we couldn't study it women anyway so now there was a problem with this line of research and the problem was well alcoholism comes along with other problems so this was correlational research right we' take we we we' pick a group of people and match them the sons of male alcoholics we'd match them with people who weren't sons of male alcoholics so they were still Sons they were still the same age but they're fathers who weren't alcoholic now here's the problem what should we match them on age gender well you can't match them on number of drinks obviously because you want the people who are alcoholic in one group not to be you don't want alcohol I in the second group so do you match them on number of drinks well if you don't then you don't know if the effect that you're measuring is a consequence of the genetic difference or on the number of drinks per week or drinks per occasion that the people had right that would be a confounding variable if you match them for antisocial personality or anti person antisocial personality symptoms because lots of people who are alcoholic have they tilt towards the antisocial side of the spectrum so do you control for that well you don't know because you don't know if antisocial personality is part of alcoholism like it's part of the same underlying genetic problem or if it's a secondary manifestation or if it's a consequence of drinking you don't know any of that do you match them on depressive symptoms do you match them on schooling do you match them on education do you match them on personality do you match them on other forms of Psychopathology do you match them on what they drink do you match them on how many drinks per occasion they drink when they drink Etc well the answer to that is you don't know and so what that means is there's actually an infinite number of potential covariants because you don't know what differences there are between the two groups are the differences that are relevant to the question at hand now that's actually one of the big problems with psychiatric research in fact it might be a problem with psychiatric research that's so serious that it cannot be solved you know so if you take kids who are attention deficit disorder say which is a horrible diagnostic criteria and you match them with kids who aren't and then you look to see what the what makes the ADHD kids different what do you match them on well you don't know and that is so usually what happens is the people who do psychiatric research finesse this a bit they match them on the important variables age physical health maybe education but the PO the problem is you you actually have no idea what the important variables are and there are an unlimited number of them and so that's actually why random assignation to groups is so important now if I take all of you and I say well um let's look at the effects of alcohol so what I would do is I'd say you'd come towards me and I'd do a g and board sorting you go to the left you go to the right you go to the left you go to the right this is non-biased separation of the two populations and then maybe I'd give one group 2 ounces of alcohol in water and the other or in Coke say and in the other group I'd put a few drops of alcohol on the top of a glass of Coke so it smelled like alcohol and tasted like alcohol when you had your first drink and then I put you through a whole battery of neuropsychological and personality tests which I did by the way to a bunch of people when I think it was the first publication I had back in about 85 or something like that um random assignation gets rid of the necessity for the infinite number of covariant right because you're all different in a whole bunch of important ways but we could assume as long as I threw half of you in this group randomly and half of you in that group randomly that all your various idiosyncrasies no matter how many there are would cancel out and that's the massive advantage to rad mass sination now one of the things you're going to notice when you go through psychology especially if you do psychopath logical work is that the studies are almost always correlational and they do they'll do they Co they'll control for Relevant covariants that's what it'll say in the paper but the problem is you don't bloody well know what the Rand the relevant covariants are if you did you'd already understand the condition you wouldn't have to do the damn research so you can make a pretty strong case that all psychiatric research that is studying psychopathological groups compared to at control it's all not interpretable and it turns out in that kind of research that who should who who the control should be is the killer cuz you don't know picking the the psychopathological population is easy you just pick them according to whatever diagnostic checklist you happen to use but when you figure out who to control them against it's like siblings I mean what do you what do you do what do you how do you match a population the answer is you can't so anyways the reason that we didn't include women was because of the of the um un the uncontrollable potential con confounding effects and the only way that you can do that is to assign randomly and that's an important thing to remember it's why experimental designs are way more powerful than correlational designs and the problem is frequently in Psychology what you see are correlational designs now I think if you're careful and you dig around and and you know you're obsessively careful you can extract information out of correlational studies but it's no it's no simple thing to do you see this problem come up too when you hear about studies on diet you know it's like well you should eat a lowfat diet or you should eat a high fat diet or usually what happens is they track people across time who hypothetically have been having one diet or the other but the problem is you don't know what the hell else makes those people different and the problem with that is there is an infinite number of potential things that make them different and that's a big problem the probability that you've picked the one thing that makes people who have a high fat diet different from other people and that it happens to be that they have a high fat diet and that's the only difference