Speaker 1: Welcome to Stuff to Blow Your Mind from how Stuff
Speaker 1: Works dot com. Hey, welcome to Stuff to Blow your Mind.
Speaker 1: My name is Robert Lamb and I'm Joe McCormick and Robert.
Speaker 1: I want you to think about something I know you've
Speaker 1: seen many times before. Okay, you've watched James Bond movies, right,
Speaker 1: of course. I grew up watching James Bond movies mostly
Speaker 1: on I think TBS on Thanksgiving Day and that one
Speaker 1: they show it. I think they would, but it seems
Speaker 1: like they just chewed it all the time. Like every
Speaker 1: weekend it was what what Bond movie will be sort
Speaker 1: of watching this weekend? And you would you would hope
Speaker 1: where I would often those days, I would hope it
Speaker 1: would be the Sean Connery. Nowadays, I think if I
Speaker 1: were to do what, I would say, Roger Moore please. Yeah,
Speaker 1: the more ones are the cheesier ones, they're better for
Speaker 1: Thanksgiving Day. Yeah, I think so, like the Sean Connery
Speaker 1: ones might be better movies, but they kind of just
Speaker 1: they have this tinge of alcoholism and misogyny that well, no,
Speaker 1: I guess the Roger Moore wins due to they do.
Speaker 1: It's kind of just part of the character I think
Speaker 1: you find that in every variation. Yeah. Anyway, So James Bond,
Speaker 1: what does he do when he walks up to a
Speaker 1: gambling table? What happens every time he walks up, lights
Speaker 1: a cigarette, makes some dirty word play with a female
Speaker 1: gambler or something like that, and then he gets a
Speaker 1: black check hand. What happens, Well, he tends to win.
Speaker 1: He wins every time. He always wins. When James Bond
Speaker 1: never loses, unless it's like a specific scene where gambling
Speaker 1: is crucial to the plot and he must lose, like
Speaker 1: in Casino Royality. Yeah, like, I can't even I think
Speaker 1: I can't remember if he won or lost in gold Finger.
Speaker 1: But there's a scene in gold Finger where, uh, gold
Speaker 1: Finger himself, you know, the villain of the piece, like
Speaker 1: cheats at cards. I think by by having somebody in
Speaker 1: in one of the high rises. Oh yeah, here's thinking
Speaker 1: James Bond isn't playing he gold Fingers cheating somebody else? Okay,
Speaker 1: but does he then play Goldfinger and win? It sounds
Speaker 1: like the kind of thing Bond would do. I don't
Speaker 1: think he ever played. He plays him, he plays him
Speaker 1: in golf and then and then they both cheat. Anyway,
Speaker 1: getting past this, okay, Yeah, but so he always wins.
Speaker 1: He all, he goes up, he hits twenty one right
Speaker 1: on the first throw every time first throwers what you
Speaker 1: call it the first hand. Um, And so my question
Speaker 1: is do you believe that there are people like that?
Speaker 1: Obviously there is luck in the sense that there are
Speaker 1: differential outcomes. You can have a lucky thing happen to you,
Speaker 1: you can have an unlucky thing happened to you. But
Speaker 1: do you believe there are people who are consistently lucky? Well,
Speaker 1: I'm sure plenty of our listeners have played the various
Speaker 1: role playing games, you know, video games as well as
Speaker 1: pin and paper games, and if you have, you've probably
Speaker 1: encountered characters or character management systems where there's an actual
Speaker 1: numerical luck rating for the character. Right, so you can
Speaker 1: like eight yourself higher on luck. Yeah, so you know, well,
Speaker 1: this character and then their strengthened in that grave. Their
Speaker 1: dexterity is a little lacking, but their luck skill is amazing.
Speaker 1: That's not a skill well I know, or an attributed
Speaker 1: ll but but yeah, if you play enough role playing games,
Speaker 1: that makes you think, yeah, I wonder what my my
Speaker 1: my luck rating is? Am I on a nine or
Speaker 1: a ten. Um. So is it like that in real life? Well,
Speaker 1: I think obviously the answer is no. Um. Though actually
Speaker 1: I want to go back on what I said a
Speaker 1: second ago, because I said, that's not a skill. It
Speaker 1: may be true in the sense that some things that
Speaker 1: look like luck are in fact skills, But personally I
Speaker 1: don't don't believe in this karmic version of luck. I
Speaker 1: would assume Robert, you probably don't either, I know, not
Speaker 1: not per se not not scientifically speaking, not in terms
Speaker 1: of like you having some kind of store of spiritual
Speaker 1: capital holding sway over future outcomes, right. I mean, if
Speaker 1: I was to adjust my the lenses through which view
Speaker 1: reality and uh and choose to load up more mystical
Speaker 1: religious views of the world, I might engage in this
Speaker 1: into a certain amount of magical thinking. Some folks are
Speaker 1: are lucky that some folks are, I don't know, guiding
Speaker 1: themselves to the multiverse of possibilities along like the most
Speaker 1: victorious line possible. But from a strictly like real world
Speaker 1: scientific pragmatic point of view, no, absolutely not, however, though
Speaker 1: I mean, I think we can both agree that even
Speaker 1: in the sense of a real world scientific pragmatic point
Speaker 1: of view, there are some people who do seem to
Speaker 1: be more consistently lucky than others, And I think this
Speaker 1: is because random events are it's or it's not because
Speaker 1: of random events being brought to heal by luck magic.
Speaker 1: It's because people are able to influence events in ways
Speaker 1: that are not in fact random, just look random from
Speaker 1: the outside. So, for example, a person who's really confident
Speaker 1: and positive might not actually have more good outcomes on
Speaker 1: average than somebody else. But when you think of that person,
Speaker 1: when you think of your friend who's really confident and positive,
Speaker 1: you're more likely to count the hits and discard the misses.
Speaker 1: You know, this selection bias thing. Good outcomes seem in
Speaker 1: character for that person. They sort of get added to
Speaker 1: the character sheet. You're like, yeah, that's that's them. Well,
Speaker 1: you know, bad outcomes you just ignore. That's like that's noise. Yeah,
Speaker 1: I mean James Bond is a classic example. We think
Speaker 1: about James Bond to you know, of course, the fictional
Speaker 1: character spread out across various movies and books, and we think, oh,
Speaker 1: he wins all the time, he always gets the girl.
Speaker 1: But there's a scene in What's the George lasonby movie
Speaker 1: um on Our Majesty's Secret Service. Yea, his wife is
Speaker 1: murdered by by Telexavalis's Blowfield's Man. You know, just spoilers
Speaker 1: well killed and spoiler for you know, arguably one of
Speaker 1: the lesser James Bond films. Oh no, it's some people's favorite. Uh.
Speaker 1: I enjoyed it. But but yeah, like there's a super
Speaker 1: traumatic moment like who would want? I wouldn't want? I
Speaker 1: would I would not want all of the you know,
Speaker 1: alleged benefits of Bond's life if I also meant I
Speaker 1: had to experience like that kind of a low. So
Speaker 1: even with James Bond, we're forgetting all the torture scenes
Speaker 1: and the injuries and the dead wife, and we're focusing
Speaker 1: on the stuff that we are in via stuff. Sure, okay,
Speaker 1: so that's just like influencing people's perceptions of you. But
Speaker 1: what what if you are actually you actually have more
Speaker 1: good outcomes than average. I think in in cases like this,
Speaker 1: there are a lot of things that we think of
Speaker 1: as luck that are in fact skill. One example would
Speaker 1: be some forms of gambling. Now it's true that there's
Speaker 1: no skill involved in getting lucky cards at blackjack, but
Speaker 1: there could be skill involved in other aspects of gambling,
Speaker 1: like in poker, knowing how and when to bet so
Speaker 1: as to manipulate your opponents. Uh, you can turn even
Speaker 1: a bad hand into a winning hand in poker. In
Speaker 1: black jack, you you know you can't control what cards
Speaker 1: you get, but if you can count cards, if you
Speaker 1: know the odds on any given play, if you know
Speaker 1: you know, okay, here are the cards I have, and
Speaker 1: here's what the dealer is showing. I know the odds
Speaker 1: of what I should bet. You can sort of start
Speaker 1: to leverage an advantage. In black check. I think you
Speaker 1: still probably can't get better than but but there is
Speaker 1: some skill involved there. And don't count out just flat
Speaker 1: out cheating. Oh of course, I mean the most important
Speaker 1: skill in peopling. It's the skill that the house has
Speaker 1: leveraged against you. With your consent, you agree to a
Speaker 1: game that they openly acknowledge they have rigged. This is
Speaker 1: true and nice call back to our slot Machines episode
Speaker 1: that we recently republished. Right. Uh. And another way to
Speaker 1: think about this, Uh, this concept of skill versus luck
Speaker 1: is in the realm of guessing. I think guessing is
Speaker 1: a really interesting phenomenon for human beings because we use
Speaker 1: this word a lot of different ways. Some times we
Speaker 1: use it to mean, uh, you know, just going with
Speaker 1: a gut feeling when you have no information. Sometimes we
Speaker 1: use it to mean coming up with an answer on
Speaker 1: very limited or little information. But but generally it means
Speaker 1: like trying to produce a piece of information without a
Speaker 1: strong determinative process to get you there. Well, I think
Speaker 1: in a lot of cases it's it's the kind of
Speaker 1: it's kind of artificial scenario that would not exist out
Speaker 1: of the human realm, such as I think one of
Speaker 1: the classic examples would be a multiple choice test. Then
Speaker 1: maybe you didn't study for all that well, right, and
Speaker 1: so you have suddenly are forced to answer a question
Speaker 1: that you just have no idea about. Maybe you can
Speaker 1: eliminate one possible answer. If you're still left with three
Speaker 1: likely answers, and you just have to go with your guy,
Speaker 1: you just gotta guess. You gotta get a wild shot
Speaker 1: in the dark. Yeah. And and from this concept we
Speaker 1: we have this concept of the lucky guess. Of course,
Speaker 1: people who are lucky guessers who seem to have a
Speaker 1: much better than average hit ratio at tossing out a
Speaker 1: correct or nearly correct answer to a question even when
Speaker 1: you've got essentially no knowledge or very little information to
Speaker 1: work with. And that's what I want to talk about today,
Speaker 1: about this, this process of guessing, and about how in
Speaker 1: many cases things which appear to be random lucky guesses
Speaker 1: are not in fact random. There's a skill, you know,
Speaker 1: there's a skill and art and a science to many
Speaker 1: different kinds of guessing and smart guessing. Uh. And there
Speaker 1: are even a few techniques that you can harness for
Speaker 1: yourself to get a little bit better at guessing than
Speaker 1: you might be if you're just always going with your gut.
