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Speaker 1: Welcome to Stuff to Blow Your Mind from how Stuff

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Speaker 1: Works dot com. Hey, welcome to Stuff to Blow your Mind.

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Speaker 1: My name is Robert Lamb and I'm Joe McCormick and Robert.

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Speaker 1: I want you to think about something I know you've

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Speaker 1: seen many times before. Okay, you've watched James Bond movies, right,

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Speaker 1: of course. I grew up watching James Bond movies mostly

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Speaker 1: on I think TBS on Thanksgiving Day and that one

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Speaker 1: they show it. I think they would, but it seems

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Speaker 1: like they just chewed it all the time. Like every

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Speaker 1: weekend it was what what Bond movie will be sort

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Speaker 1: of watching this weekend? And you would you would hope

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Speaker 1: where I would often those days, I would hope it

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Speaker 1: would be the Sean Connery. Nowadays, I think if I

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Speaker 1: were to do what, I would say, Roger Moore please. Yeah,

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Speaker 1: the more ones are the cheesier ones, they're better for

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Speaker 1: Thanksgiving Day. Yeah, I think so, like the Sean Connery

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Speaker 1: ones might be better movies, but they kind of just

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Speaker 1: they have this tinge of alcoholism and misogyny that well, no,

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Speaker 1: I guess the Roger Moore wins due to they do.

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Speaker 1: It's kind of just part of the character I think

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Speaker 1: you find that in every variation. Yeah. Anyway, So James Bond,

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Speaker 1: what does he do when he walks up to a

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Speaker 1: gambling table? What happens every time he walks up, lights

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Speaker 1: a cigarette, makes some dirty word play with a female

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Speaker 1: gambler or something like that, and then he gets a

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Speaker 1: black check hand. What happens, Well, he tends to win.

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Speaker 1: He wins every time. He always wins. When James Bond

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Speaker 1: never loses, unless it's like a specific scene where gambling

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Speaker 1: is crucial to the plot and he must lose, like

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Speaker 1: in Casino Royality. Yeah, like, I can't even I think

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Speaker 1: I can't remember if he won or lost in gold Finger.

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Speaker 1: But there's a scene in gold Finger where, uh, gold

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Speaker 1: Finger himself, you know, the villain of the piece, like

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Speaker 1: cheats at cards. I think by by having somebody in

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Speaker 1: in one of the high rises. Oh yeah, here's thinking

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Speaker 1: James Bond isn't playing he gold Fingers cheating somebody else? Okay,

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Speaker 1: but does he then play Goldfinger and win? It sounds

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Speaker 1: like the kind of thing Bond would do. I don't

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Speaker 1: think he ever played. He plays him, he plays him

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Speaker 1: in golf and then and then they both cheat. Anyway,

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Speaker 1: getting past this, okay, Yeah, but so he always wins.

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Speaker 1: He all, he goes up, he hits twenty one right

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Speaker 1: on the first throw every time first throwers what you

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Speaker 1: call it the first hand. Um, And so my question

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Speaker 1: is do you believe that there are people like that?

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Speaker 1: Obviously there is luck in the sense that there are

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Speaker 1: differential outcomes. You can have a lucky thing happen to you,

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Speaker 1: you can have an unlucky thing happened to you. But

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Speaker 1: do you believe there are people who are consistently lucky? Well,

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Speaker 1: I'm sure plenty of our listeners have played the various

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Speaker 1: role playing games, you know, video games as well as

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Speaker 1: pin and paper games, and if you have, you've probably

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Speaker 1: encountered characters or character management systems where there's an actual

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Speaker 1: numerical luck rating for the character. Right, so you can

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Speaker 1: like eight yourself higher on luck. Yeah, so you know, well,

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Speaker 1: this character and then their strengthened in that grave. Their

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Speaker 1: dexterity is a little lacking, but their luck skill is amazing.

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Speaker 1: That's not a skill well I know, or an attributed

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Speaker 1: ll but but yeah, if you play enough role playing games,

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Speaker 1: that makes you think, yeah, I wonder what my my

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Speaker 1: my luck rating is? Am I on a nine or

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Speaker 1: a ten. Um. So is it like that in real life? Well,

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Speaker 1: I think obviously the answer is no. Um. Though actually

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Speaker 1: I want to go back on what I said a

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Speaker 1: second ago, because I said, that's not a skill. It

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Speaker 1: may be true in the sense that some things that

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Speaker 1: look like luck are in fact skills, But personally I

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Speaker 1: don't don't believe in this karmic version of luck. I

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Speaker 1: would assume Robert, you probably don't either, I know, not

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Speaker 1: not per se not not scientifically speaking, not in terms

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Speaker 1: of like you having some kind of store of spiritual

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Speaker 1: capital holding sway over future outcomes, right. I mean, if

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Speaker 1: I was to adjust my the lenses through which view

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Speaker 1: reality and uh and choose to load up more mystical

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Speaker 1: religious views of the world, I might engage in this

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Speaker 1: into a certain amount of magical thinking. Some folks are

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Speaker 1: are lucky that some folks are, I don't know, guiding

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Speaker 1: themselves to the multiverse of possibilities along like the most

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Speaker 1: victorious line possible. But from a strictly like real world

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Speaker 1: scientific pragmatic point of view, no, absolutely not, however, though

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Speaker 1: I mean, I think we can both agree that even

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Speaker 1: in the sense of a real world scientific pragmatic point

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Speaker 1: of view, there are some people who do seem to

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Speaker 1: be more consistently lucky than others, And I think this

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Speaker 1: is because random events are it's or it's not because

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Speaker 1: of random events being brought to heal by luck magic.

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Speaker 1: It's because people are able to influence events in ways

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Speaker 1: that are not in fact random, just look random from

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Speaker 1: the outside. So, for example, a person who's really confident

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Speaker 1: and positive might not actually have more good outcomes on

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Speaker 1: average than somebody else. But when you think of that person,

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Speaker 1: when you think of your friend who's really confident and positive,

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Speaker 1: you're more likely to count the hits and discard the misses.

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Speaker 1: You know, this selection bias thing. Good outcomes seem in

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Speaker 1: character for that person. They sort of get added to

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Speaker 1: the character sheet. You're like, yeah, that's that's them. Well,

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Speaker 1: you know, bad outcomes you just ignore. That's like that's noise. Yeah,

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Speaker 1: I mean James Bond is a classic example. We think

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Speaker 1: about James Bond to you know, of course, the fictional

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Speaker 1: character spread out across various movies and books, and we think, oh,

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Speaker 1: he wins all the time, he always gets the girl.

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Speaker 1: But there's a scene in What's the George lasonby movie

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Speaker 1: um on Our Majesty's Secret Service. Yea, his wife is

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Speaker 1: murdered by by Telexavalis's Blowfield's Man. You know, just spoilers

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Speaker 1: well killed and spoiler for you know, arguably one of

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Speaker 1: the lesser James Bond films. Oh no, it's some people's favorite. Uh.

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Speaker 1: I enjoyed it. But but yeah, like there's a super

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Speaker 1: traumatic moment like who would want? I wouldn't want? I

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Speaker 1: would I would not want all of the you know,

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Speaker 1: alleged benefits of Bond's life if I also meant I

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Speaker 1: had to experience like that kind of a low. So

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Speaker 1: even with James Bond, we're forgetting all the torture scenes

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Speaker 1: and the injuries and the dead wife, and we're focusing

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Speaker 1: on the stuff that we are in via stuff. Sure, okay,

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Speaker 1: so that's just like influencing people's perceptions of you. But

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Speaker 1: what what if you are actually you actually have more

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Speaker 1: good outcomes than average. I think in in cases like this,

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Speaker 1: there are a lot of things that we think of

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Speaker 1: as luck that are in fact skill. One example would

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Speaker 1: be some forms of gambling. Now it's true that there's

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Speaker 1: no skill involved in getting lucky cards at blackjack, but

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Speaker 1: there could be skill involved in other aspects of gambling,

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Speaker 1: like in poker, knowing how and when to bet so

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Speaker 1: as to manipulate your opponents. Uh, you can turn even

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Speaker 1: a bad hand into a winning hand in poker. In

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Speaker 1: black jack, you you know you can't control what cards

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Speaker 1: you get, but if you can count cards, if you

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Speaker 1: know the odds on any given play, if you know

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Speaker 1: you know, okay, here are the cards I have, and

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Speaker 1: here's what the dealer is showing. I know the odds

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Speaker 1: of what I should bet. You can sort of start

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Speaker 1: to leverage an advantage. In black check. I think you

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Speaker 1: still probably can't get better than but but there is

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Speaker 1: some skill involved there. And don't count out just flat

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Speaker 1: out cheating. Oh of course, I mean the most important

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Speaker 1: skill in peopling. It's the skill that the house has

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Speaker 1: leveraged against you. With your consent, you agree to a

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Speaker 1: game that they openly acknowledge they have rigged. This is

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Speaker 1: true and nice call back to our slot Machines episode

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Speaker 1: that we recently republished. Right. Uh. And another way to

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Speaker 1: think about this, Uh, this concept of skill versus luck

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Speaker 1: is in the realm of guessing. I think guessing is

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Speaker 1: a really interesting phenomenon for human beings because we use

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Speaker 1: this word a lot of different ways. Some times we

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Speaker 1: use it to mean, uh, you know, just going with

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Speaker 1: a gut feeling when you have no information. Sometimes we

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Speaker 1: use it to mean coming up with an answer on

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Speaker 1: very limited or little information. But but generally it means

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Speaker 1: like trying to produce a piece of information without a

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Speaker 1: strong determinative process to get you there. Well, I think

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Speaker 1: in a lot of cases it's it's the kind of

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Speaker 1: it's kind of artificial scenario that would not exist out

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Speaker 1: of the human realm, such as I think one of

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Speaker 1: the classic examples would be a multiple choice test. Then

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Speaker 1: maybe you didn't study for all that well, right, and

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Speaker 1: so you have suddenly are forced to answer a question

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Speaker 1: that you just have no idea about. Maybe you can

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Speaker 1: eliminate one possible answer. If you're still left with three

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Speaker 1: likely answers, and you just have to go with your guy,

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Speaker 1: you just gotta guess. You gotta get a wild shot

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Speaker 1: in the dark. Yeah. And and from this concept we

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Speaker 1: we have this concept of the lucky guess. Of course,

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Speaker 1: people who are lucky guessers who seem to have a

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Speaker 1: much better than average hit ratio at tossing out a

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Speaker 1: correct or nearly correct answer to a question even when

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Speaker 1: you've got essentially no knowledge or very little information to

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Speaker 1: work with. And that's what I want to talk about today,

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Speaker 1: about this, this process of guessing, and about how in

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Speaker 1: many cases things which appear to be random lucky guesses

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Speaker 1: are not in fact random. There's a skill, you know,

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Speaker 1: there's a skill and art and a science to many

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Speaker 1: different kinds of guessing and smart guessing. Uh. And there

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Speaker 1: are even a few techniques that you can harness for

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Speaker 1: yourself to get a little bit better at guessing than

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Speaker 1: you might be if you're just always going with your gut.

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Speaker 1: So one thing I wanted to do. I don't know

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Speaker 1: how many good answers we can really come up with here,

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Speaker 1: but I was wondered, like, who are some people who

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Speaker 1: are some famous, really good guessers. I've got one answer,

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Speaker 1: but other than that, I don't know. You know people

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Speaker 1: like this personally, right, You've got friends who you know

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Speaker 1: are better guessers than others. But in terms of finding

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Speaker 1: like historic moments and saying like legends of guessing. I

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Speaker 1: did some poking around, and there aren't a lot of

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Speaker 1: great options, like you know, military history, etcetera. There aren't

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Speaker 1: situations where someone just takes a wild guess and it

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Speaker 1: pays off and it becomes the stuff of just absolute legends. Yeah,

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Speaker 1: one of the few examples I could come across. And again,

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Speaker 1: this is not a high stake situation. I mean, it

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Speaker 1: kind of is for one specific person, but it's not

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Speaker 1: a warfare scenario, and it's taking place within a very

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Speaker 1: artificial human environment, not the multiple choice quiz but the

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Speaker 1: game show. All right, So Wheel of Fortune, Wheel of Fortune,

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Speaker 1: this was This occurred on a two thousand fourteen episode.

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Speaker 1: So you had this contestant by the name of Emil

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Speaker 1: de Leon and he had If you're familiar with Wheel

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Speaker 1: of Fortune, it's where you have you know, those blank

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Speaker 1: uh blank places where the letters go on the It's

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Speaker 1: like a combination of Roulette and scrabble. Yeah, so it's

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Speaker 1: it's it's a very specific game. It's you know, if

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Speaker 1: you're like me, you at least grew up watching your

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Speaker 1: grandparents watch it, and a lot of people watch it regularly.

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Speaker 1: It's a it has a certain system in play, and

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Speaker 1: it's kind of neat to to sit there and play

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Speaker 1: along at home, you know what. It's actually more like,

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Speaker 1: I don't know why we're explaining this. Everybody's seeing wheels

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Speaker 1: watch but no, I mean if you actually haven't. It's

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Speaker 1: like the game Hangman, where you guess letters. You have

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Speaker 1: a set number of spaces. Uh you know they're like

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Speaker 1: eight maybe you know there are eight letters in this

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Speaker 1: word and you're trying to guess letters and if you

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Speaker 1: get one right, it gets filled in there you go, Yeah,

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Speaker 1: it's it's Hangman, with with with with with monetary rewards

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Speaker 1: and Pat say Jack, okay, okay, So uh, the leon

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Speaker 1: is playing all right, and there's like a there's a

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Speaker 1: three word problem up on the board and the only

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Speaker 1: letter up there is in, so that the it's in

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Speaker 1: blank blank space blank blank blank blank space blank blank

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Speaker 1: blank blank blank. Right, that's what it is. That's it.

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Speaker 1: That's always got to go on. What would you guess?