it's like yeah no not not at all definitely not so okay so anyways random assignation gets rid of the problem of the infinite number of covariants and that's worth knowing and then the other thing that we've just figured out is that you know if you make a measurement you're going to get a distribution around the measurement and if you do that in two groups you can check out the overlap between the two groups groups and you can determine what the probability is that that overlap is there as a consequence of chance now you might say why not set your probability level to like one in 10,000 so you could be absolutely damn sure that the two groups don't overlap you know so why wouldn't you do that why wouldn't you set P equals 00001 instead of P equal 005 so that would be 1 in a th instead of 1 in 20 yes you guys conclude that know that that's when is that's exactly right so you're basically damned if you do and damned if you don't which is a very important thing to remember about life because generally when you make a choice of any sort there's error and risk on both sides of the choice right it's really really useful to remember that because people always ask act like their current situation is risk-free which is never ever the case yeah so people we trying to figure out well how do we balance the risk of finding something that doesn't exist against the risk of not finding something that exists and the reason it's one in 20 and why is that it's because someone made that guess 40 years ago or 50 years ago and it's just stuck there's no reason and so the other thing to notice is that you want to be careful about the P equals 05 phenomena because one of the things you'll see is that people treat any experimental result they get like it's significant if the probability is 005 or less and it's not if it's 006 or greater and that's not not smart because the cut off is the cut off is arbitrary you need to know three things to interpret an experimental result it's like you it's like you can't calculate the area of a triangle without knowing three things I think it's three things anyways um you need to know the number of people in the study you need to know the size of the effect so that would be the relationship between two or more variables that you're interested in looking at and then you need to know the probability that you would find that effect size among a population of that size by chance and you cannot interpret one of those numbers without the other two it's not possible and now psychologists the stat statisticians among psychologists who have a clue have been jumping up and down for 50 years trying to get psychologists to report all three of those every time they report anything effect size which seems logical right the effect size is the difference between the two means divided by the standard deviation of the entire of of the of the pool Group Well you need both of those because it's the standard deviation that tells you how damn big the me the numbers are because what does 70 mean well it doesn't mean anything 70 it's like if I just came out here and said that you I could say 70 and 40 are those different well what the hell does that mean it doesn't mean anything you need to know what the units are and the distribution gives you the units and so once you know the units are you can say well here's how big the difference is between these two groups and here's the probability that that would be acquired by chance and so that's how confident you could be that it's an actual difference and so you want to always report effect sizes and most of you are going to be taught to look at the damn probability and that's stupid it's like when you want to know how how tall how much difference there is in height between two people you want to know how much difference there is in height you also might want to know to what degree that's there because of chance but it's the effect size that's the critical variable now you can't understand the effect size without understanding how many subjects were in the experiment and also the probability that you would acquire that by chance so you want to keep that in mind and when you're reading scientific papers you want to be looking at the effect size how large an effect size is this now I all the effect sizes are you can transform one into another so you have correlation coefficients which go up to one you have the square of the correlation coefficient which is the amount of variance that's accounted for and that also goes up to one and you have your basic correlation your basic correlation coefficients or sorry your your standard deviation effect sizes which is mean one minus mean2 over the standard deviation of the entire group and so large effect sizes in Psychology I've talked to you about this before our correlation coefficients of about 0.5 or above or R squar of 25% of the variance which is. 5^ squar or um uh mean 1 minus mean 2 of say half a standard deviation or greater and you can derive the conclusion that those are relatively large effect sizes by looking at the distribution of the effect sizes across the published literature and most of you will be told estimates for effect sizes that are actually way too large because those were guesses too how big's a large effect size well some statistician guessed 50 years ago so it's just like the 05 rule it's arbitrary it doesn't mean it's stupid but it's arbitrary and you don't want to get stuck on it like it's like it's some sort of fact okay so now let's look at um parito distributions I do okay the first thing you're going to see about a Pito distribution is it's definitely not normal now one of the things that I mentioned you before but I want to hammer this home because it's it's unbelievably important for determining how to understand the way the world lays itself out and how to interpret the way people distribute themselves in terms of their success across time now this is a this is a fundamental law and I I'll show you how it works okay so the law is most of you get nothing and a few of you get everything okay so that's the law now you might say well that's because of you might attribute that to various things so for example if you're a left Winger you attribute to it to the inequities