Speaker 1: So one thing I wanted to do. I don't know
Speaker 1: how many good answers we can really come up with here,
Speaker 1: but I was wondered, like, who are some people who
Speaker 1: are some famous, really good guessers. I've got one answer,
Speaker 1: but other than that, I don't know. You know people
Speaker 1: like this personally, right, You've got friends who you know
Speaker 1: are better guessers than others. But in terms of finding
Speaker 1: like historic moments and saying like legends of guessing. I
Speaker 1: did some poking around, and there aren't a lot of
Speaker 1: great options, like you know, military history, etcetera. There aren't
Speaker 1: situations where someone just takes a wild guess and it
Speaker 1: pays off and it becomes the stuff of just absolute legends. Yeah,
Speaker 1: one of the few examples I could come across. And again,
Speaker 1: this is not a high stake situation. I mean, it
Speaker 1: kind of is for one specific person, but it's not
Speaker 1: a warfare scenario, and it's taking place within a very
Speaker 1: artificial human environment, not the multiple choice quiz but the
Speaker 1: game show. All right, So Wheel of Fortune, Wheel of Fortune,
Speaker 1: this was This occurred on a two thousand fourteen episode.
Speaker 1: So you had this contestant by the name of Emil
Speaker 1: de Leon and he had If you're familiar with Wheel
Speaker 1: of Fortune, it's where you have you know, those blank
Speaker 1: uh blank places where the letters go on the It's
Speaker 1: like a combination of Roulette and scrabble. Yeah, so it's
Speaker 1: it's it's a very specific game. It's you know, if
Speaker 1: you're like me, you at least grew up watching your
Speaker 1: grandparents watch it, and a lot of people watch it regularly.
Speaker 1: It's a it has a certain system in play, and
Speaker 1: it's kind of neat to to sit there and play
Speaker 1: along at home, you know what. It's actually more like,
Speaker 1: I don't know why we're explaining this. Everybody's seeing wheels
Speaker 1: watch but no, I mean if you actually haven't. It's
Speaker 1: like the game Hangman, where you guess letters. You have
Speaker 1: a set number of spaces. Uh you know they're like
Speaker 1: eight maybe you know there are eight letters in this
Speaker 1: word and you're trying to guess letters and if you
Speaker 1: get one right, it gets filled in there you go, Yeah,
Speaker 1: it's it's Hangman, with with with with with monetary rewards
Speaker 1: and Pat say Jack, okay, okay, So uh, the leon
Speaker 1: is playing all right, and there's like a there's a
Speaker 1: three word problem up on the board and the only
Speaker 1: letter up there is in, so that the it's in
Speaker 1: blank blank space blank blank blank blank space blank blank
Speaker 1: blank blank blank. Right, that's what it is. That's it.
Speaker 1: That's always got to go on. What would you guess?
Speaker 1: I don't know, see unlikely leon. I haven't put a lot.
Speaker 1: I haven't put a lot of thought into the system
Speaker 1: of it. Like here's a guy who watched it pretty
Speaker 1: religiously and then he was gonna, you know, he got
Speaker 1: to go on the show. So I think he was
Speaker 1: he was very much in the mind to try and
Speaker 1: game the system. I look, if I were to look
Speaker 1: at those blank spaces. I don't know what I guess,
Speaker 1: uh new rats lover. That doesn't work now, I give up. Well,
Speaker 1: but you got the new, all right, so you figured
Speaker 1: that part out. And indeed he also guessed the new,
Speaker 1: but he also went all the way and guessed new
Speaker 1: baby Buggy and one sixty three thousand. Yeah, cheating must
Speaker 1: have been cheating. Well, some people leveled that charge and
Speaker 1: he uh, he ended up explaining himself because this was
Speaker 1: apparently a big deal, like even Pat say Jack said
Speaker 1: this was the craziest guests ever in his history of
Speaker 1: hosting the show and when they When Leon Julianne was interviewed,
Speaker 1: he said that, well, first of all, he'd been watching
Speaker 1: the show for some time. He knew the game inside out,
Speaker 1: and he knew that knew had to be the first
Speaker 1: word like that was even we got that. You know
Speaker 1: that if it's in blank blank? How are many? How?
Speaker 1: What what are some more common words that come to mind?
Speaker 1: Not many? Maybe not not not now, but he he,
Speaker 1: I guess watched it enough to know that a lot
Speaker 1: that's probably gonna be new. And then he said that
Speaker 1: since he was studying for nursing exams, he had babies
Speaker 1: on the brain, so he just kind of it just
Speaker 1: happened to be that that, like baby is the perfect
Speaker 1: four letter word. I don't is new baby buggy like
Speaker 1: a like a common phrase I don't not in in
Speaker 1: my experience, So that's like an expression that I'm not
Speaker 1: familiar with. I mean, I guess it's like a new baby.
Speaker 1: Is it like a some sort of a rhyming nursery
Speaker 1: rhyme kind of a thing or tongue twister? I guess
Speaker 1: is a tongue twister. Maybe that's the origin there, but
Speaker 1: uh yeah, it just seems kind of crazy that that
Speaker 1: he just instantly produces the answer to this seemingly out
Speaker 1: of nowhere. Uh As it turns out it's not quite
Speaker 1: out of nowhere. He at least had a very educated
Speaker 1: guests on that first word, and then his prior experience
Speaker 1: just happened to ease him into those last two words. Okay,
Speaker 1: Well that that might you might actually just call that
Speaker 1: luck in some ways, like it might know the game
Speaker 1: well enough to see new there. But I mean, those
Speaker 1: other words could have been anything, right, but his experience
Speaker 1: prepared him to be lucky in a way that other
Speaker 1: people would not have been lucky, like if they had
Speaker 1: not a watch the show a bunch of times, which
Speaker 1: I don't know if anybody ever just shows up on
Speaker 1: Will of Fortune, they've basically never seen the show. They
Speaker 1: do this new baby buggy puzzle every other week. Yeah,
Speaker 1: so you know, I I think you can you can
Speaker 1: make the argument either way. But yeah, I would say
Speaker 1: that his his experiences put him in just the right
Speaker 1: position to to to be a little quote unquote luckier
Speaker 1: than other people. Okay, well, I mean, so whatever is
Speaker 1: happening in that scenario, we do know that, at least
Speaker 1: in much the same way, somebody who appears to be
Speaker 1: a consistently lucky gambler might just be a skilled gambler
Speaker 1: counting cards, calculating odds, manipulating opponents. Um. When it comes
Speaker 1: to numerical values, uh, somebody who appears to be a
Speaker 1: lucky guess or with numbers is more likely to be
Speaker 1: a skillful guesser figuring out how to leverage existing knowledge
Speaker 1: that you wouldn't even thought of to take into account
Speaker 1: into a kind of ballpark accurate guess. And one person
Speaker 1: who's famous for this is the Italian physicist Enrico Fermi
Speaker 1: So Fermi lived from nineteen o one to nineteen fifty four. UM.
Speaker 1: He grew up in Italy. After the passage of anti
Speaker 1: Semitic restrictions and fascist Italy in ninety eight, for Me
Speaker 1: and his family fled to the United States, where he
Speaker 1: ended up working on the Manhattan Project and in his
Speaker 1: role for Me, was present for the first test of
Speaker 1: the atomic bomb on July sixteenth, ninety the Trinity Test.
Speaker 1: You've heard about this, and at the time, this was
Speaker 1: new territory. Nobody had ever tested a nuclear weapon before,
Speaker 1: you know, a fission weapon with this big yield. They
Speaker 1: didn't know exactly what was going to happen. You know,
Speaker 1: the physicists had their calculations. Uh, they were fairly confident
Speaker 1: that the device would explode. It was this plutonium implosion
Speaker 1: bomb that they called the Gadget, and they thought it
Speaker 1: would generate a large explosion, but the outcome was all
Speaker 1: theoretical at that point. They weren't sure what the level
Speaker 1: of energy output would be. Yeah, I remember reading that
Speaker 1: like on the extreme ends of the spectrum, that where
Speaker 1: there was the possibility that it could be a dud
Speaker 1: or it could catch the air on fire. About that
Speaker 1: that sort of thing. Yeah, and so they didn't know.
Speaker 1: So Enrico Fermi that this great physicist who's famous at
Speaker 1: good guesses he's there to watch the test. So picture
Speaker 1: him there. Uh, he's there with his colleagues, and he's
Speaker 1: at a camp about ten miles away from ground zero,
Speaker 1: ten miles from where the bomb goes off. Jay Robert
Speaker 1: Oppenheimer's there like scribbling notes into a Hindu epic. I'm sure,
Speaker 1: I'm sure. Yes. So they're behind some shielding for good reasons. Uh.
Speaker 1: And Fermi watches the blast through a board that's got
Speaker 1: a viewing window made of welding glass. And there's a
Speaker 1: two thousand five issue of the Nuclear Weapons Journal that
Speaker 1: includes an article with some great quotes from Fermi and
Speaker 1: others who were eyewitnesses to this to the event, and
Speaker 1: Fermi wrote, So he's there, he's looking through the welding glass. Um,
Speaker 1: and uh that he very first saw quote a very
Speaker 1: intense flash of light that was brighter than full daylight,
Speaker 1: and then a conglomeration of flame that rose into the sky,
Speaker 1: and a huge pillar of smoke with an expanded head,
Speaker 1: like a gigantic mushroom. Here's where we get our mushroom
Speaker 1: cloud um and that rose rapidly into the clouds. Now,
Speaker 1: when there's an explosion and you're pretty far away, there's
Speaker 1: a time gap between when you see the flash and
Speaker 1: when you feel the blast. Right could because why light
Speaker 1: travels faster than sound. It's the same thing that happens
Speaker 1: between lightning and thunder. You see the light and then
Speaker 1: you hear the sound of the thunder. Uh So it
Speaker 1: was about forty seconds after the visible explosion that the
Speaker 1: air blast actually hit the observation camp. And when the
Speaker 1: air blast arrived, Fermy did something really weird. He held
Speaker 1: up a handful of scraps of paper about six ft
Speaker 1: off the ground, and he dropped them and he let
Speaker 1: them flutter away in the force of the air blast,
Speaker 1: and then after seeing where they fell, he released some more,
Speaker 1: just in regular air, no blast. And then after he
Speaker 1: looked at how far they went, I think it was
Speaker 1: like two point five meters or something, he quickly guessed
Speaker 1: that the detonation had been about ten kilo tons worth
Speaker 1: of explosion, meaning it released the same energy as ten
Speaker 1: thousand tons of t n T. Now, when the actual
Speaker 1: readings came in, it was about twenty kill a tons,
Speaker 1: about twice Fermi's estimate. So he wasn't exactly right, but
Speaker 1: this is still a remarkably good guess for having no
Speaker 1: direct readings to work with. I mean, after all, you
Speaker 1: think about it, can you look at an explosion ten
Speaker 1: miles away and say how many tons of T n
Speaker 1: T you think it's equivalent to know? I wouldn't even
Speaker 1: know how to. I wouldn't know what order of magnitude
Speaker 1: no tons to kill a tons to mega tons um.