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Speaker 1: I don't know, see unlikely leon. I haven't put a lot.

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Speaker 1: I haven't put a lot of thought into the system

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Speaker 1: of it. Like here's a guy who watched it pretty

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Speaker 1: religiously and then he was gonna, you know, he got

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Speaker 1: to go on the show. So I think he was

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Speaker 1: he was very much in the mind to try and

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Speaker 1: game the system. I look, if I were to look

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Speaker 1: at those blank spaces. I don't know what I guess,

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Speaker 1: uh new rats lover. That doesn't work now, I give up. Well,

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Speaker 1: but you got the new, all right, so you figured

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Speaker 1: that part out. And indeed he also guessed the new,

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Speaker 1: but he also went all the way and guessed new

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Speaker 1: baby Buggy and one sixty three thousand. Yeah, cheating must

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Speaker 1: have been cheating. Well, some people leveled that charge and

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Speaker 1: he uh, he ended up explaining himself because this was

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Speaker 1: apparently a big deal, like even Pat say Jack said

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Speaker 1: this was the craziest guests ever in his history of

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Speaker 1: hosting the show and when they When Leon Julianne was interviewed,

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Speaker 1: he said that, well, first of all, he'd been watching

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Speaker 1: the show for some time. He knew the game inside out,

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Speaker 1: and he knew that knew had to be the first

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Speaker 1: word like that was even we got that. You know

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Speaker 1: that if it's in blank blank? How are many? How?

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Speaker 1: What what are some more common words that come to mind?

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Speaker 1: Not many? Maybe not not not now, but he he,

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Speaker 1: I guess watched it enough to know that a lot

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Speaker 1: that's probably gonna be new. And then he said that

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Speaker 1: since he was studying for nursing exams, he had babies

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Speaker 1: on the brain, so he just kind of it just

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Speaker 1: happened to be that that, like baby is the perfect

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Speaker 1: four letter word. I don't is new baby buggy like

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Speaker 1: a like a common phrase I don't not in in

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Speaker 1: my experience, So that's like an expression that I'm not

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Speaker 1: familiar with. I mean, I guess it's like a new baby.

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Speaker 1: Is it like a some sort of a rhyming nursery

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Speaker 1: rhyme kind of a thing or tongue twister? I guess

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Speaker 1: is a tongue twister. Maybe that's the origin there, but

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Speaker 1: uh yeah, it just seems kind of crazy that that

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Speaker 1: he just instantly produces the answer to this seemingly out

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Speaker 1: of nowhere. Uh As it turns out it's not quite

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Speaker 1: out of nowhere. He at least had a very educated

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Speaker 1: guests on that first word, and then his prior experience

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Speaker 1: just happened to ease him into those last two words. Okay,

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Speaker 1: Well that that might you might actually just call that

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Speaker 1: luck in some ways, like it might know the game

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Speaker 1: well enough to see new there. But I mean, those

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Speaker 1: other words could have been anything, right, but his experience

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Speaker 1: prepared him to be lucky in a way that other

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Speaker 1: people would not have been lucky, like if they had

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Speaker 1: not a watch the show a bunch of times, which

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Speaker 1: I don't know if anybody ever just shows up on

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Speaker 1: Will of Fortune, they've basically never seen the show. They

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Speaker 1: do this new baby buggy puzzle every other week. Yeah,

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Speaker 1: so you know, I I think you can you can

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Speaker 1: make the argument either way. But yeah, I would say

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Speaker 1: that his his experiences put him in just the right

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Speaker 1: position to to to be a little quote unquote luckier

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Speaker 1: than other people. Okay, well, I mean, so whatever is

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Speaker 1: happening in that scenario, we do know that, at least

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Speaker 1: in much the same way, somebody who appears to be

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Speaker 1: a consistently lucky gambler might just be a skilled gambler

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Speaker 1: counting cards, calculating odds, manipulating opponents. Um. When it comes

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Speaker 1: to numerical values, uh, somebody who appears to be a

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Speaker 1: lucky guess or with numbers is more likely to be

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Speaker 1: a skillful guesser figuring out how to leverage existing knowledge

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Speaker 1: that you wouldn't even thought of to take into account

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Speaker 1: into a kind of ballpark accurate guess. And one person

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Speaker 1: who's famous for this is the Italian physicist Enrico Fermi

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Speaker 1: So Fermi lived from nineteen o one to nineteen fifty four. UM.

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Speaker 1: He grew up in Italy. After the passage of anti

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Speaker 1: Semitic restrictions and fascist Italy in ninety eight, for Me

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Speaker 1: and his family fled to the United States, where he

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Speaker 1: ended up working on the Manhattan Project and in his

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Speaker 1: role for Me, was present for the first test of

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Speaker 1: the atomic bomb on July sixteenth, ninety the Trinity Test.

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Speaker 1: You've heard about this, and at the time, this was

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Speaker 1: new territory. Nobody had ever tested a nuclear weapon before,

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Speaker 1: you know, a fission weapon with this big yield. They

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Speaker 1: didn't know exactly what was going to happen. You know,

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Speaker 1: the physicists had their calculations. Uh, they were fairly confident

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Speaker 1: that the device would explode. It was this plutonium implosion

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Speaker 1: bomb that they called the Gadget, and they thought it

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Speaker 1: would generate a large explosion, but the outcome was all

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Speaker 1: theoretical at that point. They weren't sure what the level

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Speaker 1: of energy output would be. Yeah, I remember reading that

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Speaker 1: like on the extreme ends of the spectrum, that where

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Speaker 1: there was the possibility that it could be a dud

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Speaker 1: or it could catch the air on fire. About that

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Speaker 1: that sort of thing. Yeah, and so they didn't know.

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Speaker 1: So Enrico Fermi that this great physicist who's famous at

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Speaker 1: good guesses he's there to watch the test. So picture

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Speaker 1: him there. Uh, he's there with his colleagues, and he's

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Speaker 1: at a camp about ten miles away from ground zero,

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Speaker 1: ten miles from where the bomb goes off. Jay Robert

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Speaker 1: Oppenheimer's there like scribbling notes into a Hindu epic. I'm sure,

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Speaker 1: I'm sure. Yes. So they're behind some shielding for good reasons. Uh.

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Speaker 1: And Fermi watches the blast through a board that's got

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Speaker 1: a viewing window made of welding glass. And there's a

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Speaker 1: two thousand five issue of the Nuclear Weapons Journal that

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Speaker 1: includes an article with some great quotes from Fermi and

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Speaker 1: others who were eyewitnesses to this to the event, and

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Speaker 1: Fermi wrote, So he's there, he's looking through the welding glass. Um,

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Speaker 1: and uh that he very first saw quote a very

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Speaker 1: intense flash of light that was brighter than full daylight,

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Speaker 1: and then a conglomeration of flame that rose into the sky,

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Speaker 1: and a huge pillar of smoke with an expanded head,

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Speaker 1: like a gigantic mushroom. Here's where we get our mushroom

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Speaker 1: cloud um and that rose rapidly into the clouds. Now,

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Speaker 1: when there's an explosion and you're pretty far away, there's

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Speaker 1: a time gap between when you see the flash and

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Speaker 1: when you feel the blast. Right could because why light

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Speaker 1: travels faster than sound. It's the same thing that happens

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Speaker 1: between lightning and thunder. You see the light and then

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Speaker 1: you hear the sound of the thunder. Uh So it

335
00:18:31,600 --> 00:18:35,400
Speaker 1: was about forty seconds after the visible explosion that the

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Speaker 1: air blast actually hit the observation camp. And when the

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Speaker 1: air blast arrived, Fermy did something really weird. He held

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Speaker 1: up a handful of scraps of paper about six ft

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Speaker 1: off the ground, and he dropped them and he let

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Speaker 1: them flutter away in the force of the air blast,

341
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Speaker 1: and then after seeing where they fell, he released some more,

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Speaker 1: just in regular air, no blast. And then after he

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Speaker 1: looked at how far they went, I think it was

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Speaker 1: like two point five meters or something, he quickly guessed

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Speaker 1: that the detonation had been about ten kilo tons worth

346
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Speaker 1: of explosion, meaning it released the same energy as ten

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Speaker 1: thousand tons of t n T. Now, when the actual

348
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Speaker 1: readings came in, it was about twenty kill a tons,

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Speaker 1: about twice Fermi's estimate. So he wasn't exactly right, but

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Speaker 1: this is still a remarkably good guess for having no

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Speaker 1: direct readings to work with. I mean, after all, you

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Speaker 1: think about it, can you look at an explosion ten

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Speaker 1: miles away and say how many tons of T n

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Speaker 1: T you think it's equivalent to know? I wouldn't even

355
00:19:40,040 --> 00:19:42,760
Speaker 1: know how to. I wouldn't know what order of magnitude

356
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Speaker 1: no tons to kill a tons to mega tons um.

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Speaker 1: So with just some scraps of paper watching how far

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Speaker 1: they blew in the wind, FIRMI was able to do

359
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Speaker 1: some quick calculations in his head and correctly guess within

360
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Speaker 1: the true order of magnitude. So how did he do it? Well,

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Speaker 1: we'll come back to that in a bit when we

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Speaker 1: get into the Fermi estimation method. Now, before we move on,

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00:20:07,720 --> 00:20:09,800
Speaker 1: and I think this is also of interest that the U.

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Speaker 1: S Army as well as other U S Armed forces,

365
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Speaker 1: have used the acronym SWAG before, which stands for a

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Speaker 1: scientific wild ass. Guests, now, you're not you're not swearing

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Speaker 1: on the podcast? Now? No, no, no, not necessarily. I

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Speaker 1: guess it depends on your your viewpoint here, But uh,

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Speaker 1: this was a now Robert, Uh, you know what we're

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Speaker 1: talking about here. Of course, as a guestimate, a guest

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Speaker 1: made by an expert or institution with a certain amount

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Speaker 1: of expertise in a given topic. Um, you know, it's

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Speaker 1: still a guests but but hopefully you're leveraging your best

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Speaker 1: information and making that guess. It's I think it's generally

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Speaker 1: considered a guest that comes from somebody who should know

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Speaker 1: what they're talking about, even if they don't have direct

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Speaker 1: information to work with. So you know, you might be

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Speaker 1: in a situation where, uh uh, somebody has some weird

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Speaker 1: array of symptoms and they don't really correspond to any

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00:21:07,640 --> 00:21:11,120
Speaker 1: known medical condition, and maybe you don't have any instruments.

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Speaker 1: You can't take their temperature, you can't do any lab

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00:21:13,200 --> 00:21:15,680
Speaker 1: work or whatever. But you could still have a doctor

383
00:21:15,760 --> 00:21:18,399
Speaker 1: look at them and guess what's wrong with them, or

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Speaker 1: just have a I don't know, a football player look

385
00:21:22,640 --> 00:21:25,440
Speaker 1: at them and guess what's wrong with them. Even though

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Speaker 1: the doctor doesn't have a lot of his or her

387
00:21:27,480 --> 00:21:31,840
Speaker 1: tools at their disposal. Um, they still might just have

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00:21:31,960 --> 00:21:35,440
Speaker 1: some intuitions based on their experience. Right right now, now,

389
00:21:35,640 --> 00:21:37,840
Speaker 1: you have to say it might be a football related injury.

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00:21:37,880 --> 00:21:40,720
Speaker 1: In which case the football player might have insight that

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00:21:40,800 --> 00:21:42,880
Speaker 1: someone else might not have, so in the in their

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Speaker 1: case it might be a swag if you will. Now,

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Speaker 1: I do want to point out that the wild ass

394
00:21:48,640 --> 00:21:53,880
Speaker 1: part of this is technically not um me being obscene,

395
00:21:53,960 --> 00:21:58,480
Speaker 1: because as William Saffire points out has pointed out before,

396
00:21:58,480 --> 00:22:01,359
Speaker 1: I believe in the New York Times, uh he said

397
00:22:01,400 --> 00:22:04,960
Speaker 1: that the wild ass is not a mirror of vulgarism,

398
00:22:05,000 --> 00:22:07,160
Speaker 1: as it can be found five times in the King

399
00:22:07,240 --> 00:22:12,840
Speaker 1: James Bible, most notably job behold as wild asses in

400
00:22:12,880 --> 00:22:16,800
Speaker 1: the desert go, they fourth to do their work. So

401
00:22:16,920 --> 00:22:22,120
Speaker 1: there you go. Well, of course, yeah, I knew that's

402
00:22:22,119 --> 00:22:25,480
Speaker 1: what you meant. Yeah. So, now that we've biblically grounded

403
00:22:25,760 --> 00:22:28,160
Speaker 1: the episode, I think maybe we should take a quick

404
00:22:28,160 --> 00:22:31,840
Speaker 1: break and when we come back we will jump into

405
00:22:31,880 --> 00:22:36,520
Speaker 1: mathematical estimation. So we're talking about guessing as a skill

406
00:22:36,720 --> 00:22:41,119
Speaker 1: rather than as pure blind luck. In one way you

407
00:22:41,160 --> 00:22:44,679
Speaker 1: can maybe get better than chance at certain kinds of

408
00:22:44,720 --> 00:22:49,040
Speaker 1: guessing is to leverage the power of simple observations and

409
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Speaker 1: rough math. There are a lot of situations in your

410
00:22:52,520 --> 00:22:56,080
Speaker 1: life where you might be asked to guess something and

411
00:22:56,359 --> 00:22:58,800
Speaker 1: it's at first not apparent that you can do any

412
00:22:58,840 --> 00:23:01,520
Speaker 1: better than just got feeling just come up with a

413
00:23:01,760 --> 00:23:05,520
Speaker 1: you know, this number sounds right. Uh. You know, somebody

414
00:23:05,560 --> 00:23:10,560
Speaker 1: asks how many buildings are in Atlanta and you'd be like, uh,

415
00:23:10,680 --> 00:23:12,320
Speaker 1: I don't know. You might come up with a number

416
00:23:12,359 --> 00:23:16,320
Speaker 1: and be like a hundred thousand, you know that feels

417
00:23:16,320 --> 00:23:19,520
Speaker 1: about right, but you have nothing to work with there.