of the social structure and if you're a right-winger you basically attribute to the fact that well most people are you know not that good at anything so it's no wonder they end up with nothing so they're not very smart and they're not very hardworking so they don't get very much of whatever it is that they're after okay so we'll take that apart a little bit but before we do that I want to show you an animation now you got to watch this one carefully oh yes that figes okay yeah this thing moves very very quickly so we'll go back to the beginning and all right now so here's the deal each of you gets $10 okay so that's a non-random starting place right you're all starting in the same place that's definitely not random okay now here here's how you play this game you flip a coin you turn to your partner and you flip a coin and if uh it both comes up heads then you win and they lose and if it comes up tails and heads then you lose and they win and if the winner gets a dollar from the loser okay and so you can imagine that this is a simulation so basically it's like your Apes trading bananas and if you if you if you uh give away a banana then you're done with the banana so one of you will walk away with 11 and one of you will walk away with nine and then you turn to someone else you just wander around the room and randomly trade okay so what happens well the first thing that happens is this okay so we started the graph with everyone at 10 now you've done a bunch of Trades um we don't know how many trades you've done but a fair number and so what happens what you see happening is some winners are starting to emerge right on the right hand side those are the people who won every single trade maybe they've traded 10 times so now they have 20 and then there's the people on the other side who've lost nine out of the last trades now what what happens when you lose nine what what's the big problem that you have what happens you only have one dollar left so what happens if you lose another trade that's right you hit zero and zero is not a number like any other another number zero is like the black hole of numbers you fall into the zero hole and that's it you're not in the game anymore and poverty is like that it's like that it's a kind of a trap it's very very difficult to get out of so and it seems to be in part because once you fall off the charts enough a bunch of things start conspiring against you so for example um you don't have enough money to buy a large amount of decent food cheaply so you have to buy expensive junk food in the short term so that might be one possibility or let's say you end up on the street well you can't even get a job then because you don't have an address and you can't get Social Security because you don't have an address and like things start to conspire against you very badly or maybe you're unemployed and you've been unemployed for a year and a half because it's been a prolonged downturn in the economy well if you're 17 who the hell cares but if you're 50 that might be that for you right you've hit zero you've been out of the market for 18 months nobody's going to hire you and so you've hit zero and the problem is we don't know what the hell to do when people hit zero it's difficult to pry them out of zero and throw them back in the game now you could say well what if you just rearm them with money and that would actually work if the game was truly random but it isn't obvious that the game is truly random and that's where things get weird Okay so anyways by this point in the game there's some winners piling up and there's some losers piling up what what's the difference between the people who have 19 and the people who have one well we know the one people they're going to be wiped out they got a 50% chance of hitting zero what about the 19 people uh uh they've got a much better chance of not hitting zero because they they' have to fail a l more right exactly so they're in this place where they're basically sitting pretty they can failed nine times and that just puts them back to where they were to begin with so okay so we keep playing well so you see what's happening is the with repeated random play the normal distribution turns into a pital distribution and most of the population Stacks up at zero okay this old guy I told you about he had a theory of social structure that was predicated at on this there's a bit of a Marxist twist to it he thought that what happened is that so if you ran a game long enough most people will stack up at zero and a few people have everything okay so let's say now you're down at zero what's your best strategy well you might say well we should reset the game right because if you're at zero and it's basically a random game if you wipe out that game and you put a another one in place there's some probability that when the next game starts playing you're not going to end up at zero and that was his theory of revolutions fundamentally once the once the game had played itself out until resources were maximally distributed it didn't cost the people at the bottom anything to be revolutionaries because they had already hit zero and so one of his hypothesis was that one of the things that political and economic systems have to do they have to figure out how to do is to make sure that the people who end up on the zero side of the distribution don't have nothing to lose because if you have nothing to lose God only knows what you're going to do next now it's a big problem because merely shoveling money down there is not likely to change the outcome very much yes yes I was about to say that sounds a little bit like the gambling policy though like if you just once you reset the game your probability of ending up at zero is exactly the same the first time yeah but the the probability that any given person will end up at zero is the same but the probability that you'll end up the same isn't because at the beginning of the game you have just as much probability of moving up to the top as you do of moving down to the bottom once you're at zero though you don't get to play anymore so at zero your probability of moving up is zero whereas at the beginning your probability of moving up is 50% so when the gambler's fallacy is merely that if you keep