Speaker 1: So with just some scraps of paper watching how far
Speaker 1: they blew in the wind, FIRMI was able to do
Speaker 1: some quick calculations in his head and correctly guess within
Speaker 1: the true order of magnitude. So how did he do it? Well,
Speaker 1: we'll come back to that in a bit when we
Speaker 1: get into the Fermi estimation method. Now, before we move on,
Speaker 1: and I think this is also of interest that the U.
Speaker 1: S Army as well as other U S Armed forces,
Speaker 1: have used the acronym SWAG before, which stands for a
Speaker 1: scientific wild ass. Guests, now, you're not you're not swearing
Speaker 1: on the podcast? Now? No, no, no, not necessarily. I
Speaker 1: guess it depends on your your viewpoint here, But uh,
Speaker 1: this was a now Robert, Uh, you know what we're
Speaker 1: talking about here. Of course, as a guestimate, a guest
Speaker 1: made by an expert or institution with a certain amount
Speaker 1: of expertise in a given topic. Um, you know, it's
Speaker 1: still a guests but but hopefully you're leveraging your best
Speaker 1: information and making that guess. It's I think it's generally
Speaker 1: considered a guest that comes from somebody who should know
Speaker 1: what they're talking about, even if they don't have direct
Speaker 1: information to work with. So you know, you might be
Speaker 1: in a situation where, uh uh, somebody has some weird
Speaker 1: array of symptoms and they don't really correspond to any
Speaker 1: known medical condition, and maybe you don't have any instruments.
Speaker 1: You can't take their temperature, you can't do any lab
Speaker 1: work or whatever. But you could still have a doctor
Speaker 1: look at them and guess what's wrong with them, or
Speaker 1: just have a I don't know, a football player look
Speaker 1: at them and guess what's wrong with them. Even though
Speaker 1: the doctor doesn't have a lot of his or her
Speaker 1: tools at their disposal. Um, they still might just have
Speaker 1: some intuitions based on their experience. Right right now, now,
Speaker 1: you have to say it might be a football related injury.
Speaker 1: In which case the football player might have insight that
Speaker 1: someone else might not have, so in the in their
Speaker 1: case it might be a swag if you will. Now,
Speaker 1: I do want to point out that the wild ass
Speaker 1: part of this is technically not um me being obscene,
Speaker 1: because as William Saffire points out has pointed out before,
Speaker 1: I believe in the New York Times, uh he said
Speaker 1: that the wild ass is not a mirror of vulgarism,
Speaker 1: as it can be found five times in the King
Speaker 1: James Bible, most notably job behold as wild asses in
Speaker 1: the desert go, they fourth to do their work. So
Speaker 1: there you go. Well, of course, yeah, I knew that's
Speaker 1: what you meant. Yeah. So, now that we've biblically grounded
Speaker 1: the episode, I think maybe we should take a quick
Speaker 1: break and when we come back we will jump into
Speaker 1: mathematical estimation. So we're talking about guessing as a skill
Speaker 1: rather than as pure blind luck. In one way you
Speaker 1: can maybe get better than chance at certain kinds of
Speaker 1: guessing is to leverage the power of simple observations and
Speaker 1: rough math. There are a lot of situations in your
Speaker 1: life where you might be asked to guess something and
Speaker 1: it's at first not apparent that you can do any
Speaker 1: better than just got feeling just come up with a
Speaker 1: you know, this number sounds right. Uh. You know, somebody
Speaker 1: asks how many buildings are in Atlanta and you'd be like, uh,
Speaker 1: I don't know. You might come up with a number
Speaker 1: and be like a hundred thousand, you know that feels
Speaker 1: about right, but you have nothing to work with there.
Speaker 1: In many situations like this, you can do better, and
Speaker 1: you can do better without going to the you know,
Speaker 1: encyclopedia or the you know, city statistics to look up
Speaker 1: the information you need, because you can just leverage simple
Speaker 1: observations with math. One great example of this, I think
Speaker 1: would be the gumball jar contest. Oh yeah, you see
Speaker 1: variations in this everywhere you go, Like it might be gumballs,
Speaker 1: that might be jelly beans, but it's Yeah. This is
Speaker 1: a wonderful example because one of the problems with be
Speaker 1: how many buildings in Atlantic question is that just off
Speaker 1: the top of my head, I mean, I know Atlanta,
Speaker 1: I know how to get where I need to go,
Speaker 1: but I don't have like the firmest vision in my
Speaker 1: head of its limits and its size and it's true
Speaker 1: shape and and scope and Unlikewise, I don't have a
Speaker 1: great idea of like just off the top of my head,
Speaker 1: like how many buildings tend to occupy, say a given
Speaker 1: square of you know, of of urban real estate. Now,
Speaker 1: I think you could still do better than chance guessing
Speaker 1: at this, even not knowing those things, if we just
Speaker 1: uh leverage the power of making wild donkey guesses, uh,
Speaker 1: and then and then bring it together with some math
Speaker 1: in in terms of the thing we're going to talk
Speaker 1: about in the mid in a bit, which is firm
Speaker 1: me estimation. But back the gumball, the gumball jars, that's doable.
Speaker 1: You might look at a gumball jar and what you
Speaker 1: probably do is try to gut feel it. Right, Yeah,
Speaker 1: because with the gumball um the container, I can see
Speaker 1: how big the overall container is. I can make a
Speaker 1: rough visual guess about how many gumballs occupy a given
Speaker 1: area and then just sort of roughly multiply that area
Speaker 1: in my mind until it fills up the space of container. Yeah. Yeah,
Speaker 1: So yeah, you're you're trying to eyeball it. Uh, but
Speaker 1: I contend you can do better. So Okay, So you
Speaker 1: might Robert picture yourself at the County Fair is that
Speaker 1: usually where the gumballs would be. Oh, I tend to
Speaker 1: encounter them in like school fair scenarios. Okay, school fair,
Speaker 1: you know, that's exactly I was talking to Rachel about.
Speaker 1: She used to when she was a kid. She always
Speaker 1: wanted to be able to guess the number of gumballs
Speaker 1: that were at their school spring fling, I think, and
Speaker 1: she never got it right. They had to do them
Speaker 1: at you know, bars and restaurants. More like a container
Speaker 1: of pickled eggs. That's perfect. You know how many pickled eggs.
Speaker 1: Guess the number of pickled eggs. Get a free pickled egg. Okay, so,
Speaker 1: but you're at a school fair then, Robert and uh,
Speaker 1: it's guess how many gumballs are in the jar. The
Speaker 1: closest guests gets a prize. What's the prize? It is
Speaker 1: a deep fried, unopened can of corned beef. Hash uh
Speaker 1: laughing at my own jokes. That's bad. Uh. So now
Speaker 1: this game is easy to play, right because you can
Speaker 1: eyeball it. You look at the jar somewhere deep behind
Speaker 1: the curtain in your brain, a damon rises out of
Speaker 1: the darkness and just plants this random, wild ass number
Speaker 1: in your mind. It's like two hundred and thirty, and
Speaker 1: you look at the jar again and you think that
Speaker 1: sounds about right, you write it down. You hope you win,
Speaker 1: but you don't win because who won. The person who
Speaker 1: won was somebody who did some rough math. Because if
Speaker 1: you stop to think about it, you do have some
Speaker 1: ways of knowing about how many gumballs are in the jar.
Speaker 1: If you've got some basic like high school geometry and
Speaker 1: a pair of eyes, you can start getting a solid
Speaker 1: rough estimate to work with. So, Robert, I put a
Speaker 1: picture of some gumballs in our notes here, and I
Speaker 1: already did some calculations on this. But um, so this
Speaker 1: is a jar of gumballs, right? You attest that it?
Speaker 1: Truly it does look like a jar of gunballs. No,
Speaker 1: this is a two dimensional image. I have no idea
Speaker 1: how how long this could be. This could be I'd
Speaker 1: assume that it shaped like a are, but it could
Speaker 1: be shaped like something else. Yeah, well we'll just assume
Speaker 1: it's basically circular. So for simplicity's sake. One thing that's
Speaker 1: a really good method when trying to come up with
Speaker 1: these rough math guesses is skip standard units of measure.
Speaker 1: Don't measure things in terms of inches, centimeters, pounds, whatever,
Speaker 1: measure in terms of something that you're directly looking at.
Speaker 1: So instead of measuring the size of the jar in
Speaker 1: inches or centimeters, we're gonna calculate it in units of gumballs. Okay, like,
Speaker 1: don't try and measure in calories. Continue. So, look, you
Speaker 1: look at a jar and you think how many gumballs
Speaker 1: are wide? Does it look like this jar is in
Speaker 1: diameter ten? Maybe? I guess nine if you want to
Speaker 1: go with nine, nine sounds good, okay. And then how
Speaker 1: many gumballs high? Do you think that the jar looks? Oh,
Speaker 1: I'd say more than that, like twelve or thirteen. I
Speaker 1: guess ten. Okay, we'll go with ten. Okay, let's go
Speaker 1: with him. Yeah, okay, Now now I feel like I've
Speaker 1: stomped all over your guests. No, no, no, no, I
Speaker 1: think it's I think that's good because if I sort
Speaker 1: of turn it sideways, it's it's still it's a very
Speaker 1: square looking jar, all right. So it's about nine in diameter,
Speaker 1: about ten tall. Now, a jar is roughly a cylinder, right,
Speaker 1: You remember from geometry, what's the formula for the volume
Speaker 1: of a cylinder. It's not that complicated volume of a
Speaker 1: cylinder is the area of the circle times the height.
Speaker 1: The area of the circle is pie times the radius squared.
Speaker 1: So you start with the base of the jar the
Speaker 1: circle pie, which is three point fourteen times are. The
Speaker 1: diameter was nine, right, if it's nine across are, the
Speaker 1: radius is four point five because it's half of that.
Speaker 1: The first you square the radius four point five squares
Speaker 1: is a little over twenty. We just go with twenty,
Speaker 1: and then you multiply that times three point fourteen, which
Speaker 1: is sixty two point eight. I'm glad we could agree
Speaker 1: on the the fat the figures here, because otherwise we
Speaker 1: would have had to recalculated everything in our notes. You
Speaker 1: have you have seen through my insistence whatever. Okay, so
Speaker 1: you got sixty two point eight times the height of
Speaker 1: how many gumballs high? Ten? Ten? Alright, So that says
Speaker 1: they're about six hundred and twenty eight gumballs in the jar. Now,
Speaker 1: that's probably not going to be right on the money,
Speaker 1: but I'd say it's also probably going to be a
Speaker 1: lot closer than the real number to the real number
Speaker 1: than if you just eyeballed it. Right, if I had
Speaker 1: eyeballed the jar, I might have said, I don't know
Speaker 1: three hundred and fifty, But now looking back at it,
Speaker 1: I'm like, oh, you know that probably is more than
Speaker 1: three hundred and fifty. Yeah. Yeah. I feel like when
Speaker 1: I was first looking at it, I would have probably
Speaker 1: gone on ten by ten hundred and then try to
Speaker 1: like I think, like, all right, maybe three or four
Speaker 1: deep and I would have gone three hundred four hundred. Yeah,
Speaker 1: but but I think our estimate now is actually probably better. Uh.