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00:23:20,160 --> 00:23:23,719
Speaker 1: In many situations like this, you can do better, and

419
00:23:23,760 --> 00:23:26,040
Speaker 1: you can do better without going to the you know,

420
00:23:26,200 --> 00:23:29,440
Speaker 1: encyclopedia or the you know, city statistics to look up

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00:23:29,480 --> 00:23:33,159
Speaker 1: the information you need, because you can just leverage simple

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Speaker 1: observations with math. One great example of this, I think

423
00:23:37,800 --> 00:23:41,040
Speaker 1: would be the gumball jar contest. Oh yeah, you see

424
00:23:41,080 --> 00:23:44,000
Speaker 1: variations in this everywhere you go, Like it might be gumballs,

425
00:23:44,000 --> 00:23:46,399
Speaker 1: that might be jelly beans, but it's Yeah. This is

426
00:23:46,400 --> 00:23:48,159
Speaker 1: a wonderful example because one of the problems with be

427
00:23:48,200 --> 00:23:51,280
Speaker 1: how many buildings in Atlantic question is that just off

428
00:23:51,280 --> 00:23:53,680
Speaker 1: the top of my head, I mean, I know Atlanta,

429
00:23:53,800 --> 00:23:55,159
Speaker 1: I know how to get where I need to go,

430
00:23:55,560 --> 00:23:58,840
Speaker 1: but I don't have like the firmest vision in my

431
00:23:58,920 --> 00:24:02,000
Speaker 1: head of its limits and its size and it's true

432
00:24:02,000 --> 00:24:06,199
Speaker 1: shape and and scope and Unlikewise, I don't have a

433
00:24:06,240 --> 00:24:08,960
Speaker 1: great idea of like just off the top of my head,

434
00:24:09,040 --> 00:24:12,320
Speaker 1: like how many buildings tend to occupy, say a given

435
00:24:12,440 --> 00:24:16,040
Speaker 1: square of you know, of of urban real estate. Now,

436
00:24:16,080 --> 00:24:19,760
Speaker 1: I think you could still do better than chance guessing

437
00:24:19,760 --> 00:24:22,359
Speaker 1: at this, even not knowing those things, if we just

438
00:24:22,760 --> 00:24:29,000
Speaker 1: uh leverage the power of making wild donkey guesses, uh,

439
00:24:29,040 --> 00:24:31,880
Speaker 1: and then and then bring it together with some math

440
00:24:31,960 --> 00:24:33,600
Speaker 1: in in terms of the thing we're going to talk

441
00:24:33,600 --> 00:24:35,360
Speaker 1: about in the mid in a bit, which is firm

442
00:24:35,359 --> 00:24:39,680
Speaker 1: me estimation. But back the gumball, the gumball jars, that's doable.

443
00:24:40,000 --> 00:24:42,119
Speaker 1: You might look at a gumball jar and what you

444
00:24:42,200 --> 00:24:45,200
Speaker 1: probably do is try to gut feel it. Right, Yeah,

445
00:24:45,440 --> 00:24:48,320
Speaker 1: because with the gumball um the container, I can see

446
00:24:48,320 --> 00:24:51,159
Speaker 1: how big the overall container is. I can make a

447
00:24:51,320 --> 00:24:55,439
Speaker 1: rough visual guess about how many gumballs occupy a given

448
00:24:55,480 --> 00:24:58,200
Speaker 1: area and then just sort of roughly multiply that area

449
00:24:58,240 --> 00:25:00,960
Speaker 1: in my mind until it fills up the space of container. Yeah. Yeah,

450
00:25:01,040 --> 00:25:04,439
Speaker 1: So yeah, you're you're trying to eyeball it. Uh, but

451
00:25:04,520 --> 00:25:06,960
Speaker 1: I contend you can do better. So Okay, So you

452
00:25:07,040 --> 00:25:09,639
Speaker 1: might Robert picture yourself at the County Fair is that

453
00:25:09,720 --> 00:25:12,800
Speaker 1: usually where the gumballs would be. Oh, I tend to

454
00:25:13,320 --> 00:25:17,240
Speaker 1: encounter them in like school fair scenarios. Okay, school fair,

455
00:25:17,280 --> 00:25:19,440
Speaker 1: you know, that's exactly I was talking to Rachel about.

456
00:25:19,480 --> 00:25:21,200
Speaker 1: She used to when she was a kid. She always

457
00:25:21,200 --> 00:25:23,199
Speaker 1: wanted to be able to guess the number of gumballs

458
00:25:23,240 --> 00:25:27,080
Speaker 1: that were at their school spring fling, I think, and

459
00:25:27,160 --> 00:25:28,680
Speaker 1: she never got it right. They had to do them

460
00:25:28,720 --> 00:25:31,400
Speaker 1: at you know, bars and restaurants. More like a container

461
00:25:31,440 --> 00:25:34,879
Speaker 1: of pickled eggs. That's perfect. You know how many pickled eggs.

462
00:25:35,160 --> 00:25:37,760
Speaker 1: Guess the number of pickled eggs. Get a free pickled egg. Okay, so,

463
00:25:37,800 --> 00:25:40,520
Speaker 1: but you're at a school fair then, Robert and uh,

464
00:25:40,600 --> 00:25:42,679
Speaker 1: it's guess how many gumballs are in the jar. The

465
00:25:42,720 --> 00:25:46,439
Speaker 1: closest guests gets a prize. What's the prize? It is

466
00:25:46,600 --> 00:25:52,879
Speaker 1: a deep fried, unopened can of corned beef. Hash uh

467
00:25:53,119 --> 00:25:56,920
Speaker 1: laughing at my own jokes. That's bad. Uh. So now

468
00:25:57,560 --> 00:25:59,480
Speaker 1: this game is easy to play, right because you can

469
00:25:59,480 --> 00:26:02,680
Speaker 1: eyeball it. You look at the jar somewhere deep behind

470
00:26:02,680 --> 00:26:05,399
Speaker 1: the curtain in your brain, a damon rises out of

471
00:26:05,440 --> 00:26:09,119
Speaker 1: the darkness and just plants this random, wild ass number

472
00:26:09,119 --> 00:26:12,760
Speaker 1: in your mind. It's like two hundred and thirty, and

473
00:26:12,840 --> 00:26:14,760
Speaker 1: you look at the jar again and you think that

474
00:26:14,800 --> 00:26:17,360
Speaker 1: sounds about right, you write it down. You hope you win,

475
00:26:17,680 --> 00:26:20,719
Speaker 1: but you don't win because who won. The person who

476
00:26:20,760 --> 00:26:24,240
Speaker 1: won was somebody who did some rough math. Because if

477
00:26:24,240 --> 00:26:26,480
Speaker 1: you stop to think about it, you do have some

478
00:26:26,560 --> 00:26:29,440
Speaker 1: ways of knowing about how many gumballs are in the jar.

479
00:26:29,560 --> 00:26:32,800
Speaker 1: If you've got some basic like high school geometry and

480
00:26:32,840 --> 00:26:35,440
Speaker 1: a pair of eyes, you can start getting a solid

481
00:26:35,560 --> 00:26:38,560
Speaker 1: rough estimate to work with. So, Robert, I put a

482
00:26:38,600 --> 00:26:43,199
Speaker 1: picture of some gumballs in our notes here, and I

483
00:26:43,240 --> 00:26:46,760
Speaker 1: already did some calculations on this. But um, so this

484
00:26:46,800 --> 00:26:49,560
Speaker 1: is a jar of gumballs, right? You attest that it?

485
00:26:49,600 --> 00:26:52,399
Speaker 1: Truly it does look like a jar of gunballs. No,

486
00:26:52,560 --> 00:26:55,560
Speaker 1: this is a two dimensional image. I have no idea

487
00:26:55,640 --> 00:26:58,800
Speaker 1: how how long this could be. This could be I'd

488
00:26:58,800 --> 00:27:00,680
Speaker 1: assume that it shaped like a are, but it could

489
00:27:00,720 --> 00:27:03,639
Speaker 1: be shaped like something else. Yeah, well we'll just assume

490
00:27:03,640 --> 00:27:07,520
Speaker 1: it's basically circular. So for simplicity's sake. One thing that's

491
00:27:07,560 --> 00:27:10,639
Speaker 1: a really good method when trying to come up with

492
00:27:10,680 --> 00:27:15,840
Speaker 1: these rough math guesses is skip standard units of measure.

493
00:27:16,000 --> 00:27:19,639
Speaker 1: Don't measure things in terms of inches, centimeters, pounds, whatever,

494
00:27:20,119 --> 00:27:22,919
Speaker 1: measure in terms of something that you're directly looking at.

495
00:27:23,040 --> 00:27:26,639
Speaker 1: So instead of measuring the size of the jar in

496
00:27:26,800 --> 00:27:30,720
Speaker 1: inches or centimeters, we're gonna calculate it in units of gumballs. Okay, like,

497
00:27:30,760 --> 00:27:35,000
Speaker 1: don't try and measure in calories. Continue. So, look, you

498
00:27:35,000 --> 00:27:36,919
Speaker 1: look at a jar and you think how many gumballs

499
00:27:36,920 --> 00:27:39,480
Speaker 1: are wide? Does it look like this jar is in

500
00:27:39,560 --> 00:27:43,760
Speaker 1: diameter ten? Maybe? I guess nine if you want to

501
00:27:43,760 --> 00:27:46,520
Speaker 1: go with nine, nine sounds good, okay. And then how

502
00:27:46,520 --> 00:27:50,280
Speaker 1: many gumballs high? Do you think that the jar looks? Oh,

503
00:27:50,359 --> 00:27:52,960
Speaker 1: I'd say more than that, like twelve or thirteen. I

504
00:27:53,000 --> 00:27:57,960
Speaker 1: guess ten. Okay, we'll go with ten. Okay, let's go

505
00:27:58,000 --> 00:28:01,720
Speaker 1: with him. Yeah, okay, Now now I feel like I've

506
00:28:01,720 --> 00:28:04,000
Speaker 1: stomped all over your guests. No, no, no, no, I

507
00:28:04,000 --> 00:28:05,920
Speaker 1: think it's I think that's good because if I sort

508
00:28:05,920 --> 00:28:08,320
Speaker 1: of turn it sideways, it's it's still it's a very

509
00:28:08,359 --> 00:28:13,720
Speaker 1: square looking jar, all right. So it's about nine in diameter,

510
00:28:13,800 --> 00:28:17,200
Speaker 1: about ten tall. Now, a jar is roughly a cylinder, right,

511
00:28:17,640 --> 00:28:20,879
Speaker 1: You remember from geometry, what's the formula for the volume

512
00:28:20,880 --> 00:28:23,679
Speaker 1: of a cylinder. It's not that complicated volume of a

513
00:28:23,720 --> 00:28:28,120
Speaker 1: cylinder is the area of the circle times the height.

514
00:28:28,200 --> 00:28:31,640
Speaker 1: The area of the circle is pie times the radius squared.

515
00:28:33,080 --> 00:28:34,720
Speaker 1: So you start with the base of the jar the

516
00:28:34,720 --> 00:28:39,160
Speaker 1: circle pie, which is three point fourteen times are. The

517
00:28:39,200 --> 00:28:42,200
Speaker 1: diameter was nine, right, if it's nine across are, the

518
00:28:42,320 --> 00:28:44,840
Speaker 1: radius is four point five because it's half of that.

519
00:28:45,560 --> 00:28:48,280
Speaker 1: The first you square the radius four point five squares

520
00:28:48,280 --> 00:28:50,320
Speaker 1: is a little over twenty. We just go with twenty,

521
00:28:50,880 --> 00:28:54,400
Speaker 1: and then you multiply that times three point fourteen, which

522
00:28:54,680 --> 00:28:58,760
Speaker 1: is sixty two point eight. I'm glad we could agree

523
00:28:58,760 --> 00:29:02,320
Speaker 1: on the the fat the figures here, because otherwise we

524
00:29:02,320 --> 00:29:06,320
Speaker 1: would have had to recalculated everything in our notes. You

525
00:29:06,360 --> 00:29:10,520
Speaker 1: have you have seen through my insistence whatever. Okay, so

526
00:29:10,560 --> 00:29:12,920
Speaker 1: you got sixty two point eight times the height of

527
00:29:12,920 --> 00:29:17,000
Speaker 1: how many gumballs high? Ten? Ten? Alright, So that says

528
00:29:17,000 --> 00:29:20,040
Speaker 1: they're about six hundred and twenty eight gumballs in the jar. Now,

529
00:29:20,120 --> 00:29:22,720
Speaker 1: that's probably not going to be right on the money,

530
00:29:22,760 --> 00:29:24,480
Speaker 1: but I'd say it's also probably going to be a

531
00:29:24,520 --> 00:29:27,600
Speaker 1: lot closer than the real number to the real number

532
00:29:27,880 --> 00:29:30,200
Speaker 1: than if you just eyeballed it. Right, if I had

533
00:29:30,240 --> 00:29:32,880
Speaker 1: eyeballed the jar, I might have said, I don't know

534
00:29:32,960 --> 00:29:35,479
Speaker 1: three hundred and fifty, But now looking back at it,

535
00:29:35,560 --> 00:29:37,640
Speaker 1: I'm like, oh, you know that probably is more than

536
00:29:37,680 --> 00:29:41,959
Speaker 1: three hundred and fifty. Yeah. Yeah. I feel like when

537
00:29:42,000 --> 00:29:43,760
Speaker 1: I was first looking at it, I would have probably

538
00:29:43,800 --> 00:29:47,280
Speaker 1: gone on ten by ten hundred and then try to

539
00:29:47,320 --> 00:29:49,239
Speaker 1: like I think, like, all right, maybe three or four

540
00:29:49,280 --> 00:29:51,920
Speaker 1: deep and I would have gone three hundred four hundred. Yeah,

541
00:29:51,960 --> 00:29:55,320
Speaker 1: but but I think our estimate now is actually probably better. Uh.