dumping good money after bad you'll win it back you know because let's say you've lost 10 times in a row you think well I've lost 10 times in a row it's 50/50 I must have a virtually 100% chance of winning the next time it's like well no you don't because probability doesn't have any memory that's the gamblers fousy so all right now one of the things we might ask ourself is this is something that I that I discussed with him in length because he really thought of this as a random process and you know because he he thought here it's very very interesting and very complicated it's like we know for example that the IQ distribution in this room is approximately normal and so we could assume that it's a consequence of random factors okay so what what do we mean random factors well there have been random genetic things going on in your lineage since life began and here you are you know you're smarter than him perhaps why well we're going to eliminate the environmental effects for now just forget about them we're going to assume that everybody's been raised in an environment where they had enough to eat and where they have enough resources um informational resources so that their intellect can capitalize so none of you people have been starved for information or food so I would say in many ways the important variation in the environment has already been ironed out for most of you not completely okay so it's random occurrences in the past in the evolutionary history that's put you wherever the hell you are in the IQ distribution so you're the Ben beneficiary of random forces so he thought of the problem as being random all the way down to the bottom now the problem I had with that is wait a second we have evidence that some things predict where you're going to end up in the distribution and so what are those things well IQ conscientiousness emotional stability openness if it's a creative product and then some other smattering of Personality features depending on the particular domain so for example if you want to be a salesperson some extroversion is you is useful if you want to be a manager it tends to be better if you're disagreeable to some degree rather than if you're agreeable if you want to be a nurse or a caretaker then agreeableness is useful but the big performance predictors across time are intelligence and conscientiousness now if the damn game is random which the statistics seem to indicate or at least that you can model it using processes that model random processes why in the world do IQ and conscientiousness we'll just stick with those two for now why in the world do they predict success so does anybody have any ideas about that let's start with IQ I mean for example if you apply your IQ to the stock market if I distribute you all a bunch of money and I and I measured your IQ and I said okay put together a portfolio you get to pick 30 stocks you can't sell them for a year you get to sell them at the end of the year um what's the probability that the high IQ people would pick a better set of stocks than the low IQ people the answer to that as far as I can tell is zero it's not it's not going to because you can't pick stocks as it turns out all the information is already eaten up so I and and the stock market's an interesting analogy of the environment right you know what I mean because the stock market is basically an index of the continually transforming human economic and social environment it's basically random you can't predict the damn thing even if you're smart so why is it that smart people win across time and then again why is it that hardworking people win across time if we imagine each Peg on the gon board as like a choice point in life then I think the people with a higher IQ are more likely to choose the choice point that will lead them you know no but that's only a restatement of what I just said it's not a causal account you're basically saying that the high IQ people make better choices yeah but that's if it's a random environment how can they I'm thinking instead of money we can view it as opportunities and genetics which make somebody adoptable in multiple environments and each coin flip as each subsequent generation of that lineage so that there is an increase like the more good adaptable genetics emotional stability and situational wealth you have the more likely your next generation has you know like they have to lose a lot more to get to the zero okay so what what about a given individual though forget about forget about it playing across time and that's also a weird thing e because some of the single- celled organisms that you that were your ancestors 3 billion years ago are still single celled organisms whereas here you are you you know same environment roughly speaking so there's this tremendous branching of possibility across time and you might say well IQ is adaptive which is a terrible word word adaptive it's like yeah okay so how do you account for all the single- celled organisms they're just not that bright but there they are there's there's more of them by weight than there are of us people by weight so I puzzled this out a bunch of different ways and you can you can think about this and see what you think I mean the first thing that seems to me to be the case is that you don't have to play one game you know like imagine that you're sitting around with your friends and there are 50 board games going on and you can keep a hand in in each of them well then at some point in one of the board games you're going to start to amass some success right just by chance and if you're playing 50 board games and 25 of them after playing for some time you're going to be at the top of the you know you're going to be moving towards the top 50% rather than the botom we might say Okay Play 50 games play 20 turns throw away the ones that you're doing the worst it the 5% that you're doing the worst it and then just keep doing that until you end up with the one game that you win and maybe you can do that better if you have high IQ CU you're faster so maybe you all you can all that happens is that if you're smart is that you can play many games faster and as a consequence of that you can choose the ones that you seem to be winning and stick with those and then maybe