Speaker 1: And that's one of the last things you should do
Speaker 1: whenever you do this kind of mathematical calculation is you'll
Speaker 1: get the jar again and you think, is my estimate
Speaker 1: within the realm of possibility? Is it stupid? If I
Speaker 1: came up with thirteen point eight billion gumballs in the jar?
Speaker 1: This is an indication that the math or the counting
Speaker 1: went wrong somewhere along the line. You should back up
Speaker 1: and try again, or the jar is is seriously spooky
Speaker 1: and you try not have anything to do with it. Yeah.
Speaker 1: Another way of checking against reality is to test the
Speaker 1: method in the real world. So would such a method
Speaker 1: actually win you a gumball jar guessing contest? Well, I
Speaker 1: thought I'd do some googling, and I did, and sure, enough.
Speaker 1: I found a blog post about a guy who won
Speaker 1: a gumball jar guessing contest. Somebody asked him what method
Speaker 1: he used, and he said he calculated the volume of
Speaker 1: the cylinder in the jar, and then he randomly added
Speaker 1: twenty five to that number. So it's sort of like
Speaker 1: being the the the the the area of error in
Speaker 1: his calculations. Huh, yeah, I guess it could be. So
Speaker 1: he came up with like seventeen uh, one thousand seven
Speaker 1: five gunballs, and actually it was one thousand, seven hundred
Speaker 1: and fifty. So yeah, so so, so you've got these principles,
Speaker 1: right you. You don't have to just surrender to your
Speaker 1: gut instinct when it's time to guess something. You can
Speaker 1: couple very simple rough math. You know, this is not
Speaker 1: complex calculus or anything like that, with observations that you
Speaker 1: can just get by looking at what's in front of
Speaker 1: you or by drawing on really basic knowledge or even
Speaker 1: just guesses. All you need to do is think about
Speaker 1: the logical relationships between numbers and know how to look
Speaker 1: for those relevant pieces information that might be in your
Speaker 1: memory or might be right in front of your eyes. Now,
Speaker 1: I think it's time to get back to Enrico Fermi, so,
Speaker 1: as we mentioned earlier, for me, was apparently known for
Speaker 1: being a really good guesser when it came to numbers.
Speaker 1: And there is a classic example that's often used as
Speaker 1: an example of how his method of estimation works. Um,
Speaker 1: it would be how many piano tuners are there in
Speaker 1: the city of Chicago. Now, I have found lots of
Speaker 1: different versions of this all over the internet, you know,
Speaker 1: people working it out in different ways. But the goal
Speaker 1: of Fermi estimation is not to hit the number exactly,
Speaker 1: but it is to get into the right ballpark, get
Speaker 1: in striking distance of it, if you will. Yeah. And
Speaker 1: so one version of how many piano tuners are in
Speaker 1: Chicago appears on NASA's Glenn Research Center page. And and
Speaker 1: this is their version. Uh. So they start with how
Speaker 1: would you even begin to calculate that? Well, one number
Speaker 1: you can work with is the population of Chicago. Yeah, okay,
Speaker 1: so that will give you something to start with. So
Speaker 1: they go to the almanac. They say, at this time,
Speaker 1: the Chicago as a population of about three million people.
Speaker 1: Now assume that the average family has four members, so
Speaker 1: like four members per household, So the number of households
Speaker 1: in Chicago is going to be three million divided by four,
Speaker 1: so that's about seven fifty thousand seven households. How many
Speaker 1: households own a piano? They guess one in five. I
Speaker 1: think that's probably kind of high, but I don't know, Yeah,
Speaker 1: I have, I just have no way of of Well.
Speaker 1: One thing you can do in these scenarios that that
Speaker 1: I'll get to in a little more depth than just
Speaker 1: a minute is if you don't know how to guess
Speaker 1: something like what percent of families have a piano in
Speaker 1: their household, you come up with boundaries. So you say, Okay,
Speaker 1: what's the lowest number that would make any sense, what's
Speaker 1: the highest number that would make any sense, and then
Speaker 1: you take what's known as a geometric mean between them,
Speaker 1: which means you multiply them together, and then you take
Speaker 1: the square root of that number. Okay, so the process
Speaker 1: here could be one in ten people have a piano.
Speaker 1: That sounds like that would make pianos a bit too rare.
Speaker 1: One in three. I don't know if they're that common.
Speaker 1: Let's split the difference more or less and go with
Speaker 1: one in five. Yeah, that that's actually really close. So
Speaker 1: if you if you multiply together, um, one in three,
Speaker 1: which would be about point three. Uh, and then one
Speaker 1: in ten, which would be point one. And then you
Speaker 1: take that number and get the square root of it.
Speaker 1: Your answer is like point seventeen, which is close to
Speaker 1: point two, which is one in five. So there we go.
Speaker 1: We're on track. So if one in five families has
Speaker 1: a piano and there are seven hundred and fifty thousand
Speaker 1: families in Chicago, that means there's gonna be one hundred
Speaker 1: and fifty thousand pianos in Chicago. There's a number to
Speaker 1: work with. All right, you got a hundred fifty Now
Speaker 1: that is a number of pianos that are available to
Speaker 1: be tuned. So this can give us a foothold to
Speaker 1: try to figure out how many tuners there are. If
Speaker 1: you've got an average piano tuner, I mean, how many
Speaker 1: pianos do you think they could tune in a day
Speaker 1: in a work day? Okay, this is going with the
Speaker 1: assumption that like they're design like the piano tuner makes
Speaker 1: this his or her um life. Like, they're not just
Speaker 1: doing a little piano tuning on the side, right, this
Speaker 1: is their full time job. Oh I don't know. Um
Speaker 1: lets you have to travel there, you have it I
Speaker 1: mean comfortably, what maybe three or four a day? Well,
Speaker 1: in this estimation they come up with four. I think
Speaker 1: four is a reasonable guests. Yeah, Like I think of
Speaker 1: other jobs, like you know, forst is, my wife's a photographer.
Speaker 1: She's not tuning pianos, but she has to travel somewhere,
Speaker 1: do a session and then come back. And I think, like,
Speaker 1: if she was just just crazy busy, how many should
Speaker 1: could you fit in a day? You know? Like that
Speaker 1: seems about right. Yeah. Another option, if we didn't believe
Speaker 1: that four days, we could do the geometric mean again,
Speaker 1: we could say, well, it's got to be more than one,
Speaker 1: and it can't be more than what like six, I
Speaker 1: mean that that'd just be care you can't be certainly
Speaker 1: can't be more than eight. Um. So then you'd get
Speaker 1: a GMO tricked me and that that probably put it
Speaker 1: a little bit lower than four, but you'd still have
Speaker 1: some number in that, you know, three something like that, Okay,
Speaker 1: And then of course you assume they don't work on
Speaker 1: the weekends and they've got a two week vacation during
Speaker 1: the summer. So that's fifty weeks in a year of
Speaker 1: tuning four pianos a day, five days a week. So
Speaker 1: that means in one year, the average worker, the average
Speaker 1: piano tuner, would service one thousand pianos. Now, if we
Speaker 1: said that there are a hundred and fifty thousand pianos
Speaker 1: in the city of Chicago, that means there should be
Speaker 1: about a hundred and fifty piano tuners in the city.
Speaker 1: I don't know, does that number sound reasonable. It's at
Speaker 1: least got you in the ballpark. I guess it sounds reasonable.
Speaker 1: I it's I mean, I guess this is a difficult
Speaker 1: thing to check because is there like a Piano Tuners
Speaker 1: Association of America that you can check with on this
Speaker 1: sort of thing. Well, I've seen other estimates that work
Speaker 1: out the number differently, so they they you know, they
Speaker 1: might say, well, I think that your estimate on step
Speaker 1: four here is not smart. I would change it to this,
Speaker 1: and that actually gives me, uh, you know, something more
Speaker 1: like forty piano tuners in the city of Chicago. And
Speaker 1: one thing you can check is you can look at
Speaker 1: see how many are in the phone book. Then again,
Speaker 1: I mean, in this day and age, there's probably a
Speaker 1: lot of things that aren't in the phone book, right, Yeah,
Speaker 1: you kind of end up like the the the yelp
Speaker 1: versus phone book uh tug awar, depending on where you're going,
Speaker 1: is it a yelptown or are they still yellow pages down?
Speaker 1: And and then you're you're you know, you're you're also
Speaker 1: forgetting about all the black market piano tuners out there.
Speaker 1: Uh yeah, but those black market piano tuners get less
Speaker 1: piano tuning done because they're also moonlighting as uh piano
Speaker 1: wire assassins. That's that's true. Now, when you're estimating big
Speaker 1: numbers based on little data, one of the things that's
Speaker 1: really helpful, this helpful concept is the idea of orders
Speaker 1: of magnitude. We've talked about this a little so far,
Speaker 1: but just to be clear about what this is. Um
Speaker 1: when you read about really big a very little numbers
Speaker 1: in science, you'll often see those numbers expressed not in
Speaker 1: full notation, written out. But you've seen this before where
Speaker 1: it is scientific notation. It's a like four point eight
Speaker 1: times tend to the nineteen or something like that. That
Speaker 1: would be a really big number. And so U instead
Speaker 1: of writing a thousand, you write like ten to the three,
Speaker 1: or instead of writing point zero zero one, it's ten
Speaker 1: to the negative three, and you get more precise instead
Speaker 1: of two thousand, five hundred, you write two point five
Speaker 1: times ten to the three or instead of point zero
Speaker 1: zero zero zero eight seven, it's eight point seven times
Speaker 1: tend to the negative five. Right, So you've you've got
Speaker 1: orders of magnitude, and they are the exponent in that
Speaker 1: type of notation. Every time the exponent goes up or
Speaker 1: down a number, that's an order of magnitude. Another simpler
Speaker 1: way of thinking about this is that the order of
Speaker 1: magnitude is just the number of digits in a number.
Speaker 1: Get single digit number, double digit, triple digit, quadruple digit, um.
Speaker 1: When somebody is talking about the number of figures in
Speaker 1: a salary, they're concerned about orders of magnitude. You know.
Speaker 1: One thing this reminds me of is, of course, the
Speaker 1: the classic educational film created by the Aims uh the
Speaker 1: Powers of Tin which granted that so there's a visual,
Speaker 1: very strong visual element to that as well, but it
Speaker 1: basically seeks out to explain and make digestible the scale
Speaker 1: of the universe. This is that classic zooming in and out.