542
00:29:55,320 --> 00:29:57,120
Speaker 1: And that's one of the last things you should do

543
00:29:57,160 --> 00:29:59,880
Speaker 1: whenever you do this kind of mathematical calculation is you'll

544
00:30:00,120 --> 00:30:03,040
Speaker 1: get the jar again and you think, is my estimate

545
00:30:03,920 --> 00:30:06,640
Speaker 1: within the realm of possibility? Is it stupid? If I

546
00:30:06,720 --> 00:30:09,720
Speaker 1: came up with thirteen point eight billion gumballs in the jar?

547
00:30:10,160 --> 00:30:12,760
Speaker 1: This is an indication that the math or the counting

548
00:30:12,800 --> 00:30:15,120
Speaker 1: went wrong somewhere along the line. You should back up

549
00:30:15,160 --> 00:30:19,440
Speaker 1: and try again, or the jar is is seriously spooky

550
00:30:19,560 --> 00:30:21,920
Speaker 1: and you try not have anything to do with it. Yeah.

551
00:30:21,960 --> 00:30:24,960
Speaker 1: Another way of checking against reality is to test the

552
00:30:25,000 --> 00:30:28,440
Speaker 1: method in the real world. So would such a method

553
00:30:28,560 --> 00:30:32,320
Speaker 1: actually win you a gumball jar guessing contest? Well, I

554
00:30:32,320 --> 00:30:35,000
Speaker 1: thought I'd do some googling, and I did, and sure, enough.

555
00:30:35,160 --> 00:30:37,320
Speaker 1: I found a blog post about a guy who won

556
00:30:37,440 --> 00:30:41,040
Speaker 1: a gumball jar guessing contest. Somebody asked him what method

557
00:30:41,040 --> 00:30:43,640
Speaker 1: he used, and he said he calculated the volume of

558
00:30:43,640 --> 00:30:47,080
Speaker 1: the cylinder in the jar, and then he randomly added

559
00:30:47,160 --> 00:30:50,239
Speaker 1: twenty five to that number. So it's sort of like

560
00:30:51,040 --> 00:30:55,360
Speaker 1: being the the the the the area of error in

561
00:30:55,440 --> 00:30:58,040
Speaker 1: his calculations. Huh, yeah, I guess it could be. So

562
00:30:58,080 --> 00:31:02,000
Speaker 1: he came up with like seventeen uh, one thousand seven

563
00:31:02,760 --> 00:31:05,680
Speaker 1: five gunballs, and actually it was one thousand, seven hundred

564
00:31:05,720 --> 00:31:10,960
Speaker 1: and fifty. So yeah, so so, so you've got these principles,

565
00:31:11,040 --> 00:31:13,840
Speaker 1: right you. You don't have to just surrender to your

566
00:31:13,880 --> 00:31:16,800
Speaker 1: gut instinct when it's time to guess something. You can

567
00:31:16,840 --> 00:31:19,840
Speaker 1: couple very simple rough math. You know, this is not

568
00:31:20,160 --> 00:31:24,479
Speaker 1: complex calculus or anything like that, with observations that you

569
00:31:24,480 --> 00:31:26,640
Speaker 1: can just get by looking at what's in front of

570
00:31:26,680 --> 00:31:30,360
Speaker 1: you or by drawing on really basic knowledge or even

571
00:31:30,400 --> 00:31:33,680
Speaker 1: just guesses. All you need to do is think about

572
00:31:33,720 --> 00:31:37,280
Speaker 1: the logical relationships between numbers and know how to look

573
00:31:37,320 --> 00:31:40,000
Speaker 1: for those relevant pieces information that might be in your

574
00:31:40,000 --> 00:31:43,160
Speaker 1: memory or might be right in front of your eyes. Now,

575
00:31:43,160 --> 00:31:46,080
Speaker 1: I think it's time to get back to Enrico Fermi, so,

576
00:31:46,320 --> 00:31:48,880
Speaker 1: as we mentioned earlier, for me, was apparently known for

577
00:31:49,000 --> 00:31:52,720
Speaker 1: being a really good guesser when it came to numbers.

578
00:31:52,920 --> 00:31:56,280
Speaker 1: And there is a classic example that's often used as

579
00:31:56,320 --> 00:32:01,280
Speaker 1: an example of how his method of estimation works. Um,

580
00:32:01,320 --> 00:32:04,560
Speaker 1: it would be how many piano tuners are there in

581
00:32:04,600 --> 00:32:08,960
Speaker 1: the city of Chicago. Now, I have found lots of

582
00:32:09,000 --> 00:32:11,760
Speaker 1: different versions of this all over the internet, you know,

583
00:32:11,840 --> 00:32:15,480
Speaker 1: people working it out in different ways. But the goal

584
00:32:15,680 --> 00:32:19,560
Speaker 1: of Fermi estimation is not to hit the number exactly,

585
00:32:20,320 --> 00:32:24,120
Speaker 1: but it is to get into the right ballpark, get

586
00:32:24,120 --> 00:32:27,280
Speaker 1: in striking distance of it, if you will. Yeah. And

587
00:32:27,360 --> 00:32:30,600
Speaker 1: so one version of how many piano tuners are in

588
00:32:30,760 --> 00:32:35,239
Speaker 1: Chicago appears on NASA's Glenn Research Center page. And and

589
00:32:35,280 --> 00:32:38,640
Speaker 1: this is their version. Uh. So they start with how

590
00:32:38,880 --> 00:32:41,040
Speaker 1: would you even begin to calculate that? Well, one number

591
00:32:41,120 --> 00:32:44,600
Speaker 1: you can work with is the population of Chicago. Yeah, okay,

592
00:32:44,680 --> 00:32:47,080
Speaker 1: so that will give you something to start with. So

593
00:32:47,120 --> 00:32:49,160
Speaker 1: they go to the almanac. They say, at this time,

594
00:32:49,200 --> 00:32:51,880
Speaker 1: the Chicago as a population of about three million people.

595
00:32:52,760 --> 00:32:56,760
Speaker 1: Now assume that the average family has four members, so

596
00:32:56,920 --> 00:32:59,840
Speaker 1: like four members per household, So the number of households

597
00:32:59,840 --> 00:33:03,240
Speaker 1: in Chicago is going to be three million divided by four,

598
00:33:03,320 --> 00:33:08,480
Speaker 1: so that's about seven fifty thousand seven households. How many

599
00:33:08,520 --> 00:33:13,280
Speaker 1: households own a piano? They guess one in five. I

600
00:33:13,280 --> 00:33:16,680
Speaker 1: think that's probably kind of high, but I don't know, Yeah,

601
00:33:16,680 --> 00:33:20,360
Speaker 1: I have, I just have no way of of Well.

602
00:33:20,400 --> 00:33:23,440
Speaker 1: One thing you can do in these scenarios that that

603
00:33:23,520 --> 00:33:25,320
Speaker 1: I'll get to in a little more depth than just

604
00:33:25,360 --> 00:33:28,240
Speaker 1: a minute is if you don't know how to guess

605
00:33:28,280 --> 00:33:32,520
Speaker 1: something like what percent of families have a piano in

606
00:33:32,560 --> 00:33:36,840
Speaker 1: their household, you come up with boundaries. So you say, Okay,

607
00:33:36,880 --> 00:33:40,640
Speaker 1: what's the lowest number that would make any sense, what's

608
00:33:40,680 --> 00:33:43,200
Speaker 1: the highest number that would make any sense, and then

609
00:33:43,280 --> 00:33:46,680
Speaker 1: you take what's known as a geometric mean between them,

610
00:33:46,920 --> 00:33:49,160
Speaker 1: which means you multiply them together, and then you take

611
00:33:49,200 --> 00:33:52,800
Speaker 1: the square root of that number. Okay, so the process

612
00:33:52,840 --> 00:33:55,520
Speaker 1: here could be one in ten people have a piano.

613
00:33:55,600 --> 00:33:57,840
Speaker 1: That sounds like that would make pianos a bit too rare.

614
00:33:58,160 --> 00:34:00,600
Speaker 1: One in three. I don't know if they're that common.

615
00:34:00,880 --> 00:34:03,560
Speaker 1: Let's split the difference more or less and go with

616
00:34:03,640 --> 00:34:06,400
Speaker 1: one in five. Yeah, that that's actually really close. So

617
00:34:06,440 --> 00:34:09,920
Speaker 1: if you if you multiply together, um, one in three,

618
00:34:09,960 --> 00:34:13,040
Speaker 1: which would be about point three. Uh, and then one

619
00:34:13,080 --> 00:34:15,480
Speaker 1: in ten, which would be point one. And then you

620
00:34:15,520 --> 00:34:17,600
Speaker 1: take that number and get the square root of it.

621
00:34:17,880 --> 00:34:20,600
Speaker 1: Your answer is like point seventeen, which is close to

622
00:34:20,960 --> 00:34:24,239
Speaker 1: point two, which is one in five. So there we go.

623
00:34:24,320 --> 00:34:27,359
Speaker 1: We're on track. So if one in five families has

624
00:34:27,400 --> 00:34:30,920
Speaker 1: a piano and there are seven hundred and fifty thousand

625
00:34:30,960 --> 00:34:34,440
Speaker 1: families in Chicago, that means there's gonna be one hundred

626
00:34:34,440 --> 00:34:37,360
Speaker 1: and fifty thousand pianos in Chicago. There's a number to

627
00:34:37,400 --> 00:34:40,320
Speaker 1: work with. All right, you got a hundred fifty Now

628
00:34:40,680 --> 00:34:43,719
Speaker 1: that is a number of pianos that are available to

629
00:34:43,760 --> 00:34:46,520
Speaker 1: be tuned. So this can give us a foothold to

630
00:34:46,560 --> 00:34:49,560
Speaker 1: try to figure out how many tuners there are. If

631
00:34:49,560 --> 00:34:52,719
Speaker 1: you've got an average piano tuner, I mean, how many

632
00:34:52,760 --> 00:34:56,799
Speaker 1: pianos do you think they could tune in a day

633
00:34:57,080 --> 00:34:59,080
Speaker 1: in a work day? Okay, this is going with the

634
00:34:59,080 --> 00:35:02,839
Speaker 1: assumption that like they're design like the piano tuner makes

635
00:35:02,920 --> 00:35:07,120
Speaker 1: this his or her um life. Like, they're not just

636
00:35:07,160 --> 00:35:09,560
Speaker 1: doing a little piano tuning on the side, right, this

637
00:35:09,680 --> 00:35:13,120
Speaker 1: is their full time job. Oh I don't know. Um

638
00:35:13,760 --> 00:35:16,319
Speaker 1: lets you have to travel there, you have it I

639
00:35:16,360 --> 00:35:21,200
Speaker 1: mean comfortably, what maybe three or four a day? Well,

640
00:35:21,640 --> 00:35:23,759
Speaker 1: in this estimation they come up with four. I think

641
00:35:23,800 --> 00:35:25,759
Speaker 1: four is a reasonable guests. Yeah, Like I think of

642
00:35:25,760 --> 00:35:28,600
Speaker 1: other jobs, like you know, forst is, my wife's a photographer.

643
00:35:28,719 --> 00:35:31,200
Speaker 1: She's not tuning pianos, but she has to travel somewhere,

644
00:35:31,320 --> 00:35:33,480
Speaker 1: do a session and then come back. And I think, like,

645
00:35:33,520 --> 00:35:36,840
Speaker 1: if she was just just crazy busy, how many should

646
00:35:36,880 --> 00:35:39,360
Speaker 1: could you fit in a day? You know? Like that

647
00:35:39,440 --> 00:35:42,520
Speaker 1: seems about right. Yeah. Another option, if we didn't believe

648
00:35:42,640 --> 00:35:46,200
Speaker 1: that four days, we could do the geometric mean again,

649
00:35:46,600 --> 00:35:49,480
Speaker 1: we could say, well, it's got to be more than one,

650
00:35:50,320 --> 00:35:54,560
Speaker 1: and it can't be more than what like six, I

651
00:35:54,560 --> 00:35:56,400
Speaker 1: mean that that'd just be care you can't be certainly

652
00:35:56,400 --> 00:35:59,560
Speaker 1: can't be more than eight. Um. So then you'd get

653
00:35:59,560 --> 00:36:01,279
Speaker 1: a GMO tricked me and that that probably put it

654
00:36:01,320 --> 00:36:03,319
Speaker 1: a little bit lower than four, but you'd still have

655
00:36:03,719 --> 00:36:07,320
Speaker 1: some number in that, you know, three something like that, Okay,

656
00:36:07,400 --> 00:36:09,160
Speaker 1: And then of course you assume they don't work on

657
00:36:09,200 --> 00:36:11,640
Speaker 1: the weekends and they've got a two week vacation during

658
00:36:11,640 --> 00:36:15,040
Speaker 1: the summer. So that's fifty weeks in a year of

659
00:36:15,239 --> 00:36:18,920
Speaker 1: tuning four pianos a day, five days a week. So

660
00:36:19,000 --> 00:36:22,680
Speaker 1: that means in one year, the average worker, the average

661
00:36:22,680 --> 00:36:26,880
Speaker 1: piano tuner, would service one thousand pianos. Now, if we

662
00:36:27,000 --> 00:36:30,479
Speaker 1: said that there are a hundred and fifty thousand pianos

663
00:36:30,520 --> 00:36:33,360
Speaker 1: in the city of Chicago, that means there should be

664
00:36:33,360 --> 00:36:35,920
Speaker 1: about a hundred and fifty piano tuners in the city.

665
00:36:36,160 --> 00:36:38,839
Speaker 1: I don't know, does that number sound reasonable. It's at

666
00:36:38,920 --> 00:36:41,920
Speaker 1: least got you in the ballpark. I guess it sounds reasonable.