what happens with conscientiousness is that you actually stick to them so that means that you actually don't have to predict the future in order to master it it means that you have to simulate multiple futures keep your options open and sort of play dynamically as the environment unfolds so now it might be more complicated than that because it's also possible that in some environments at least for local periods of time the the game is actually not random you know so it's it's funny like do you think is Monopoly random what do you think you've all who everybody here has played Monopoly I presume right is there anybody who hasn't okay so what do what do you think do the smart people win monopoly more often I think it's the people that ahe you do you think so you would say it's the conscientious people that win more often what each move will be to yeah well it's pretty clear that you can you can lose stupidly in Monopoly right although I'm not sure if you can win intelligently you know you can avoid obviously bad choices like one choice would be don't buy any property and just hold on to your money that seems to be a losing strategy right so that'll wipe you out so maybe another thing that smart people can do is that they don't do things that are self-evidently that that make it certain that you'll lose it might be something like that so all right so one of the things we might ask ourselves too is given that IQ and and conscientiousness do predict success how much success do they predict across time and the answer to that that is they predict a substantial amount if you combine IQ and personality I'll show you the equations these were formulated in the 1990s so we're going to look at this here so um let me walk you through it a bit so this is a spreadsheet I put together quite a long time ago get this a little bigger so can see it all right so here's wh here's the elements of the of the equation so you need to know first element in the equation how many people so we're going to say well how powerfully can you predict the performance outcome of people across time and the the the value we're going to use is dollar value and dollars are basically they're sort of like a they're obviously they're the universal currency because you can trade money in for virtually anything so we use dollars as our standard of value um the first question might be well how many people do you want to predict for so let's say we're going to take 10 so we put that in this part of the equation here 10 people then you might say well over what period of time do you want to calculate their success and one decent answer is five years because people seem to stay in the same job position or career position for about 5 years Al although that's shortening okay so that's another thing that you guys should keep in mind you're probably not going to be in the same position in your life for more than about 5 years at a time and so you have to plan and plot for dynamic transformations in your career and you have to do that in a way that um that that works to your advantage which partly means you have to keep op options open and you have to be able to say no so okay the next thing you might ask yourself is well how high a predictor what's the r for your predictor what's the correlation coefficient and we put together a neuropsychological battery um I published the results of this with a with a with a someone named Dan Daniel Higgins quite a long time ago we used neuros pychological battery looking at dorsal lateral prefrontal ability which we thought of as something potentially separate from fluid intelligence which proba but which probably just turned out to be quite a good measure of fluid intelligence and we also used conscientiousness and we found that when we validated that in a we we we validated it in a factory and and we looked at the performance of the administrators and managers in the factory because they had relatively complex jobs and we had access to seven years of their performance records which someone else had gathered independently of us and what we found was that for those people who had performance records that were more than 4 years old we could predict their performance at about 0.59 about about 6 tremendously powerful prediction now one of the things we found was that the degree to which our predictions were accurate increased with the increase in the number of years of performance data we have so for example if I want to predict how you do next year and I get a performance in you you enter a new job and I get a performance index after your first year I'm not going to be able to predict that very well well from your intelligence and your conscientiousness because the measure of your performance actually won't be very good because it turns out that you can't even figure out how well someone is performing in a job until about 3 years so it turns out that if you take a complex job on you get better and better at it over 3 years if you're going to get better and better at it and so we can't tell how well you're doing until about that period of time so that's another thing that's useful to know by the way when you guys go off to find your next complex role in life you you can't really expect to be good at what you're going to do until about 3 years after about 4 years additional experience doesn't seem to matter so that's also worth knowing so you're going to feel like a bloody for the first bit of your new job and that's because you are but you you if you keep at it you'll acrw um experience and and and expertise quite rapidly over a threee period and that's probably the right amount of time over which to evaluate your performance cuz you need know that right should you be freaking out if after 6 months you're not doing a good job well you got to see how you're doing compared to your peers hopefully you're not doing a worse job than them but if it's a complex job it's going to take you a long time to master it so okay so anyways our predictor was 0.59 which we're pretty damn thrilled about and we comparing that we're going to compare that to a a comparison predictor of zero for now zero Is Random so if I was going to predict your trajectory trory through life let's say I want to predict your industrial