Speaker 1: That thing is great, it is, it's still it's wonderful,
Speaker 1: still holds up really well today and uh and it's
Speaker 1: just you know, phenomenal to watch. But yeah, by considering
Speaker 1: the order of magnitude, like, it's able to make some
Speaker 1: of these that the scale is able to make the
Speaker 1: scale of the universe more digestible. Yeah. Now, if you
Speaker 1: haven't seen that, go out and google it right now.
Speaker 1: You can put us on pause. It's it's worth that
Speaker 1: you should really watch. I think it's on YouTube, isn't it. Yes,
Speaker 1: I believe that there's an official YouTube version of it.
Speaker 1: It's just it's fantastic. Um. But yeah, So back to
Speaker 1: why why to orders of magnitude matter? Well, for me,
Speaker 1: estimation that this uh process that was really made immortal
Speaker 1: by Enrico Fermi, is a way of easily guessing numbers
Speaker 1: by rounding up or down by orders of magnitude and
Speaker 1: then calculating based on these easy to work with round numbers.
Speaker 1: So we started doing that in our last example right
Speaker 1: when we were taking geometrical means. Um. But the basic
Speaker 1: way that a Fermi estimation problem works is you start
Speaker 1: by figuring out what are the key assumptions, what are
Speaker 1: the factors you would need to know in order in
Speaker 1: order to calculate your answer. So in the piano tune
Speaker 1: or example, you'd be like, well, Okay, if we know
Speaker 1: the city of Chicago has a certain population, and we
Speaker 1: know that piano tuners can tune a certain amount of
Speaker 1: pianos each week, we can derive from those numbers what
Speaker 1: we need to calculate our answer. So the next step
Speaker 1: would be like thinking about what order of magnitude your
Speaker 1: your key pieces of information are on. So like when
Speaker 1: you're making a guess, this is where the boundaries come in.
Speaker 1: If you have no idea for a number, if somebody
Speaker 1: asks you, um, how many lucky charms marshmallows have ever
Speaker 1: been manufactured on planet Earth? You have no idea, right,
Speaker 1: I mean I wouldn't even know where to start, absolutely
Speaker 1: no idea. But actually you you do know where to
Speaker 1: start because you can play with boundaries again. Okay, so
Speaker 1: what's a low number that you you know it's got
Speaker 1: to be more than ten thousand? I mean that's ridiculous,
Speaker 1: more than ten for sure. Yeah, but you keep keep
Speaker 1: bringing your lower bound up so you know it's more
Speaker 1: than a hundred thousand, right you know? Well yeah, because
Speaker 1: you know, in fact, you probably know it's more than
Speaker 1: a million, because what do you think at least a
Speaker 1: million people of eating a bowl of lucky Charms at
Speaker 1: some point in history. Yeah, it's been around for at
Speaker 1: least decades. Yeah, and so if at least a million
Speaker 1: people of eating a bowl of lucky Charms and each
Speaker 1: bowl had more than one marshmallow in it, you know
Speaker 1: there's at least more than a million. Um. I that
Speaker 1: we could even go safely to ten million, but I
Speaker 1: don't know. I'll stick to a million. That's our lower bound.
Speaker 1: And then, uh, you know what's the upper bound? I mean,
Speaker 1: you know there cannot have been ten trillion of these marshmallows, right,
Speaker 1: there's just too many. Way Yeah, Okay, so now you've
Speaker 1: actually got boundaries, so you know there's less than ten trillion,
Speaker 1: and a geometric mean between one million and ten trillion
Speaker 1: is ten billion? Is that anywhere close to the right answer?
Speaker 1: Well maybe not, But now you've got something to work
Speaker 1: with that's better than you started with, which was just
Speaker 1: I have no idea. Well, this is quite a useful tool.
Speaker 1: We've been we've been talking about those far because I
Speaker 1: can already see the ways that this can be easily
Speaker 1: applied to say the person's work week. You know, how
Speaker 1: much how much of um, you know, my given work
Speaker 1: can I fit in could I, you know, could could
Speaker 1: I write this many articles? Could I write this many?
Speaker 1: What's the what's the most extravagant and the smallest number?
Speaker 1: And then ending that middle ground. Right, So yeah, but
Speaker 1: remember it's not just the simple mean, because what what
Speaker 1: you're really looking for is the geometric mean, which again
Speaker 1: is instead of so the simple mean simple average is
Speaker 1: you add them together and divide by two. The geometric
Speaker 1: mean is multiply them together and then take the square root.
Speaker 1: So if you say, how many articles do you think
Speaker 1: you could write in a week, Robert, what's the what's
Speaker 1: the highest possible number? That's kind of crazy, the highest
Speaker 1: possible number. We'll just without boring anybody about details and
Speaker 1: get into a big conversation about which form of article, etcetera.
Speaker 1: Let's just go ahead and say, um, twenty articles twenty Okay, Now,
Speaker 1: what's a really low ball number, lazy as heck, Let's
Speaker 1: say four or five. Let's say five just to keep
Speaker 1: it cleaner, maybe, or four, whichever one is easier to compute. Okay,
Speaker 1: so four times twenty. Then take the square root of
Speaker 1: that number. It's about eight point nine or nine. So
Speaker 1: that's a number, all right, That that's better than not
Speaker 1: having anything to work with. One of the key things
Speaker 1: about this type of estimation is that it's useful, but
Speaker 1: it's only useful if you treat it critically. I mean,
Speaker 1: obviously you can't just generate numbers using this method and
Speaker 1: then go with them. But it does give you a
Speaker 1: place to a foothold, essentially for thinking about numbers. Whereas
Speaker 1: you started with paralysis, you're starting staring into a void
Speaker 1: of all possible numbers and you have no idea where
Speaker 1: to start FIRMI estimation helps give you a place to
Speaker 1: start with and say is that reasonable? And you can
Speaker 1: work up and down from there. Um. But okay, so
Speaker 1: so you've got that. When when you want to get
Speaker 1: a factor and you have no idea what it is,
Speaker 1: put some boundaries in place and then take a geometric mean. Um. Now,
Speaker 1: once you use these assumptions, you make a rough calculation
Speaker 1: like they did with the piano tuners example, and then
Speaker 1: you look at your answer and you do a reality check.
Speaker 1: You say, is this reasonable? Is this number within the
Speaker 1: realm of possibility? And do I need to go back
Speaker 1: and adjust anything I did before. Now this might be
Speaker 1: a terrible example, but I kind of wanted to just
Speaker 1: have us try one on the fly. Okay, let's do it. Okay,
Speaker 1: so you want to guess a totally unknown number. And
Speaker 1: here's my question. How many pounds of hair do Americans
Speaker 1: get cut off their heads in total each year? Not
Speaker 1: individual Americans, all of America? How many pounds of hair
Speaker 1: are cut? All? Right? Well, the obvious starting point there
Speaker 1: would be how many Americans are we dealing with? Right? Okay,
Speaker 1: so there you go. So how many Americans there? I
Speaker 1: think there are what like three? Do you want to
Speaker 1: go with the three hundred and there are more than
Speaker 1: three hundred million, but we could round down to make
Speaker 1: it simple. Three hundred millions sounds good. Okay, so we've
Speaker 1: got three hundred million Americans? Uh, in a very rough estimate. Now,
Speaker 1: how many pounds of hair on average does American have?
Speaker 1: This is going to vary widely. Some people have dreadlocks
Speaker 1: to their knees, some people are totally bald. But what's
Speaker 1: a good average that would put us right in the middle,
Speaker 1: like the pounds and like how much hair they haven't
Speaker 1: cut off or just how much hair they have have?
Speaker 1: Al Right? Well, alright, well, I think what do I
Speaker 1: know the weight of the human brain is about three pounds.
Speaker 1: I feel like hair weighs less than a brain in general,
Speaker 1: so I would say a pound of hair. It still
Speaker 1: kind of feels big. Yeah, I would tend to think
Speaker 1: that people on average have less than a pound of hair.
Speaker 1: I mean, somebody who has really long hair maybe might
Speaker 1: have a pound of hair. I don't know. Maybe this
Speaker 1: is the beauty of it. Just rough gas, Okay, like
Speaker 1: a quarter of a pound. Okay, let's start with five
Speaker 1: pounds of hair per person. Okay, Well, I did just
Speaker 1: do the calculation of how many pounds of hair there
Speaker 1: are in America, but we might not actually need that figure.
Speaker 1: So three million people times a quarter pound of hair
Speaker 1: per person is seventy five million pounds of hair. But
Speaker 1: like I said, we might not need it. In fact,
Speaker 1: let's just stick with the quarter pounds of hair per person.
Speaker 1: What percentage of your hair does the average person get
Speaker 1: cut off in a haircut? Again, this is going to
Speaker 1: vary wildly. Some people get there, you know, long hair
Speaker 1: shaved completely off. Some people get a tiny little trim.
Speaker 1: But on average, what what is the mass of your
Speaker 1: hair that is removed in a haircut? Um off? Off hand?
Speaker 1: I'm thinking, Okay, I would guess kind of higher. I
Speaker 1: was thinking I probably wait too long to get a haircut,
Speaker 1: So with me, I think it's like fifty percent um.
Speaker 1: But maybe we can get get in between them. I
Speaker 1: don't know if everybody other people wait as long as
Speaker 1: I do and look as scruffy as I do by
Speaker 1: the time I go in, or or just get people
Speaker 1: get you know, really well groomed all the time. Let's say, uh,
Speaker 1: ok or you can go with high thirty if you want.