667
00:36:42,080 --> 00:36:44,880
Speaker 1: I it's I mean, I guess this is a difficult

668
00:36:44,880 --> 00:36:47,520
Speaker 1: thing to check because is there like a Piano Tuners

669
00:36:47,560 --> 00:36:49,719
Speaker 1: Association of America that you can check with on this

670
00:36:49,800 --> 00:36:52,279
Speaker 1: sort of thing. Well, I've seen other estimates that work

671
00:36:52,280 --> 00:36:56,040
Speaker 1: out the number differently, so they they you know, they

672
00:36:56,120 --> 00:36:58,520
Speaker 1: might say, well, I think that your estimate on step

673
00:36:58,560 --> 00:37:01,160
Speaker 1: four here is not smart. I would change it to this,

674
00:37:01,800 --> 00:37:04,959
Speaker 1: and that actually gives me, uh, you know, something more

675
00:37:05,080 --> 00:37:08,480
Speaker 1: like forty piano tuners in the city of Chicago. And

676
00:37:08,640 --> 00:37:10,400
Speaker 1: one thing you can check is you can look at

677
00:37:10,440 --> 00:37:14,000
Speaker 1: see how many are in the phone book. Then again,

678
00:37:14,160 --> 00:37:16,279
Speaker 1: I mean, in this day and age, there's probably a

679
00:37:16,320 --> 00:37:18,960
Speaker 1: lot of things that aren't in the phone book, right, Yeah,

680
00:37:19,080 --> 00:37:20,879
Speaker 1: you kind of end up like the the the yelp

681
00:37:21,040 --> 00:37:25,040
Speaker 1: versus phone book uh tug awar, depending on where you're going,

682
00:37:25,120 --> 00:37:27,799
Speaker 1: is it a yelptown or are they still yellow pages down?

683
00:37:28,280 --> 00:37:30,120
Speaker 1: And and then you're you're you know, you're you're also

684
00:37:30,360 --> 00:37:33,640
Speaker 1: forgetting about all the black market piano tuners out there.

685
00:37:34,239 --> 00:37:38,200
Speaker 1: Uh yeah, but those black market piano tuners get less

686
00:37:38,239 --> 00:37:42,200
Speaker 1: piano tuning done because they're also moonlighting as uh piano

687
00:37:42,239 --> 00:37:46,879
Speaker 1: wire assassins. That's that's true. Now, when you're estimating big

688
00:37:47,000 --> 00:37:49,600
Speaker 1: numbers based on little data, one of the things that's

689
00:37:49,640 --> 00:37:53,040
Speaker 1: really helpful, this helpful concept is the idea of orders

690
00:37:53,040 --> 00:37:55,319
Speaker 1: of magnitude. We've talked about this a little so far,

691
00:37:55,360 --> 00:37:57,839
Speaker 1: but just to be clear about what this is. Um

692
00:37:57,920 --> 00:38:01,600
Speaker 1: when you read about really big a very little numbers

693
00:38:01,640 --> 00:38:04,120
Speaker 1: in science, you'll often see those numbers expressed not in

694
00:38:04,160 --> 00:38:06,879
Speaker 1: full notation, written out. But you've seen this before where

695
00:38:07,000 --> 00:38:11,000
Speaker 1: it is scientific notation. It's a like four point eight

696
00:38:11,040 --> 00:38:14,920
Speaker 1: times tend to the nineteen or something like that. That

697
00:38:14,920 --> 00:38:18,800
Speaker 1: would be a really big number. And so U instead

698
00:38:18,840 --> 00:38:22,200
Speaker 1: of writing a thousand, you write like ten to the three,

699
00:38:22,840 --> 00:38:25,560
Speaker 1: or instead of writing point zero zero one, it's ten

700
00:38:25,640 --> 00:38:28,760
Speaker 1: to the negative three, and you get more precise instead

701
00:38:28,800 --> 00:38:31,680
Speaker 1: of two thousand, five hundred, you write two point five

702
00:38:31,760 --> 00:38:34,759
Speaker 1: times ten to the three or instead of point zero

703
00:38:34,840 --> 00:38:37,760
Speaker 1: zero zero zero eight seven, it's eight point seven times

704
00:38:37,760 --> 00:38:40,600
Speaker 1: tend to the negative five. Right, So you've you've got

705
00:38:40,760 --> 00:38:44,359
Speaker 1: orders of magnitude, and they are the exponent in that

706
00:38:44,440 --> 00:38:47,520
Speaker 1: type of notation. Every time the exponent goes up or

707
00:38:47,600 --> 00:38:51,040
Speaker 1: down a number, that's an order of magnitude. Another simpler

708
00:38:51,040 --> 00:38:52,920
Speaker 1: way of thinking about this is that the order of

709
00:38:52,960 --> 00:38:56,040
Speaker 1: magnitude is just the number of digits in a number.

710
00:38:56,400 --> 00:39:00,800
Speaker 1: Get single digit number, double digit, triple digit, quadruple digit, um.

711
00:39:00,840 --> 00:39:03,400
Speaker 1: When somebody is talking about the number of figures in

712
00:39:03,440 --> 00:39:07,239
Speaker 1: a salary, they're concerned about orders of magnitude. You know.

713
00:39:07,280 --> 00:39:09,480
Speaker 1: One thing this reminds me of is, of course, the

714
00:39:09,200 --> 00:39:14,040
Speaker 1: the classic educational film created by the Aims uh the

715
00:39:14,080 --> 00:39:17,000
Speaker 1: Powers of Tin which granted that so there's a visual,

716
00:39:17,280 --> 00:39:19,560
Speaker 1: very strong visual element to that as well, but it

717
00:39:19,600 --> 00:39:24,160
Speaker 1: basically seeks out to explain and make digestible the scale

718
00:39:24,200 --> 00:39:27,520
Speaker 1: of the universe. This is that classic zooming in and out.

719
00:39:28,120 --> 00:39:30,680
Speaker 1: That thing is great, it is, it's still it's wonderful,

720
00:39:30,680 --> 00:39:33,359
Speaker 1: still holds up really well today and uh and it's

721
00:39:33,400 --> 00:39:35,800
Speaker 1: just you know, phenomenal to watch. But yeah, by considering

722
00:39:35,840 --> 00:39:38,279
Speaker 1: the order of magnitude, like, it's able to make some

723
00:39:38,400 --> 00:39:40,759
Speaker 1: of these that the scale is able to make the

724
00:39:40,800 --> 00:39:43,520
Speaker 1: scale of the universe more digestible. Yeah. Now, if you

725
00:39:43,600 --> 00:39:45,480
Speaker 1: haven't seen that, go out and google it right now.

726
00:39:45,520 --> 00:39:47,400
Speaker 1: You can put us on pause. It's it's worth that

727
00:39:47,440 --> 00:39:49,879
Speaker 1: you should really watch. I think it's on YouTube, isn't it. Yes,

728
00:39:49,920 --> 00:39:53,760
Speaker 1: I believe that there's an official YouTube version of it.

729
00:39:53,760 --> 00:39:57,279
Speaker 1: It's just it's fantastic. Um. But yeah, So back to

730
00:39:57,440 --> 00:39:59,840
Speaker 1: why why to orders of magnitude matter? Well, for me,

731
00:40:00,080 --> 00:40:06,640
Speaker 1: estimation that this uh process that was really made immortal

732
00:40:06,840 --> 00:40:10,200
Speaker 1: by Enrico Fermi, is a way of easily guessing numbers

733
00:40:10,200 --> 00:40:13,960
Speaker 1: by rounding up or down by orders of magnitude and

734
00:40:14,000 --> 00:40:17,360
Speaker 1: then calculating based on these easy to work with round numbers.

735
00:40:17,360 --> 00:40:19,480
Speaker 1: So we started doing that in our last example right

736
00:40:19,480 --> 00:40:24,680
Speaker 1: when we were taking geometrical means. Um. But the basic

737
00:40:24,760 --> 00:40:29,080
Speaker 1: way that a Fermi estimation problem works is you start

738
00:40:29,080 --> 00:40:31,799
Speaker 1: by figuring out what are the key assumptions, what are

739
00:40:31,840 --> 00:40:34,239
Speaker 1: the factors you would need to know in order in

740
00:40:34,360 --> 00:40:37,080
Speaker 1: order to calculate your answer. So in the piano tune

741
00:40:37,120 --> 00:40:38,960
Speaker 1: or example, you'd be like, well, Okay, if we know

742
00:40:39,040 --> 00:40:43,040
Speaker 1: the city of Chicago has a certain population, and we

743
00:40:43,160 --> 00:40:46,080
Speaker 1: know that piano tuners can tune a certain amount of

744
00:40:46,080 --> 00:40:50,439
Speaker 1: pianos each week, we can derive from those numbers what

745
00:40:50,520 --> 00:40:53,520
Speaker 1: we need to calculate our answer. So the next step

746
00:40:53,560 --> 00:40:56,440
Speaker 1: would be like thinking about what order of magnitude your

747
00:40:56,560 --> 00:40:59,319
Speaker 1: your key pieces of information are on. So like when

748
00:40:59,360 --> 00:41:02,000
Speaker 1: you're making a guess, this is where the boundaries come in.

749
00:41:02,400 --> 00:41:04,840
Speaker 1: If you have no idea for a number, if somebody

750
00:41:04,880 --> 00:41:09,359
Speaker 1: asks you, um, how many lucky charms marshmallows have ever

751
00:41:09,400 --> 00:41:13,000
Speaker 1: been manufactured on planet Earth? You have no idea, right,

752
00:41:13,160 --> 00:41:15,680
Speaker 1: I mean I wouldn't even know where to start, absolutely

753
00:41:15,719 --> 00:41:17,719
Speaker 1: no idea. But actually you you do know where to

754
00:41:17,760 --> 00:41:21,239
Speaker 1: start because you can play with boundaries again. Okay, so

755
00:41:21,320 --> 00:41:23,839
Speaker 1: what's a low number that you you know it's got

756
00:41:23,840 --> 00:41:27,040
Speaker 1: to be more than ten thousand? I mean that's ridiculous,

757
00:41:27,080 --> 00:41:30,839
Speaker 1: more than ten for sure. Yeah, but you keep keep

758
00:41:30,880 --> 00:41:35,560
Speaker 1: bringing your lower bound up so you know it's more

759
00:41:35,640 --> 00:41:39,120
Speaker 1: than a hundred thousand, right you know? Well yeah, because

760
00:41:39,480 --> 00:41:41,080
Speaker 1: you know, in fact, you probably know it's more than

761
00:41:41,120 --> 00:41:42,840
Speaker 1: a million, because what do you think at least a

762
00:41:42,840 --> 00:41:45,160
Speaker 1: million people of eating a bowl of lucky Charms at

763
00:41:45,200 --> 00:41:48,080
Speaker 1: some point in history. Yeah, it's been around for at

764
00:41:48,160 --> 00:41:50,920
Speaker 1: least decades. Yeah, and so if at least a million

765
00:41:50,960 --> 00:41:54,000
Speaker 1: people of eating a bowl of lucky Charms and each

766
00:41:54,080 --> 00:41:56,640
Speaker 1: bowl had more than one marshmallow in it, you know

767
00:41:56,760 --> 00:42:00,040
Speaker 1: there's at least more than a million. Um. I that

768
00:42:00,200 --> 00:42:02,640
Speaker 1: we could even go safely to ten million, but I

769
00:42:02,640 --> 00:42:05,440
Speaker 1: don't know. I'll stick to a million. That's our lower bound.

770
00:42:06,080 --> 00:42:09,080
Speaker 1: And then, uh, you know what's the upper bound? I mean,

771
00:42:09,120 --> 00:42:14,759
Speaker 1: you know there cannot have been ten trillion of these marshmallows, right,

772
00:42:14,800 --> 00:42:18,839
Speaker 1: there's just too many. Way Yeah, Okay, so now you've

773
00:42:18,840 --> 00:42:23,200
Speaker 1: actually got boundaries, so you know there's less than ten trillion,

774
00:42:23,600 --> 00:42:27,720
Speaker 1: and a geometric mean between one million and ten trillion

775
00:42:27,920 --> 00:42:31,080
Speaker 1: is ten billion? Is that anywhere close to the right answer?

776
00:42:31,160 --> 00:42:33,399
Speaker 1: Well maybe not, But now you've got something to work

777
00:42:33,440 --> 00:42:36,239
Speaker 1: with that's better than you started with, which was just

778
00:42:36,320 --> 00:42:39,399
Speaker 1: I have no idea. Well, this is quite a useful tool.

779
00:42:39,440 --> 00:42:41,480
Speaker 1: We've been we've been talking about those far because I

780
00:42:41,520 --> 00:42:44,000
Speaker 1: can already see the ways that this can be easily

781
00:42:44,080 --> 00:42:47,799
Speaker 1: applied to say the person's work week. You know, how

782
00:42:47,880 --> 00:42:51,080
Speaker 1: much how much of um, you know, my given work

783
00:42:51,080 --> 00:42:53,520
Speaker 1: can I fit in could I, you know, could could

784
00:42:53,520 --> 00:42:56,680
Speaker 1: I write this many articles? Could I write this many?

785
00:42:56,719 --> 00:42:59,560
Speaker 1: What's the what's the most extravagant and the smallest number?

786
00:42:59,560 --> 00:43:02,279
Speaker 1: And then ending that middle ground. Right, So yeah, but

787
00:43:02,560 --> 00:43:05,960
Speaker 1: remember it's not just the simple mean, because what what

788
00:43:06,000 --> 00:43:09,080
Speaker 1: you're really looking for is the geometric mean, which again

789
00:43:09,280 --> 00:43:12,239
Speaker 1: is instead of so the simple mean simple average is

790
00:43:12,320 --> 00:43:15,960
Speaker 1: you add them together and divide by two. The geometric

791
00:43:16,040 --> 00:43:19,400
Speaker 1: mean is multiply them together and then take the square root.