productivity I'll take 10 of you I'm going to I'm going to predict your industrial productivity over the next five years that's that'll be the goal um or we can do this we'll do this a slightly different way um let's see so that's the selection ratio the last one is the selection ratio so let's say um I wanted to select you guys for a position that a well let's say you're going to be managers in a in a in a relatively complex corporate organization there's about 10 of you or something like that in here now maybe let's say 100 say I'm going to pick 10 of you that means my selection ratio is right there it's one it's it's 1 in 10 that you can transform that into a standard score of 1.76 now it's important to know how many people you get to choose from because if you're going to hire one person and you only have one applicant there's not a lot of sense doing any selection because you only have one applicant so I don't care how much you know about that person it's not going to be helpful if you have to hire them but in this situation we're going to assume that I can screen all 100 of you and only recommend 10 okay so we're going to assume you have to put in a variable for job complexity because the relationship between intelligence and conscientiousness increases the relationship between intelligence conscientiousness and predictive power increases as the job becomes more complicated which is exactly what you'd expect right so if you can do a job by rot all that IQ and conscientiousness will predict is how fast you can learn it but if it's a dynamic job where you have to make decisions on a Non-Stop basis then IQ and conscientiousness are going to predict your performance over the long run so we're going to assume that you're in a complex job we're going to assume that you have an annual salary of $75,000 so then the question is armed with that knowledge how much money would the company that's using my products obtain by using the selection compared to with if they just pick 10 of you randomly and the answer is right there in five years they would have made $4 million more than they would have if they would have just picked randomly and that would be a productivity increase of 104% so there that's graphed here so with random selection 50% of the people you hire are going to be above average and 50% are going to be below and using a good psychometric battery you can get that to about 8020 and the consequence of that is $4 million in increased productivity so now we're going to turn that into one person so over five years if you use selection processes properly you make $400,000 more by hiring one better person right it's $40,000 it's what is that it's $100,000 a year it's actually more than you're paying them in salary and now the reason for that there's a bunch of reasons the first reason is is that there are tremendous amounts there's tremendous differences in individual productivity now one of the things that's weird about these formulas is this formula is predicated on the idea that productivity is actually normally distributed but actually productivity is pedo distributed so what that means is that the basic consequence of using this sort of prediction is probably higher than this formula indicates now there's a variety of reasons to know this one is obviously the Practical idea the Practical idea is of what use is it knowing something about the people that you're going to hire and the answer or work with and the answer to that is it depends on what you know but if the things that you know are valid and they include personality and intelligence it's not only valuable to know it it's virtually vital because the success of your Enterprise will depend on the people that you hire and associate with the second thing is I think that knowing this should change the way that you look at the world if you understand that the outcomes in life are distributed on a pedo distribution and that there are inbuilt temperamental factors that play an important role role in determining that one of the problems that you have before you as modern people is what the hell do you do about that from a conceptual social political and economic perspective because nobody who's currently considering how societies are structured pays any attention to this sort of thing they don't assume that there are differences between people with regards to their life outcome chances not of this sort of magnitude and so none of the social policies that we have in place reflect any of this and so I would say like our psychometrics are 21st century and our political and economic theories are basically 17th century and it's not a good thing and you guys are going to suffer for this or benefit from it because as Society becomes more technological and as it transforms more and more rapidly the degree to which the Pito distribution is going to kick in is going to increase now you already see that that's why the the separation between rich and poor in industrialized countries is becoming increasingly severe now that's Modified by some degree to the fact by the fact that the middle class worldwide is growing like mad and so that's a really good thing but still the long-term play is the Pito distribution and it's partly because an intelligent person is one thing but an intelligent person with infinite computational power that's a completely different thing and that's where we're headed so I don't know exactly what to make of all this sort of thing politically but I know that to the degree that we ignore it we're going to have very unhappy and and unstable societies so we'll see you Thursday on that happy note we'll see you Thursday

Channel: Jordan B Peterson

Views: 65,066

Keywords: mythology, Mircea Eliade, Atrocity, Peterson, Freud, emotion, Crime and Punishment, Carl Jung, motivation, Great Father, Creativity, Jordan, Conscientiousness, Great Mother, Fear, Buddhism, Carl Rogers, Personality, Alfred Adler, Clinical Psychology, Ludwig Binswanger, Hope, Archetypes, Anger, Existentialism, Jeffrey Gray, Dragon, Medard Boss, change blindness, Maps of Meaning, Behaviorism, Christ, Developmental Psychology, existentialism, education, Hero, Jordan B Peterson

Id: 5p5YEvi8CHQ

Length: 65min 49sec (3949 seconds)

Published: Tue Mar 31 2015