Speaker 1: I feel like like thirties, not too high. Okay. It
Speaker 1: feels like enough to where you would say, hey, you
Speaker 1: got a haircut, didn't you, Whereas if you go too low,
Speaker 1: you more attempted to say, hey, your hair is a
Speaker 1: little wetter than normal or something, you know. I mean,
Speaker 1: thet seems like it would be like a comfortable level
Speaker 1: of notice, but not a woe did you join a
Speaker 1: cult level of haircut? Okay, Well, that gives us a number. Actually,
Speaker 1: So if we say that the average person has a
Speaker 1: quarter pound of hair, and that thirty percent of their
Speaker 1: hair is removed in the average haircut, that means that
Speaker 1: the average haircut in America removes point zero seven five
Speaker 1: pounds of hair Okay, Now that's going to vary widely
Speaker 1: up and down again, but we're just trying to get
Speaker 1: an average. Now, if we say that the average haircut
Speaker 1: removes x amount of hair, all we need to know
Speaker 1: now are how many haircuts there are in America every year,
Speaker 1: So we already know how many people there are. How
Speaker 1: often would you say that the average person gets a haircut? Oh,
Speaker 1: this is this is a tough one, right, but I'm
Speaker 1: guessing once every two months. Okay, so six times a year. Yeah,
Speaker 1: that feels maybe a little. That's a little higher than
Speaker 1: what I actually tend to do, like I might do
Speaker 1: it four times a year. Now that they think about it, well,
Speaker 1: let's take the average and go five times. I feel
Speaker 1: like I'm not being very consistent with my mathematic people
Speaker 1: are trying to figure out how fast my hair grows
Speaker 1: based on my strange figures. Here, I guess, but you
Speaker 1: know that sounds good. Okay, So in this case, uh,
Speaker 1: if you get point zero seven five pounds of hair
Speaker 1: removed every time you get a haircut, and you get
Speaker 1: a haircut five times a year, every year, you get
Speaker 1: point three seven five pounds of hair removed from your
Speaker 1: head point three seven five pounds removed every haircut or
Speaker 1: every year. Every year, it's point zero seven five removed
Speaker 1: per haircut, five times a year. That's point three seven
Speaker 1: five pounds. All right, Well that number is that feels
Speaker 1: right to me? Okay, Well, now all we need to
Speaker 1: do is multiply by our three d million people each
Speaker 1: each one of them gets an average of point three
Speaker 1: seven five pounds of hair removed free year, and there
Speaker 1: are three million people, so that gives us a total
Speaker 1: mass of hair removed from human heads in the United
Speaker 1: States every year of about a hundred and twelve million
Speaker 1: pounds hundred and twelve million, five hundred thousand pounds. Does
Speaker 1: that sound right? Mhmm, Well, we feel it feels more
Speaker 1: right having done the leg work, you know what I'm saying, Like,
Speaker 1: we're able to break it down. If you just come
Speaker 1: up with that number just on the fly, I might
Speaker 1: have really kind of um, you know, set there and
Speaker 1: crunched it for a while thing, And I don't know
Speaker 1: if that feels right. But since we did the legwork
Speaker 1: and we dealt with with with quantities that were more
Speaker 1: relatable in order to get there, I'm certainly more inclined
Speaker 1: to trust it now. One of the beautiful things about
Speaker 1: this type of estimation is that errors tend to balance
Speaker 1: each other out. So one of the things we were
Speaker 1: saying as we're going through is we're using very rough figures. Obviously,
Speaker 1: the population in the United States is more than three million.
Speaker 1: We just rounded down to make it easy. Um, the
Speaker 1: amount of hair on each person's head, we don't really know.
Speaker 1: It's a quarter of a pound. That was just a guess.
Speaker 1: That might be too much, that might be too little.
Speaker 1: But as you keep going through the experiment, at each stage,
Speaker 1: you are making a guess, and that guess if unless
Speaker 1: you're consistently biasing in one direction or another, always overestimating
Speaker 1: or always underestimating, your errors will start to balance each
Speaker 1: other out. And this kind of helps keep your answer
Speaker 1: within the bounds of possibility. Even if you're wrong on
Speaker 1: one thing, you might be wrong in the opposite direction
Speaker 1: on another guess. It's kind of like life, and exactly
Speaker 1: it's a lot like the game of life, or you
Speaker 1: mean the life of life. Just just uh, a life
Speaker 1: in general, not Life magazine, but you know that's part
Speaker 1: of life. Oh, I should smack myself for that joke.
Speaker 1: I'm sorry, But anyway, whether or not our answer is correct.
Speaker 1: It maybe totally off the mark, but we've started to
Speaker 1: give ourselves something to work with. And if we really
Speaker 1: cared about this, like if it mattered how much hair
Speaker 1: is removed from Americans heads every year, this would give
Speaker 1: us a good starting place to start working with. One
Speaker 1: of the next steps I think would be would be
Speaker 1: to go back and look at our individual factors that
Speaker 1: we put in throughout that that calculation process and try
Speaker 1: to hone them and say, really, what's reasonable. You know,
Speaker 1: we could start looking at our own heads, the heads
Speaker 1: of people around us in the offenses and saying, it's
Speaker 1: a quarter pound of hair real that sounds kind of high.
Speaker 1: I don't know, but but you you can start refining
Speaker 1: it once you've got something to work with. And that's
Speaker 1: one of the big values of firmi estimation um. Even
Speaker 1: though the method isn't likely to give you a precisely
Speaker 1: correct answer every time, scientists and engineers find this type
Speaker 1: of guessing extremely useful because it gets you into a
Speaker 1: sort of order of magnitude ballpark where you can start
Speaker 1: to check your gas against other modes of estimation or
Speaker 1: against experiments and discoverable facts, and it also helps you
Speaker 1: get your mind around what assumptions are necessary in order
Speaker 1: to compute your final precise number. Does that make sense?
Speaker 1: Like you start to realize what the uh? You take
Speaker 1: things that were unknown unknowns turned into known unknowns. Now
Speaker 1: you at least know what the variables are, even if
Speaker 1: you don't know exactly what the numbers should be. And
Speaker 1: turning an unknown unknown into a known unknown is halfway
Speaker 1: along the process to turning it into a known known
Speaker 1: or even a gnome. Well, let's hope it didn't go
Speaker 1: that far. All right, We're gonna take a quick break,
Speaker 1: and when we come back we will jump back into
Speaker 1: this question of of estimating, gus estimating and UH and
Speaker 1: so forth. Okay, we're back. Now let's look at one
Speaker 1: of the most famous examples of a Fermi estimation type
Speaker 1: problem him in history, and this would be the Drake
Speaker 1: equation and the Fermi paradox. That is an interpretation on it. Yes,
Speaker 1: all right, So in order to get this down, we
Speaker 1: have to go back to nineteen fifty. Now, if you
Speaker 1: remembering from earlier, that's what three years before Fermi's death.
Speaker 1: So go back to nineteen fifty. Firmis having lunch with
Speaker 1: his fellow egg heads at the Lost albumost Jet Propulsion
Speaker 1: Lab Cafeteria. Alright, he's flipping through a copy of The
Speaker 1: New Yorker when he happens upon a particular cartoon. Now,
Speaker 1: I have a picture of the cartoon for really, it's
Speaker 1: the original, the original, Yeah, this is the one, and
Speaker 1: I'll try to include a link to this on the
Speaker 1: landing page for this episode of Stuff to Blow your
Speaker 1: Mind dot com. So what's going on. There's a flying
Speaker 1: saucer and some space people are carrying baskets to and
Speaker 1: from it. Yeah, they're they're collecting garbage apparently, uh furiously enough,
Speaker 1: I don't have the caption here, or I don't know
Speaker 1: they were doing the caption contest back then. But if
Speaker 1: if the caption contest from The New Yorker makes its
Speaker 1: way across your social media feeds, you know exactly what
Speaker 1: sort of cartoon we're talking here. So it's not quite
Speaker 1: far side. It's not a laugh out loud funny, but
Speaker 1: you look at it and your your your wheels began
Speaker 1: to turn a little bit. And that's what happened with Firmy.
Speaker 1: He looks at this, and if he were to enter
Speaker 1: the New York the New Yorker caption contest. His caption
Speaker 1: would have been where is everybody? Because that is, according
Speaker 1: to this story, the question he asked, and he was
Speaker 1: referring to the aliens, to life beyond this insignificant rock
Speaker 1: of ours. He wondered, uh, more specifically, you know, not
Speaker 1: only like where are where these aliens at, but he
Speaker 1: wondered whether interstellar travel was even possible. And indeed, as
Speaker 1: far as we know it has not occurred. You know,
Speaker 1: I mean this when we get we kind of broke
Speaker 1: it down some of this in our the episode the
Speaker 1: Christian and I did on the expanse and the idea
Speaker 1: of just like the vast distances in our universe, like
Speaker 1: even the instances between our planets in our Solar system
Speaker 1: are pretty colossal, and when you start extrapolating that beyond
Speaker 1: our system, uh, it just gets increasingly just incredibly distant.
Speaker 1: There is so much space in space. And so he
Speaker 1: was saying, well, you know, where are they? Is it
Speaker 1: even possible for for life forms to travel between stars?
Speaker 1: Why aren't we seeing them? Why aren't we hearing from them?
Speaker 1: Exactly so FIRMI died, you know, foot four years later,
Speaker 1: at the age of fifty four, but the question that
Speaker 1: he asked lived on, and the problem filtered through the firm,
Speaker 1: these coworkers, his contemporaries, and it became something of a legend.
Speaker 1: And in nive, the astronomer Michael Hart declared that the
Speaker 1: reason we don't see any aliens is because they do
Speaker 1: not exist, which you know, that's that's one possible answer.
Speaker 1: It certainly is. And then in nineteen seventy seven and
Speaker 1: astrophysicists by the name of David G. Stevenson said that
Speaker 1: heart statement could answer firm's question, which he officially dubbed
Speaker 1: Firm's paradox. So to be clear, Fermi himself did not
Speaker 1: pose the question. The paradox is merely named for him
Speaker 1: in honor of him and in accordance with this sort
Speaker 1: of folkloric idea. Right, But the sort of general mode
Speaker 1: of guessing or gues estimating that's now known as Fermi
Speaker 1: estimation or a Fermi type problem is related to this
Speaker 1: because there is what's known as the Drake equation, and
Speaker 1: the Drake equation is kind of like playing the how
Speaker 1: many piano tuners game or in Chicago game with the
Speaker 1: Milky Way galaxy. It is a Fermi guess formulation designed
Speaker 1: to estimate the number of piano tuners in the Milky Way,
Speaker 1: or wait a minute, no, the number of technological civilization
Speaker 1: in the Milky Way galaxy, meaning the number of civilizations
Speaker 1: whose electromagnetic emissions like radio waves, we should be able
Speaker 1: to detect today. And so it takes to form. There's
Speaker 1: actually an equation, says okay, in that's the answer, and
Speaker 1: that's the number of civilizations in the Milky Way galaxy
Speaker 1: whose electromagnetic emissions are detectable. And the version of this
Speaker 1: I'm using is the one that st has on their website.
Speaker 1: And to calculate in you multiply are which is the
Speaker 1: rate of formation of stars suitable for the development of
Speaker 1: intelligent life, by f P, meaning the fraction of those
Speaker 1: stars with planetary systems. Not all stars are going to
Speaker 1: have planets, and then you multiply that by in e
Speaker 1: the number of planets per solar system with an environment
Speaker 1: suitable for life. So every solar system uh might might
Speaker 1: have planets, but wouldn't necessarily have planets within the habitable zone.