792
00:43:19,719 --> 00:43:21,640
Speaker 1: So if you say, how many articles do you think

793
00:43:21,680 --> 00:43:23,799
Speaker 1: you could write in a week, Robert, what's the what's

794
00:43:23,840 --> 00:43:29,160
Speaker 1: the highest possible number? That's kind of crazy, the highest

795
00:43:29,160 --> 00:43:33,840
Speaker 1: possible number. We'll just without boring anybody about details and

796
00:43:33,880 --> 00:43:36,840
Speaker 1: get into a big conversation about which form of article, etcetera.

797
00:43:37,120 --> 00:43:41,960
Speaker 1: Let's just go ahead and say, um, twenty articles twenty Okay, Now,

798
00:43:42,000 --> 00:43:47,359
Speaker 1: what's a really low ball number, lazy as heck, Let's

799
00:43:47,360 --> 00:43:51,560
Speaker 1: say four or five. Let's say five just to keep

800
00:43:51,600 --> 00:43:55,400
Speaker 1: it cleaner, maybe, or four, whichever one is easier to compute. Okay,

801
00:43:55,440 --> 00:43:59,160
Speaker 1: so four times twenty. Then take the square root of

802
00:43:59,160 --> 00:44:03,360
Speaker 1: that number. It's about eight point nine or nine. So

803
00:44:03,760 --> 00:44:08,000
Speaker 1: that's a number, all right, That that's better than not

804
00:44:08,040 --> 00:44:10,120
Speaker 1: having anything to work with. One of the key things

805
00:44:10,160 --> 00:44:13,279
Speaker 1: about this type of estimation is that it's useful, but

806
00:44:13,360 --> 00:44:16,000
Speaker 1: it's only useful if you treat it critically. I mean,

807
00:44:16,000 --> 00:44:19,279
Speaker 1: obviously you can't just generate numbers using this method and

808
00:44:19,280 --> 00:44:21,920
Speaker 1: then go with them. But it does give you a

809
00:44:21,960 --> 00:44:25,720
Speaker 1: place to a foothold, essentially for thinking about numbers. Whereas

810
00:44:25,840 --> 00:44:29,960
Speaker 1: you started with paralysis, you're starting staring into a void

811
00:44:30,000 --> 00:44:32,520
Speaker 1: of all possible numbers and you have no idea where

812
00:44:32,560 --> 00:44:36,440
Speaker 1: to start FIRMI estimation helps give you a place to

813
00:44:36,520 --> 00:44:39,359
Speaker 1: start with and say is that reasonable? And you can

814
00:44:39,400 --> 00:44:43,200
Speaker 1: work up and down from there. Um. But okay, so

815
00:44:43,200 --> 00:44:45,399
Speaker 1: so you've got that. When when you want to get

816
00:44:45,440 --> 00:44:47,440
Speaker 1: a factor and you have no idea what it is,

817
00:44:47,680 --> 00:44:52,040
Speaker 1: put some boundaries in place and then take a geometric mean. Um. Now,

818
00:44:52,160 --> 00:44:54,959
Speaker 1: once you use these assumptions, you make a rough calculation

819
00:44:55,080 --> 00:44:58,319
Speaker 1: like they did with the piano tuners example, and then

820
00:44:58,360 --> 00:45:00,839
Speaker 1: you look at your answer and you do a reality check.

821
00:45:01,320 --> 00:45:04,400
Speaker 1: You say, is this reasonable? Is this number within the

822
00:45:04,400 --> 00:45:07,040
Speaker 1: realm of possibility? And do I need to go back

823
00:45:07,040 --> 00:45:10,400
Speaker 1: and adjust anything I did before. Now this might be

824
00:45:10,440 --> 00:45:12,960
Speaker 1: a terrible example, but I kind of wanted to just

825
00:45:13,080 --> 00:45:16,360
Speaker 1: have us try one on the fly. Okay, let's do it. Okay,

826
00:45:16,440 --> 00:45:18,960
Speaker 1: so you want to guess a totally unknown number. And

827
00:45:19,040 --> 00:45:23,399
Speaker 1: here's my question. How many pounds of hair do Americans

828
00:45:23,480 --> 00:45:26,120
Speaker 1: get cut off their heads in total each year? Not

829
00:45:26,280 --> 00:45:30,439
Speaker 1: individual Americans, all of America? How many pounds of hair

830
00:45:30,560 --> 00:45:35,839
Speaker 1: are cut? All? Right? Well, the obvious starting point there

831
00:45:35,880 --> 00:45:38,720
Speaker 1: would be how many Americans are we dealing with? Right? Okay,

832
00:45:38,760 --> 00:45:41,600
Speaker 1: so there you go. So how many Americans there? I

833
00:45:41,600 --> 00:45:44,400
Speaker 1: think there are what like three? Do you want to

834
00:45:44,400 --> 00:45:47,480
Speaker 1: go with the three hundred and there are more than

835
00:45:47,480 --> 00:45:50,560
Speaker 1: three hundred million, but we could round down to make

836
00:45:50,560 --> 00:45:53,719
Speaker 1: it simple. Three hundred millions sounds good. Okay, so we've

837
00:45:53,719 --> 00:45:57,359
Speaker 1: got three hundred million Americans? Uh, in a very rough estimate. Now,

838
00:45:57,440 --> 00:46:00,600
Speaker 1: how many pounds of hair on average does American have?

839
00:46:01,280 --> 00:46:04,160
Speaker 1: This is going to vary widely. Some people have dreadlocks

840
00:46:04,200 --> 00:46:07,600
Speaker 1: to their knees, some people are totally bald. But what's

841
00:46:07,600 --> 00:46:10,000
Speaker 1: a good average that would put us right in the middle,

842
00:46:10,440 --> 00:46:13,080
Speaker 1: like the pounds and like how much hair they haven't

843
00:46:13,080 --> 00:46:14,960
Speaker 1: cut off or just how much hair they have have?

844
00:46:15,239 --> 00:46:17,600
Speaker 1: Al Right? Well, alright, well, I think what do I

845
00:46:17,640 --> 00:46:20,880
Speaker 1: know the weight of the human brain is about three pounds.

846
00:46:21,120 --> 00:46:24,720
Speaker 1: I feel like hair weighs less than a brain in general,

847
00:46:24,840 --> 00:46:29,239
Speaker 1: so I would say a pound of hair. It still

848
00:46:29,280 --> 00:46:31,279
Speaker 1: kind of feels big. Yeah, I would tend to think

849
00:46:31,320 --> 00:46:34,560
Speaker 1: that people on average have less than a pound of hair.

850
00:46:34,640 --> 00:46:38,120
Speaker 1: I mean, somebody who has really long hair maybe might

851
00:46:38,200 --> 00:46:41,759
Speaker 1: have a pound of hair. I don't know. Maybe this

852
00:46:41,800 --> 00:46:44,359
Speaker 1: is the beauty of it. Just rough gas, Okay, like

853
00:46:44,400 --> 00:46:47,600
Speaker 1: a quarter of a pound. Okay, let's start with five

854
00:46:48,000 --> 00:46:50,920
Speaker 1: pounds of hair per person. Okay, Well, I did just

855
00:46:50,960 --> 00:46:53,640
Speaker 1: do the calculation of how many pounds of hair there

856
00:46:53,680 --> 00:46:56,280
Speaker 1: are in America, but we might not actually need that figure.

857
00:46:56,680 --> 00:47:00,160
Speaker 1: So three million people times a quarter pound of hair

858
00:47:00,239 --> 00:47:03,160
Speaker 1: per person is seventy five million pounds of hair. But

859
00:47:03,280 --> 00:47:04,920
Speaker 1: like I said, we might not need it. In fact,

860
00:47:05,760 --> 00:47:08,280
Speaker 1: let's just stick with the quarter pounds of hair per person.

861
00:47:09,320 --> 00:47:13,160
Speaker 1: What percentage of your hair does the average person get

862
00:47:13,160 --> 00:47:15,160
Speaker 1: cut off in a haircut? Again, this is going to

863
00:47:15,360 --> 00:47:17,759
Speaker 1: vary wildly. Some people get there, you know, long hair

864
00:47:17,760 --> 00:47:20,799
Speaker 1: shaved completely off. Some people get a tiny little trim.

865
00:47:20,880 --> 00:47:23,359
Speaker 1: But on average, what what is the mass of your

866
00:47:23,360 --> 00:47:26,759
Speaker 1: hair that is removed in a haircut? Um off? Off hand?

867
00:47:26,760 --> 00:47:30,880
Speaker 1: I'm thinking, Okay, I would guess kind of higher. I

868
00:47:30,960 --> 00:47:34,080
Speaker 1: was thinking I probably wait too long to get a haircut,

869
00:47:34,200 --> 00:47:37,640
Speaker 1: So with me, I think it's like fifty percent um.

870
00:47:37,680 --> 00:47:40,120
Speaker 1: But maybe we can get get in between them. I

871
00:47:40,120 --> 00:47:42,600
Speaker 1: don't know if everybody other people wait as long as

872
00:47:42,640 --> 00:47:44,480
Speaker 1: I do and look as scruffy as I do by

873
00:47:44,480 --> 00:47:48,040
Speaker 1: the time I go in, or or just get people

874
00:47:48,080 --> 00:47:53,600
Speaker 1: get you know, really well groomed all the time. Let's say, uh,

875
00:47:55,239 --> 00:47:57,080
Speaker 1: ok or you can go with high thirty if you want.

876
00:47:57,080 --> 00:48:00,040
Speaker 1: I feel like like thirties, not too high. Okay. It

877
00:48:01,280 --> 00:48:03,320
Speaker 1: feels like enough to where you would say, hey, you

878
00:48:03,400 --> 00:48:05,919
Speaker 1: got a haircut, didn't you, Whereas if you go too low,

879
00:48:06,200 --> 00:48:09,319
Speaker 1: you more attempted to say, hey, your hair is a

880
00:48:09,320 --> 00:48:11,839
Speaker 1: little wetter than normal or something, you know. I mean,

881
00:48:12,080 --> 00:48:15,000
Speaker 1: thet seems like it would be like a comfortable level

882
00:48:15,000 --> 00:48:17,480
Speaker 1: of notice, but not a woe did you join a

883
00:48:17,520 --> 00:48:23,160
Speaker 1: cult level of haircut? Okay, Well, that gives us a number. Actually,

884
00:48:23,239 --> 00:48:25,400
Speaker 1: So if we say that the average person has a

885
00:48:25,440 --> 00:48:28,600
Speaker 1: quarter pound of hair, and that thirty percent of their

886
00:48:28,600 --> 00:48:31,759
Speaker 1: hair is removed in the average haircut, that means that

887
00:48:31,840 --> 00:48:36,359
Speaker 1: the average haircut in America removes point zero seven five

888
00:48:36,440 --> 00:48:40,520
Speaker 1: pounds of hair Okay, Now that's going to vary widely

889
00:48:40,560 --> 00:48:42,279
Speaker 1: up and down again, but we're just trying to get

890
00:48:42,280 --> 00:48:46,080
Speaker 1: an average. Now, if we say that the average haircut

891
00:48:46,160 --> 00:48:48,640
Speaker 1: removes x amount of hair, all we need to know

892
00:48:48,920 --> 00:48:52,319
Speaker 1: now are how many haircuts there are in America every year,

893
00:48:53,480 --> 00:48:57,200
Speaker 1: So we already know how many people there are. How

894
00:48:57,280 --> 00:49:01,880
Speaker 1: often would you say that the average person gets a haircut? Oh,

895
00:49:02,080 --> 00:49:04,400
Speaker 1: this is this is a tough one, right, but I'm

896
00:49:04,480 --> 00:49:10,279
Speaker 1: guessing once every two months. Okay, so six times a year. Yeah,

897
00:49:10,320 --> 00:49:12,600
Speaker 1: that feels maybe a little. That's a little higher than

898
00:49:12,640 --> 00:49:14,719
Speaker 1: what I actually tend to do, like I might do

899
00:49:14,760 --> 00:49:17,440
Speaker 1: it four times a year. Now that they think about it, well,

900
00:49:17,520 --> 00:49:19,479
Speaker 1: let's take the average and go five times. I feel

901
00:49:19,480 --> 00:49:21,719
Speaker 1: like I'm not being very consistent with my mathematic people

902
00:49:21,760 --> 00:49:23,880
Speaker 1: are trying to figure out how fast my hair grows

903
00:49:23,880 --> 00:49:26,680
Speaker 1: based on my strange figures. Here, I guess, but you

904
00:49:26,760 --> 00:49:30,280
Speaker 1: know that sounds good. Okay, So in this case, uh,

905
00:49:30,560 --> 00:49:33,719
Speaker 1: if you get point zero seven five pounds of hair

906
00:49:33,760 --> 00:49:35,440
Speaker 1: removed every time you get a haircut, and you get

907
00:49:35,480 --> 00:49:38,439
Speaker 1: a haircut five times a year, every year, you get

908
00:49:38,760 --> 00:49:41,960
Speaker 1: point three seven five pounds of hair removed from your

909
00:49:41,960 --> 00:49:46,200
Speaker 1: head point three seven five pounds removed every haircut or

910
00:49:46,200 --> 00:49:49,120
Speaker 1: every year. Every year, it's point zero seven five removed

911
00:49:49,120 --> 00:49:51,840
Speaker 1: per haircut, five times a year. That's point three seven

912
00:49:51,880 --> 00:49:54,480
Speaker 1: five pounds. All right, Well that number is that feels

913
00:49:54,560 --> 00:49:57,040
Speaker 1: right to me? Okay, Well, now all we need to

914
00:49:57,040 --> 00:50:00,319
Speaker 1: do is multiply by our three d million people each

915
00:50:00,400 --> 00:50:02,600
Speaker 1: each one of them gets an average of point three

916
00:50:02,640 --> 00:50:05,520
Speaker 1: seven five pounds of hair removed free year, and there

917
00:50:05,520 --> 00:50:09,600
Speaker 1: are three million people, so that gives us a total

918
00:50:09,960 --> 00:50:13,960
Speaker 1: mass of hair removed from human heads in the United

919
00:50:14,000 --> 00:50:16,880
Speaker 1: States every year of about a hundred and twelve million

920
00:50:17,120 --> 00:50:22,239
Speaker 1: pounds hundred and twelve million, five hundred thousand pounds. Does

921
00:50:22,280 --> 00:50:29,440
Speaker 1: that sound right? Mhmm, Well, we feel it feels more

922
00:50:29,560 --> 00:50:31,960
Speaker 1: right having done the leg work, you know what I'm saying, Like,

923
00:50:32,160 --> 00:50:34,200
Speaker 1: we're able to break it down. If you just come

924
00:50:34,239 --> 00:50:37,359
Speaker 1: up with that number just on the fly, I might

925
00:50:37,400 --> 00:50:39,960
Speaker 1: have really kind of um, you know, set there and

926
00:50:39,960 --> 00:50:41,480
Speaker 1: crunched it for a while thing, And I don't know

927
00:50:41,480 --> 00:50:43,240
Speaker 1: if that feels right. But since we did the legwork

928
00:50:43,239 --> 00:50:46,160
Speaker 1: and we dealt with with with quantities that were more

929
00:50:46,160 --> 00:50:49,560
Speaker 1: relatable in order to get there, I'm certainly more inclined

930
00:50:49,600 --> 00:50:52,759
Speaker 1: to trust it now. One of the beautiful things about

931
00:50:52,800 --> 00:50:57,120
Speaker 1: this type of estimation is that errors tend to balance

932
00:50:57,200 --> 00:51:00,759
Speaker 1: each other out. So one of the things we were

933
00:51:00,800 --> 00:51:03,760
Speaker 1: saying as we're going through is we're using very rough figures. Obviously,

934
00:51:03,800 --> 00:51:06,480
Speaker 1: the population in the United States is more than three million.

935
00:51:06,560 --> 00:51:09,840
Speaker 1: We just rounded down to make it easy. Um, the

936
00:51:10,040 --> 00:51:13,120
Speaker 1: amount of hair on each person's head, we don't really know.

937
00:51:13,200 --> 00:51:15,120
Speaker 1: It's a quarter of a pound. That was just a guess.

938
00:51:15,160 --> 00:51:17,080
Speaker 1: That might be too much, that might be too little.

939
00:51:17,360 --> 00:51:20,880
Speaker 1: But as you keep going through the experiment, at each stage,

940
00:51:21,280 --> 00:51:24,320
Speaker 1: you are making a guess, and that guess if unless

941
00:51:24,360 --> 00:51:29,360
Speaker 1: you're consistently biasing in one direction or another, always overestimating

942
00:51:29,440 --> 00:51:33,719
Speaker 1: or always underestimating, your errors will start to balance each

943
00:51:33,719 --> 00:51:37,120
Speaker 1: other out. And this kind of helps keep your answer

944
00:51:37,239 --> 00:51:40,160
Speaker 1: within the bounds of possibility. Even if you're wrong on

945
00:51:40,160 --> 00:51:42,640
Speaker 1: one thing, you might be wrong in the opposite direction

946
00:51:42,719 --> 00:51:47,200
Speaker 1: on another guess. It's kind of like life, and exactly

947
00:51:47,239 --> 00:51:49,799
Speaker 1: it's a lot like the game of life, or you

948
00:51:49,840 --> 00:51:53,080
Speaker 1: mean the life of life. Just just uh, a life

949
00:51:53,080 --> 00:51:56,439
Speaker 1: in general, not Life magazine, but you know that's part

950
00:51:56,440 --> 00:51:58,799
Speaker 1: of life. Oh, I should smack myself for that joke.

951
00:51:58,920 --> 00:52:02,160
Speaker 1: I'm sorry, But anyway, whether or not our answer is correct.

952
00:52:02,200 --> 00:52:05,520
Speaker 1: It maybe totally off the mark, but we've started to

953
00:52:05,520 --> 00:52:07,640
Speaker 1: give ourselves something to work with. And if we really

954
00:52:07,680 --> 00:52:10,960
Speaker 1: cared about this, like if it mattered how much hair

955
00:52:11,120 --> 00:52:14,400
Speaker 1: is removed from Americans heads every year, this would give

956
00:52:14,480 --> 00:52:16,480
Speaker 1: us a good starting place to start working with. One

957
00:52:16,480 --> 00:52:18,759
Speaker 1: of the next steps I think would be would be

958
00:52:18,800 --> 00:52:21,719
Speaker 1: to go back and look at our individual factors that

959
00:52:21,800 --> 00:52:25,640
Speaker 1: we put in throughout that that calculation process and try

960
00:52:25,680 --> 00:52:28,719
Speaker 1: to hone them and say, really, what's reasonable. You know,

961
00:52:28,760 --> 00:52:30,959
Speaker 1: we could start looking at our own heads, the heads

962
00:52:31,000 --> 00:52:33,239
Speaker 1: of people around us in the offenses and saying, it's

963
00:52:33,280 --> 00:52:35,640
Speaker 1: a quarter pound of hair real that sounds kind of high.

964
00:52:35,680 --> 00:52:39,799
Speaker 1: I don't know, but but you you can start refining

965
00:52:39,880 --> 00:52:42,560
Speaker 1: it once you've got something to work with. And that's

966
00:52:42,600 --> 00:52:46,200
Speaker 1: one of the big values of firmi estimation um. Even

967
00:52:46,239 --> 00:52:49,160
Speaker 1: though the method isn't likely to give you a precisely

968
00:52:49,239 --> 00:52:52,560
Speaker 1: correct answer every time, scientists and engineers find this type

969
00:52:52,560 --> 00:52:56,279
Speaker 1: of guessing extremely useful because it gets you into a

970
00:52:56,360 --> 00:52:59,560
Speaker 1: sort of order of magnitude ballpark where you can start

971
00:52:59,600 --> 00:53:02,920
Speaker 1: to check your gas against other modes of estimation or

972
00:53:02,960 --> 00:53:06,560
Speaker 1: against experiments and discoverable facts, and it also helps you

973
00:53:06,600 --> 00:53:10,719
Speaker 1: get your mind around what assumptions are necessary in order

974
00:53:10,760 --> 00:53:13,600
Speaker 1: to compute your final precise number. Does that make sense?

975
00:53:13,640 --> 00:53:17,879
Speaker 1: Like you start to realize what the uh? You take

976
00:53:18,200 --> 00:53:22,279
Speaker 1: things that were unknown unknowns turned into known unknowns. Now

977
00:53:22,320 --> 00:53:24,920
Speaker 1: you at least know what the variables are, even if

978
00:53:24,960 --> 00:53:28,200
Speaker 1: you don't know exactly what the numbers should be. And

979
00:53:28,280 --> 00:53:31,800
Speaker 1: turning an unknown unknown into a known unknown is halfway

980
00:53:31,840 --> 00:53:34,439
Speaker 1: along the process to turning it into a known known

981
00:53:35,760 --> 00:53:38,560
Speaker 1: or even a gnome. Well, let's hope it didn't go

982
00:53:38,640 --> 00:53:41,040
Speaker 1: that far. All right, We're gonna take a quick break,

983
00:53:41,080 --> 00:53:42,879
Speaker 1: and when we come back we will jump back into

984
00:53:42,880 --> 00:53:47,359
Speaker 1: this question of of estimating, gus estimating and UH and

985
00:53:47,440 --> 00:53:55,719
Speaker 1: so forth. Okay, we're back. Now let's look at one

986
00:53:55,719 --> 00:53:59,520
Speaker 1: of the most famous examples of a Fermi estimation type

987
00:53:59,600 --> 00:54:02,640
Speaker 1: problem him in history, and this would be the Drake

988
00:54:02,680 --> 00:54:07,280
Speaker 1: equation and the Fermi paradox. That is an interpretation on it. Yes,

989
00:54:07,480 --> 00:54:10,680
Speaker 1: all right, So in order to get this down, we

990
00:54:10,719 --> 00:54:13,160
Speaker 1: have to go back to nineteen fifty. Now, if you

991
00:54:13,200 --> 00:54:16,839
Speaker 1: remembering from earlier, that's what three years before Fermi's death.

992
00:54:18,080 --> 00:54:21,400
Speaker 1: So go back to nineteen fifty. Firmis having lunch with

993
00:54:21,440 --> 00:54:24,520
Speaker 1: his fellow egg heads at the Lost albumost Jet Propulsion

994
00:54:24,840 --> 00:54:28,640
Speaker 1: Lab Cafeteria. Alright, he's flipping through a copy of The

995
00:54:28,640 --> 00:54:32,560
Speaker 1: New Yorker when he happens upon a particular cartoon. Now,

996
00:54:33,040 --> 00:54:35,439
Speaker 1: I have a picture of the cartoon for really, it's

997
00:54:35,520 --> 00:54:38,680
Speaker 1: the original, the original, Yeah, this is the one, and

998
00:54:38,920 --> 00:54:40,279
Speaker 1: I'll try to include a link to this on the

999
00:54:40,360 --> 00:54:42,480
Speaker 1: landing page for this episode of Stuff to Blow your

1000
00:54:42,480 --> 00:54:44,600
Speaker 1: Mind dot com. So what's going on. There's a flying

1001
00:54:44,640 --> 00:54:49,839
Speaker 1: saucer and some space people are carrying baskets to and

1002
00:54:49,920 --> 00:54:55,799
Speaker 1: from it. Yeah, they're they're collecting garbage apparently, uh furiously enough,

1003
00:54:55,840 --> 00:54:58,120
Speaker 1: I don't have the caption here, or I don't know

1004
00:54:58,200 --> 00:55:00,879
Speaker 1: they were doing the caption contest back then. But if

1005
00:55:01,000 --> 00:55:03,640
Speaker 1: if the caption contest from The New Yorker makes its

1006
00:55:03,640 --> 00:55:06,239
Speaker 1: way across your social media feeds, you know exactly what

1007
00:55:06,400 --> 00:55:09,479
Speaker 1: sort of cartoon we're talking here. So it's not quite

1008
00:55:09,480 --> 00:55:12,279
Speaker 1: far side. It's not a laugh out loud funny, but

1009
00:55:12,680 --> 00:55:14,799
Speaker 1: you look at it and your your your wheels began

1010
00:55:14,880 --> 00:55:17,160
Speaker 1: to turn a little bit. And that's what happened with Firmy.

1011
00:55:17,239 --> 00:55:20,520
Speaker 1: He looks at this, and if he were to enter

1012
00:55:20,600 --> 00:55:24,040
Speaker 1: the New York the New Yorker caption contest. His caption

1013
00:55:24,040 --> 00:55:27,520
Speaker 1: would have been where is everybody? Because that is, according

1014
00:55:27,560 --> 00:55:29,920
Speaker 1: to this story, the question he asked, and he was

1015
00:55:30,000 --> 00:55:34,520
Speaker 1: referring to the aliens, to life beyond this insignificant rock

1016
00:55:34,560 --> 00:55:37,839
Speaker 1: of ours. He wondered, uh, more specifically, you know, not

1017
00:55:37,880 --> 00:55:39,880
Speaker 1: only like where are where these aliens at, but he

1018
00:55:39,920 --> 00:55:45,040
Speaker 1: wondered whether interstellar travel was even possible. And indeed, as

1019
00:55:45,040 --> 00:55:48,400
Speaker 1: far as we know it has not occurred. You know,

1020
00:55:48,480 --> 00:55:51,480
Speaker 1: I mean this when we get we kind of broke

1021
00:55:51,480 --> 00:55:53,640
Speaker 1: it down some of this in our the episode the

1022
00:55:53,680 --> 00:55:56,480
Speaker 1: Christian and I did on the expanse and the idea

1023
00:55:56,480 --> 00:55:59,560
Speaker 1: of just like the vast distances in our universe, like

1024
00:55:59,600 --> 00:56:02,240
Speaker 1: even the instances between our planets in our Solar system

1025
00:56:02,239 --> 00:56:05,760
Speaker 1: are pretty colossal, and when you start extrapolating that beyond

1026
00:56:05,840 --> 00:56:10,880
Speaker 1: our system, uh, it just gets increasingly just incredibly distant.

1027
00:56:10,920 --> 00:56:14,880
Speaker 1: There is so much space in space. And so he

1028
00:56:14,960 --> 00:56:17,600
Speaker 1: was saying, well, you know, where are they? Is it

1029
00:56:17,640 --> 00:56:21,040
Speaker 1: even possible for for life forms to travel between stars?

1030
00:56:21,600 --> 00:56:23,799
Speaker 1: Why aren't we seeing them? Why aren't we hearing from them?

1031
00:56:23,840 --> 00:56:28,839
Speaker 1: Exactly so FIRMI died, you know, foot four years later,

1032
00:56:28,880 --> 00:56:31,960
Speaker 1: at the age of fifty four, but the question that

1033
00:56:32,000 --> 00:56:34,879
Speaker 1: he asked lived on, and the problem filtered through the firm,

1034
00:56:34,880 --> 00:56:38,520
Speaker 1: these coworkers, his contemporaries, and it became something of a legend.

1035
00:56:39,040 --> 00:56:43,239
Speaker 1: And in nive, the astronomer Michael Hart declared that the

1036
00:56:43,320 --> 00:56:45,719
Speaker 1: reason we don't see any aliens is because they do

1037
00:56:45,800 --> 00:56:49,600
Speaker 1: not exist, which you know, that's that's one possible answer.