Speaker 1: It might be all too hot or too cold. And
Speaker 1: then you've got f L the fraction of suitable planets
Speaker 1: on which life actually appears. Might be a lot of
Speaker 1: nice planets out there, but they're just dead. Uh. And
Speaker 1: then f I the fraction of life bearing planets on
Speaker 1: which intelligent life emerges. Maybe a lot of planets out
Speaker 1: there just to have bacteria on them. And then f
Speaker 1: C the fraction of civilizations that develop a technology that
Speaker 1: releases detectable signs of their existence into space, So there
Speaker 1: might be intelligent life out there, but they're not making
Speaker 1: radio waves. And then finally, multiplied by L the length
Speaker 1: of time such civilizations released detectable signals into space. So
Speaker 1: many of the variables in this in this calculation are
Speaker 1: pure unknowns. Answers are all over the place for this reason.
Speaker 1: But a lot of things in here are not as
Speaker 1: unknown as they once were. For example, we're starting to
Speaker 1: get a very good sense of the fraction of stars
Speaker 1: with planetary systems and the average number of planets suitable
Speaker 1: for life in the Milky Way galaxy. We're starting to say, okay,
Speaker 1: this is about how many planets are out there. Here's
Speaker 1: the proportion of them that are, you know, not too
Speaker 1: hot or too cold to sustain life. Those are coming
Speaker 1: to within reckoning distance. Other variables about like the prevalence
Speaker 1: of emergence of life and intelligence. Those are still just
Speaker 1: big question marks, but you can still play the same
Speaker 1: game with them. You could try to set up boundary conditions, Right,
Speaker 1: what's the lowest boundary. While the lowest boundary would be
Speaker 1: I don't know, some fraction of one. I mean, obviously
Speaker 1: wouldn't be zero because we're here, so we know that
Speaker 1: it's a non zero chance that these things happen. What's
Speaker 1: the highest possible thing, Well, obviously we're not seeing these uh,
Speaker 1: these planets with life on them in our solar system
Speaker 1: other than other than Earth. Well, actually we don't even
Speaker 1: know that for sure yet. But anyway, there are a
Speaker 1: lot of ways you can try to put numbers in
Speaker 1: where these variables exist. And so I've seen estimates using
Speaker 1: the Drake equation turn up answers less than one, meaning
Speaker 1: we're almost definitely alone in the galaxy, and even our
Speaker 1: existence is a real stroke of luck. Uh. And then
Speaker 1: I've seen ones that are in the hundreds of millions.
Speaker 1: But in that case, what's the deal. Why aren't we
Speaker 1: detecting anything? Are we in some kind of protected zoo
Speaker 1: where we're you know, the aliens hiding from us? The
Speaker 1: nature reserve theory. Right. Yeah, But one interesting thing is
Speaker 1: what we mentioned earlier. Whenever you're doing these types of
Speaker 1: of estimations, uh, it's good to check them against reality.
Speaker 1: So you might think of our actual radio astronomy as
Speaker 1: a reality check on the numbers generated or the gu
Speaker 1: estimates of the Drake equation. So this is this is
Speaker 1: fascinating again because you've taken something that is like a
Speaker 1: giant gaping mystery and unknown and you boil it down
Speaker 1: into a series of essentially smaller unknowns uh nowns and
Speaker 1: and guessable factors. Yeah, exactly, You're you're making the problem
Speaker 1: workable and and so this is a way in which
Speaker 1: fermi estimation has multiple uses. I guess one of them
Speaker 1: is practical. It's just practical, and you know, when you
Speaker 1: don't know any of the actors, you can use it
Speaker 1: to come up with a reasonable guests for an answer.
Speaker 1: But the other thing is what we've been talking about.
Speaker 1: It's making a problem more understandable, even if you don't
Speaker 1: actually come up with a reasonable answer. It starts to
Speaker 1: help you get your mind around what you would need
Speaker 1: to know in order to solve it. All Right, we're
Speaker 1: gonna take a quick breaking. We come back, we're gonna
Speaker 1: discuss some of the softer social science of guessing and
Speaker 1: try to conduct an experiment of our own. Alright, So
Speaker 1: we've discussed how guesswork is art as well as science,
Speaker 1: and indeed there's certainly a social art to it in
Speaker 1: some cases, so the art of overestimation or underestimation in
Speaker 1: social situations. I think we've all encountered situations in which
Speaker 1: guessing isn't merely about making a correct guess. It's also
Speaker 1: about making a guess that lands with an appropriate level
Speaker 1: of social grace. It's like guess what my s A
Speaker 1: T score was? Yeah, like a weird questions like that
Speaker 1: like another A notable example would be guess how old
Speaker 1: I am, which is generally a question you only ask
Speaker 1: a child or you ask if you are a child. Um,
Speaker 1: because it's floated right, and I've en to your point.
Speaker 1: You also see guess how how much I make as
Speaker 1: being another question that is sometimes asked. Uh. The need
Speaker 1: for for such a guest might not come up directly,
Speaker 1: but of course we can all imagine situations where it
Speaker 1: ends up. When they end up coming up, you know,
Speaker 1: like you're trying to figure out if a friend of
Speaker 1: yours is into the same movie that you are, and
Speaker 1: you're like, oh, well, how old are you? You're such
Speaker 1: and such. You know, so you might indirectly, indirectly find
Speaker 1: yourself having to make such a guess. So this is
Speaker 1: a very conundrum. Is actually explored in the Art and
Speaker 1: Science of Guessing by Shin, c. Zong and Duh And
Speaker 1: this is published in the journal Emotion in twenty eleven.
Speaker 1: So they ask, you know, are we are we gonna
Speaker 1: be happier with over guessing or happier with under guessing
Speaker 1: just in general, like people guessing too high or guessing
Speaker 1: too low? Yeah, how does that make you feel when
Speaker 1: someone get over or under estimate something about you? Now?
Speaker 1: Is this limited to certain types of factors? Are they
Speaker 1: trying to get a general effect for any sorts of numbers?
Speaker 1: Um general? But like they're they're focusing around very specific
Speaker 1: questions as as we'll discuss. So they predicted that over
Speaker 1: guessing would reign supreme. Uh, though obviously not with guessing
Speaker 1: another person's age, because that one kind of stands out
Speaker 1: generally you want people to get through you're younger than
Speaker 1: you are, Okay, So naturally the research has conducted a
Speaker 1: few tests to try this out, and it's important to
Speaker 1: note culturally, as we'll get into that. Some experiments were
Speaker 1: conducted into China and others in the US, and that's
Speaker 1: especially important with experiment one, which concerns asking friends how
Speaker 1: much money they make, which I don't know about about you,
Speaker 1: but generally that that's not something that is done at
Speaker 1: dinner parties. Did I go to where people say, hey,
Speaker 1: how much money do you make in a year? Not
Speaker 1: my friends. I asked my enemies how much? Yeah, you know,
Speaker 1: I guess with family members, maybe it's more practical. Originally,
Speaker 1: friends and contemporaries are not asking that question. It's kind
Speaker 1: of taboo, but according to the research in China, it
Speaker 1: is was more common. So they used forty employees from
Speaker 1: multiple companies in a large city in China, and I'll
Speaker 1: spare you the monetary details of the study, but the
Speaker 1: finding was quote contrary to what common wisdom and existing
Speaker 1: literature would suggest. The study revealed a happier with under
Speaker 1: guessing effect. So someone thinks, oh, well, you just you
Speaker 1: probably make thirty thousand a year, but you actually make
Speaker 1: thirty five, but you feel happy, So I guess it's
Speaker 1: like like, oh, you get to prove them wrong. You
Speaker 1: get to prove them wrong. Yeah, you're like, oh, you
Speaker 1: think I'm only worth that much, but I'm actually worth
Speaker 1: this much. I'm fantastic. That's kind of the response. No,
Speaker 1: that makes sense to me. So. Experiment to tackled academic
Speaker 1: performance with American test subjects a hundred and seven business
Speaker 1: students guessing each other's GMAT scores as a graduate Management
Speaker 1: admissions test. The results the under guest was most pleasing,
Speaker 1: the over guests was least pleasing, and the accurate guest
Speaker 1: was in between. I think it's interesting here that the
Speaker 1: accurate guess is somewhere in between, Like, nobody really wants
Speaker 1: to be pinned down completely. No, it doesn't feel good, yeah, even,
Speaker 1: but it also feels bad to be overestimated, Like it's
Speaker 1: the the inner Like if you're underestimated, you you get
Speaker 1: that that feeling of oh, I'm actually I'm actually better
Speaker 1: than you think I am. But if they if you're overestimated,
Speaker 1: there might be like this superficial feeling of oh they
Speaker 1: think I'm they think I'm better than I am, but
Speaker 1: but I'm actually not. It might be nice to have
Speaker 1: people guess, like what your favorite movies are or something.
Speaker 1: But it does not seem like it's nice to have
Speaker 1: people correctly guess what numbers are true about you. Yeah,
Speaker 1: it's it's a quantitative aspect that makes accuracy unpleasant. It's
Speaker 1: like being pinned down to a chart. So then came
Speaker 1: Experiment three two thousand and nine. Business students from a
Speaker 1: large university in the United States engaged in imagined scenario. Okay,
Speaker 1: you work at a large company. Your annual bonus will
Speaker 1: be between three thousand and thirty thousand dollars. Exact amount
Speaker 1: will be confidential. So participants were then told, in this
Speaker 1: imaginary experience experiment here uh scenario that they'd receive fifteen
Speaker 1: thousand dollars, and they were asked to imagine that they
Speaker 1: heard a colleague guessing about their bonus. The guests was
Speaker 1: thirty thousand in the over guest condition and three thousand
Speaker 1: in the under guest condition, And then they were asked
Speaker 1: to indicate whether they felt better or worse about hearing
Speaker 1: the guests The results. Again, the overguess resulted in the
Speaker 1: most happiness, But the researchers drive home that a lot
Speaker 1: of that results boils down to what's more important. To
Speaker 1: the individual individual truth or impression. So really, really, what
Speaker 1: ends up mattering more to a specific individual the actual
Speaker 1: amount of money they take home or the amount of
Speaker 1: money that people think they take home. And this is interesting, right,
Speaker 1: because so much in life is this mixture of substance
Speaker 1: and perception. Do you want to be rich or do
Speaker 1: you want to appear rich? Do you want to be
Speaker 1: smart or appear smart? And and and there's kind of
Speaker 1: this this up is this push and pull of both factors.
Speaker 1: We're back to the charm effect, the James Bond effect,
Speaker 1: And we were talking about at the beginning some people
Speaker 1: might actually not be uh better, more lucky than others,
Speaker 1: but they can sure appear that way just by sort
Speaker 1: of projecting a successful latitude. Yeah. Yeah, So the soft
Speaker 1: science of guessing becomes even softer some more you you
Speaker 1: you tease at it. Okay, one last thing, I want
Speaker 1: to look at a totally different kind of guessing. We've
Speaker 1: talked about tools to make you better at guessing, but
Speaker 1: I want to think about what goes on in the
Speaker 1: human mind when we guess. When we've got absolutely nothing
Speaker 1: to work with, no info, no probabilities, no plausible boundaries,
Speaker 1: just the opaque magic of pure randomness, because this is
Speaker 1: this is sort of the core of guessing. When we
Speaker 1: say guessing, you know, a lot of what guessing conjures
Speaker 1: in the mind is scenarios of total uncertainty randomness. Okay,
Speaker 1: so I want to do an experiment with you, Robert.
Speaker 1: I've got a deck of cards fanned out here. Here's
Speaker 1: the experiment. I'm holding up a card to Robert. Okay,
Speaker 1: what is the suit of this card? Now you are
Speaker 1: not looking at the face of the card. Robert is
Speaker 1: looking at the back of Well, this is awesome because
Speaker 1: I I have a one in four chance, right right,
Speaker 1: I'm gonna say clubs, Nope, jack of spades. Now let
Speaker 1: me try it again. Now, think really hard, this time
Speaker 1: the exact card. No, you are guessing the suit. Okay,
Speaker 1: I'm gonna say clubs, nope, hearts. But here's the question.
Speaker 1: Where did your answers come from? Your accuracy was actually
Speaker 1: not important to me. There, I'm thinking about the subjective experience.
Speaker 1: Try it one more time, Nope, spades. Why though, why
Speaker 1: did you say clubs when you have no reason to
Speaker 1: prefer clubs over any other I don't know, it just
Speaker 1: came to my mind. For I was for it's it's
Speaker 1: almost like not that I was at a loss for
Speaker 1: the words, but like that was the one that came
Speaker 1: up first. Yeah, I mean, it's it's a weird thing.
Speaker 1: It's like, next time you make a guess without a
Speaker 1: conscious methodology, you out there listening, look inside yourself and
Speaker 1: ask this question, where did that guests come from? Why
Speaker 1: did I say clubs and not something else when I
Speaker 1: had no logical reason to prefer clubs over anything else.
Speaker 1: I will say I stuck to clubs because I thought
Speaker 1: clubs has got to come up, like I might as well,
Speaker 1: even though I guess it's it's yeah, yeah, it seemed
Speaker 1: like the thing to do, like I just should should
Speaker 1: just stick to clubs and clubs will do me ride eventually. Well,
Speaker 1: that actually would be a smart strategy. If I was
Speaker 1: like removing cards from the deck and you were yeah, okay, okay, Well,
Speaker 1: then I guess the question would be, what what did
Speaker 1: you guess the first time? Or what would you have
Speaker 1: guessed if I was not removing cards from the deck,
Speaker 1: Because that yeah, there there's no there's just nothing you
Speaker 1: can do, and yet our brains still are able to
Speaker 1: come up with an answer. And I think this is
Speaker 1: one of those everyday moments that sort of passes by
Speaker 1: us without much fanfare. Just it's very humdrum, But if
Speaker 1: you force yourself to stop and examine it, it becomes
Speaker 1: so deeply weird and mysterious. We've we've got these voids
Speaker 1: inside our minds that produce information on no input. It's
Speaker 1: kind of like you you go somewhere in the back
Speaker 1: of your mind and there's one of those drive through
Speaker 1: bank teller boxes, you know, where it slides out and
Speaker 1: you open the shutter, and what you put in is
Speaker 1: just a request for a random response, and you push
Speaker 1: it in, and a split second later, the box slams
Speaker 1: back out, pops open with an answer for you. What
Speaker 1: happened inside? Where did that random answer come from? Uh?
Speaker 1: That might not even occur to you as something to
Speaker 1: think about being odd, But I don't know. It strikes
Speaker 1: me as very odd. Why do our brains come up
Speaker 1: with random answers on command, with no logical reasoning behind them.
Speaker 1: One example that I do encounter with this sometimes is
Speaker 1: in yoga class will be and we'll be doing a plank,
Speaker 1: and in order to pass the time, we'll go through
Speaker 1: the alphabet and like name trees that begin with each letter,
Speaker 1: and it's curious to self reflect and be like why
Speaker 1: did that tree come up? Why did that animal come up? Yeah,
Speaker 1: and sometimes it feels like the brain just spits it
Speaker 1: out randomly, like a like a hand with a deck
Speaker 1: of cards just shooting one to the surface. Yeah, so
Speaker 1: what's causing one card to come up instead of another? Um? So,
Speaker 1: in terms of coming up with true randomness, I've actually
Speaker 1: read a little bit about research into people studying humans
Speaker 1: ability to generate random numbers on command, Like this is
Speaker 1: actually a field of study. It's like, can you please
Speaker 1: list a series of random one digit numbers? One problem
Speaker 1: is that people are actually very cruddy random number generators,
Speaker 1: Like they they either have too much symmetry in their
Speaker 1: answers or too little symmetry. Um Like that they get
Speaker 1: caught up in trying to make it random, and thus
Speaker 1: they make it non random. But yeah, I just think
Speaker 1: it's interesting, Like what's what's the biological purpose of that? Like,
Speaker 1: why is that something brains can do? It's something you
Speaker 1: specifically have to have to command computers to figure out
Speaker 1: how to do. Computers by nature don't generate random numbers.
Speaker 1: You need to come up with a way of them
Speaker 1: to you know, draw on some kind of vada variable
Speaker 1: or data to generate random numbers. UM, So like why
Speaker 1: do brains do that? And where do the numbers come from? Uh?
Speaker 1: There there was one study that I looked at that
Speaker 1: I thought was kind of interesting, and it's a study
Speaker 1: by Elliott Rees and Dolan in the journal Neuropsychologia and uh.
Speaker 1: And what they did is they used f m R
Speaker 1: I to see if there were any differences in activation
Speaker 1: patterns in the brain between reporting on knowledge and random guessing.
Speaker 1: So in one group, researchers would show subjects a playing
Speaker 1: card on the face side. Here you go, Robert, what
Speaker 1: card is this? That would be a five clubs? Right.
Speaker 1: Because I'm showing you the card, you're just reporting. This
Speaker 1: is working memory in the brain. You're taking in information,
Speaker 1: you're spitting it back out. Not all that weird. It's
Speaker 1: a very different thing to hold up the back of
Speaker 1: a card and say what's the card here? You have
Speaker 1: no information at all, So you randomly guess six of diamonds,
Speaker 1: four of diamonds. Kind of close, kind of close. That's
Speaker 1: like a ballpark. Uh, you're within an order of magnitude.
Speaker 1: But I think I randomly said six only because I
Speaker 1: had just said five. Right. But when you're when you're
Speaker 1: guessing the front of a card, just looking at the back,
Speaker 1: there's no gunball logic, there's no firmi estimation to help you.
Speaker 1: It's just random. And yet the authors found that something's
Speaker 1: going on in the brain when we're generating random guesses.
Speaker 1: There is activity. Uh, they write, if their analysis is correct,
Speaker 1: they write, quote, these data suggests that while simple two
Speaker 1: choice guessing depends on an extensive neural system, including regions
Speaker 1: of the right lateral prefrontal cortex, activation of orbit of
Speaker 1: frontal cortex increases as the probabilistic contingencies become more complex,
Speaker 1: as it becomes harder to understand, you know, what's going on,
Speaker 1: so they say quote. Guessing thus involves not only systems
Speaker 1: implicated in working memory processes, but also depends upon orbitofrontal cortex.
Speaker 1: This region is not typically activated in working memory tasks,
Speaker 1: and its activation may reflect additional requirements of dealing with uncertainty.
Speaker 1: Their specific patterns going on in the brain when you're
Speaker 1: trying to generate random answers, and I just think, like,
Speaker 1: what's the biological function of that. Where does that come from? Why? Why?
Speaker 1: Why do animals have this ability with the brain to
Speaker 1: generate randomness? I don't know that's that. It's a wonderful question. Though.
Speaker 1: We've been talking a lot about cognitive tools rules of thumb,
Speaker 1: But there is another way of thinking about people who
Speaker 1: are good at guessing. As we said, you know, obviously
Speaker 1: some people are better at guessing and guestimating than others,
Speaker 1: but obviously not all of them are using these tools.
Speaker 1: Right when you think about people you know who are
Speaker 1: very good guessers, they're not necessarily doing firm me calculations,
Speaker 1: coming up with numbers in their head, uh, exploring boundaries,
Speaker 1: taking geometric means, and multiplying things together. A lot of
Speaker 1: times it seems to be just intuitive. So I wonder
Speaker 1: if there's another way to think about differential skill levels
Speaker 1: and guessing, and if it's more like finesse at certain
Speaker 1: sports and athletic activities, meaning that when you think about
Speaker 1: somebody who's good at hitting shots in basketball, what is
Speaker 1: that skill? It's obviously not an issue of raw strength.
Speaker 1: It's not speed, it's not endurance. If somebody can't hit
Speaker 1: three pointers, it's usually not because they're not strong enough
Speaker 1: to get the ball to the hoop. When you shoot
Speaker 1: in basketball, at some level, what you're doing is math.
Speaker 1: Obviously you're not consciously making calculations, but you're you're trying
Speaker 1: to calculate and execute a precise arc trajectory, factoring the
Speaker 1: distance and the distance to the hoop, presence the backboard,
Speaker 1: the bounciness of the ball. It's kind of like you're
Speaker 1: playing you do you ever play that old game the
Speaker 1: gorilla throwing the banana at each other? No, but it's
Speaker 1: it sounds fun. Yeah, but yes, well it's an old game,
Speaker 1: like an old basic game. You'd have two guerillas standing
Speaker 1: on rooftops and you'd enter the angle and the velocity
Speaker 1: of this bomb banana throw. Yeah. I like though that
Speaker 1: this is like it's like you're just throw bananas at
Speaker 1: each other in Virginia Guerrillas. But well, that would involve
Speaker 1: calculating precise arc trajectories too, I mean, trying to hit
Speaker 1: something by throwing it. In a sense, you are doing math,
Speaker 1: even if you're not consciously doing math. Um. So perhaps
Speaker 1: in some ways I wonder if certain kinds of skill
Speaker 1: in sports should be thought of as having less to
Speaker 1: do with the power of the body and being more
Speaker 1: like an unconscious version of the mind of a highly
Speaker 1: skilled guesser, like an intuitive for me. And in the
Speaker 1: same way, I wonder if there's something unconscious in your
Speaker 1: nervous system that's able to make good guesses about precise
Speaker 1: angles and velocity to sink a three pointer. Uh, there
Speaker 1: might be other ways in which we have un conscious
Speaker 1: intuitions that are nevertheless doing some kind of math. Math
Speaker 1: is is being calculated in the brain, even if we're
Speaker 1: not aware of it, in some cases, giving some people
Speaker 1: better intuitions about guessing than others, even without doing all
Speaker 1: this math. I don't know, just something to think about.
Speaker 1: All right. Well, on that note, we're gonna go ahead
Speaker 1: close out here. Hey, as always, check out stuff to
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