1038
00:56:49,719 --> 00:56:52,920
Speaker 1: It certainly is. And then in nineteen seventy seven and

1039
00:56:53,000 --> 00:56:56,360
Speaker 1: astrophysicists by the name of David G. Stevenson said that

1040
00:56:56,440 --> 00:57:01,120
Speaker 1: heart statement could answer firm's question, which he officially dubbed

1041
00:57:01,360 --> 00:57:05,080
Speaker 1: Firm's paradox. So to be clear, Fermi himself did not

1042
00:57:05,239 --> 00:57:09,680
Speaker 1: pose the question. The paradox is merely named for him

1043
00:57:09,840 --> 00:57:12,040
Speaker 1: in honor of him and in accordance with this sort

1044
00:57:12,040 --> 00:57:17,680
Speaker 1: of folkloric idea. Right, But the sort of general mode

1045
00:57:17,760 --> 00:57:21,520
Speaker 1: of guessing or gues estimating that's now known as Fermi

1046
00:57:21,680 --> 00:57:25,840
Speaker 1: estimation or a Fermi type problem is related to this

1047
00:57:26,480 --> 00:57:29,680
Speaker 1: because there is what's known as the Drake equation, and

1048
00:57:29,720 --> 00:57:32,920
Speaker 1: the Drake equation is kind of like playing the how

1049
00:57:32,960 --> 00:57:37,760
Speaker 1: many piano tuners game or in Chicago game with the

1050
00:57:37,800 --> 00:57:42,360
Speaker 1: Milky Way galaxy. It is a Fermi guess formulation designed

1051
00:57:42,400 --> 00:57:45,440
Speaker 1: to estimate the number of piano tuners in the Milky Way,

1052
00:57:45,560 --> 00:57:48,520
Speaker 1: or wait a minute, no, the number of technological civilization

1053
00:57:49,320 --> 00:57:52,240
Speaker 1: in the Milky Way galaxy, meaning the number of civilizations

1054
00:57:52,240 --> 00:57:56,360
Speaker 1: whose electromagnetic emissions like radio waves, we should be able

1055
00:57:56,400 --> 00:58:00,640
Speaker 1: to detect today. And so it takes to form. There's

1056
00:58:00,640 --> 00:58:04,080
Speaker 1: actually an equation, says okay, in that's the answer, and

1057
00:58:04,120 --> 00:58:07,120
Speaker 1: that's the number of civilizations in the Milky Way galaxy

1058
00:58:07,160 --> 00:58:10,320
Speaker 1: whose electromagnetic emissions are detectable. And the version of this

1059
00:58:10,400 --> 00:58:12,960
Speaker 1: I'm using is the one that st has on their website.

1060
00:58:13,600 --> 00:58:18,440
Speaker 1: And to calculate in you multiply are which is the

1061
00:58:18,520 --> 00:58:21,960
Speaker 1: rate of formation of stars suitable for the development of

1062
00:58:21,960 --> 00:58:27,400
Speaker 1: intelligent life, by f P, meaning the fraction of those

1063
00:58:27,440 --> 00:58:30,240
Speaker 1: stars with planetary systems. Not all stars are going to

1064
00:58:30,320 --> 00:58:34,320
Speaker 1: have planets, and then you multiply that by in e

1065
00:58:34,760 --> 00:58:38,000
Speaker 1: the number of planets per solar system with an environment

1066
00:58:38,040 --> 00:58:43,360
Speaker 1: suitable for life. So every solar system uh might might

1067
00:58:43,400 --> 00:58:46,720
Speaker 1: have planets, but wouldn't necessarily have planets within the habitable zone.

1068
00:58:47,280 --> 00:58:49,840
Speaker 1: It might be all too hot or too cold. And

1069
00:58:49,880 --> 00:58:53,320
Speaker 1: then you've got f L the fraction of suitable planets

1070
00:58:53,360 --> 00:58:55,680
Speaker 1: on which life actually appears. Might be a lot of

1071
00:58:55,760 --> 00:58:58,840
Speaker 1: nice planets out there, but they're just dead. Uh. And

1072
00:58:58,840 --> 00:59:02,320
Speaker 1: then f I the fraction of life bearing planets on

1073
00:59:02,360 --> 00:59:05,400
Speaker 1: which intelligent life emerges. Maybe a lot of planets out

1074
00:59:05,440 --> 00:59:08,400
Speaker 1: there just to have bacteria on them. And then f

1075
00:59:08,800 --> 00:59:13,520
Speaker 1: C the fraction of civilizations that develop a technology that

1076
00:59:13,560 --> 00:59:16,960
Speaker 1: releases detectable signs of their existence into space, So there

1077
00:59:17,040 --> 00:59:19,320
Speaker 1: might be intelligent life out there, but they're not making

1078
00:59:19,400 --> 00:59:24,640
Speaker 1: radio waves. And then finally, multiplied by L the length

1079
00:59:24,760 --> 00:59:30,240
Speaker 1: of time such civilizations released detectable signals into space. So

1080
00:59:30,720 --> 00:59:34,440
Speaker 1: many of the variables in this in this calculation are

1081
00:59:34,480 --> 00:59:37,840
Speaker 1: pure unknowns. Answers are all over the place for this reason.

1082
00:59:38,840 --> 00:59:40,960
Speaker 1: But a lot of things in here are not as

1083
00:59:41,080 --> 00:59:43,800
Speaker 1: unknown as they once were. For example, we're starting to

1084
00:59:43,840 --> 00:59:45,960
Speaker 1: get a very good sense of the fraction of stars

1085
00:59:45,960 --> 00:59:50,200
Speaker 1: with planetary systems and the average number of planets suitable

1086
00:59:50,360 --> 00:59:53,040
Speaker 1: for life in the Milky Way galaxy. We're starting to say, okay,

1087
00:59:53,040 --> 00:59:55,560
Speaker 1: this is about how many planets are out there. Here's

1088
00:59:55,560 --> 00:59:58,320
Speaker 1: the proportion of them that are, you know, not too

1089
00:59:58,320 --> 01:00:01,240
Speaker 1: hot or too cold to sustain life. Those are coming

1090
01:00:01,480 --> 01:00:05,680
Speaker 1: to within reckoning distance. Other variables about like the prevalence

1091
01:00:05,720 --> 01:00:08,720
Speaker 1: of emergence of life and intelligence. Those are still just

1092
01:00:08,800 --> 01:00:11,479
Speaker 1: big question marks, but you can still play the same

1093
01:00:11,520 --> 01:00:14,680
Speaker 1: game with them. You could try to set up boundary conditions, Right,

1094
01:00:15,040 --> 01:00:18,000
Speaker 1: what's the lowest boundary. While the lowest boundary would be

1095
01:00:18,040 --> 01:00:20,280
Speaker 1: I don't know, some fraction of one. I mean, obviously

1096
01:00:20,320 --> 01:00:23,520
Speaker 1: wouldn't be zero because we're here, so we know that

1097
01:00:23,600 --> 01:00:28,240
Speaker 1: it's a non zero chance that these things happen. What's

1098
01:00:28,240 --> 01:00:32,400
Speaker 1: the highest possible thing, Well, obviously we're not seeing these uh,

1099
01:00:32,720 --> 01:00:36,000
Speaker 1: these planets with life on them in our solar system

1100
01:00:36,200 --> 01:00:38,919
Speaker 1: other than other than Earth. Well, actually we don't even

1101
01:00:38,920 --> 01:00:41,360
Speaker 1: know that for sure yet. But anyway, there are a

1102
01:00:41,400 --> 01:00:43,480
Speaker 1: lot of ways you can try to put numbers in

1103
01:00:44,000 --> 01:00:47,360
Speaker 1: where these variables exist. And so I've seen estimates using

1104
01:00:47,400 --> 01:00:50,480
Speaker 1: the Drake equation turn up answers less than one, meaning

1105
01:00:50,560 --> 01:00:53,800
Speaker 1: we're almost definitely alone in the galaxy, and even our

1106
01:00:53,840 --> 01:00:57,000
Speaker 1: existence is a real stroke of luck. Uh. And then

1107
01:00:57,040 --> 01:00:59,640
Speaker 1: I've seen ones that are in the hundreds of millions.

1108
01:00:59,640 --> 01:01:04,200
Speaker 1: But in that case, what's the deal. Why aren't we

1109
01:01:04,400 --> 01:01:07,760
Speaker 1: detecting anything? Are we in some kind of protected zoo

1110
01:01:07,880 --> 01:01:11,160
Speaker 1: where we're you know, the aliens hiding from us? The

1111
01:01:11,760 --> 01:01:15,520
Speaker 1: nature reserve theory. Right. Yeah, But one interesting thing is

1112
01:01:15,520 --> 01:01:18,240
Speaker 1: what we mentioned earlier. Whenever you're doing these types of

1113
01:01:18,240 --> 01:01:21,960
Speaker 1: of estimations, uh, it's good to check them against reality.

1114
01:01:22,040 --> 01:01:24,520
Speaker 1: So you might think of our actual radio astronomy as

1115
01:01:24,560 --> 01:01:27,720
Speaker 1: a reality check on the numbers generated or the gu

1116
01:01:27,800 --> 01:01:30,880
Speaker 1: estimates of the Drake equation. So this is this is

1117
01:01:30,920 --> 01:01:33,280
Speaker 1: fascinating again because you've taken something that is like a

1118
01:01:33,440 --> 01:01:38,120
Speaker 1: giant gaping mystery and unknown and you boil it down

1119
01:01:38,120 --> 01:01:43,840
Speaker 1: into a series of essentially smaller unknowns uh nowns and

1120
01:01:43,600 --> 01:01:48,200
Speaker 1: and guessable factors. Yeah, exactly, You're you're making the problem

1121
01:01:48,320 --> 01:01:51,560
Speaker 1: workable and and so this is a way in which

1122
01:01:52,160 --> 01:01:55,880
Speaker 1: fermi estimation has multiple uses. I guess one of them

1123
01:01:56,040 --> 01:01:58,920
Speaker 1: is practical. It's just practical, and you know, when you

1124
01:01:58,960 --> 01:02:01,080
Speaker 1: don't know any of the actors, you can use it

1125
01:02:01,120 --> 01:02:03,560
Speaker 1: to come up with a reasonable guests for an answer.

1126
01:02:04,000 --> 01:02:05,959
Speaker 1: But the other thing is what we've been talking about.

1127
01:02:06,080 --> 01:02:09,720
Speaker 1: It's making a problem more understandable, even if you don't

1128
01:02:09,760 --> 01:02:13,000
Speaker 1: actually come up with a reasonable answer. It starts to

1129
01:02:13,080 --> 01:02:15,400
Speaker 1: help you get your mind around what you would need

1130
01:02:15,480 --> 01:02:18,160
Speaker 1: to know in order to solve it. All Right, we're

1131
01:02:18,160 --> 01:02:20,200
Speaker 1: gonna take a quick breaking. We come back, we're gonna

1132
01:02:20,280 --> 01:02:24,200
Speaker 1: discuss some of the softer social science of guessing and

1133
01:02:24,520 --> 01:02:31,760
Speaker 1: try to conduct an experiment of our own. Alright, So

1134
01:02:31,760 --> 01:02:34,440
Speaker 1: we've discussed how guesswork is art as well as science,

1135
01:02:34,440 --> 01:02:36,880
Speaker 1: and indeed there's certainly a social art to it in

1136
01:02:36,920 --> 01:02:41,680
Speaker 1: some cases, so the art of overestimation or underestimation in

1137
01:02:41,760 --> 01:02:45,840
Speaker 1: social situations. I think we've all encountered situations in which

1138
01:02:45,920 --> 01:02:49,400
Speaker 1: guessing isn't merely about making a correct guess. It's also

1139
01:02:49,400 --> 01:02:52,360
Speaker 1: about making a guess that lands with an appropriate level

1140
01:02:52,400 --> 01:02:55,720
Speaker 1: of social grace. It's like guess what my s A

1141
01:02:55,800 --> 01:02:58,760
Speaker 1: T score was? Yeah, like a weird questions like that

1142
01:02:59,480 --> 01:03:02,080
Speaker 1: like another A notable example would be guess how old

1143
01:03:02,080 --> 01:03:05,040
Speaker 1: I am, which is generally a question you only ask

1144
01:03:05,120 --> 01:03:08,880
Speaker 1: a child or you ask if you are a child. Um,

1145
01:03:09,160 --> 01:03:12,040
Speaker 1: because it's floated right, and I've en to your point.

1146
01:03:12,120 --> 01:03:14,400
Speaker 1: You also see guess how how much I make as

1147
01:03:14,440 --> 01:03:18,400
Speaker 1: being another question that is sometimes asked. Uh. The need

1148
01:03:18,480 --> 01:03:21,320
Speaker 1: for for such a guest might not come up directly,

1149
01:03:21,520 --> 01:03:23,640
Speaker 1: but of course we can all imagine situations where it

1150
01:03:23,800 --> 01:03:25,880
Speaker 1: ends up. When they end up coming up, you know,

1151
01:03:25,960 --> 01:03:28,080
Speaker 1: like you're trying to figure out if a friend of

1152
01:03:28,120 --> 01:03:30,280
Speaker 1: yours is into the same movie that you are, and

1153
01:03:30,320 --> 01:03:32,200
Speaker 1: you're like, oh, well, how old are you? You're such

1154
01:03:32,200 --> 01:03:35,280
Speaker 1: and such. You know, so you might indirectly, indirectly find

1155
01:03:35,280 --> 01:03:38,520
Speaker 1: yourself having to make such a guess. So this is

1156
01:03:38,560 --> 01:03:41,000
Speaker 1: a very conundrum. Is actually explored in the Art and

1157
01:03:41,080 --> 01:03:44,680
Speaker 1: Science of Guessing by Shin, c. Zong and Duh And

1158
01:03:44,680 --> 01:03:47,400
Speaker 1: this is published in the journal Emotion in twenty eleven.

1159
01:03:48,160 --> 01:03:50,760
Speaker 1: So they ask, you know, are we are we gonna

1160
01:03:50,800 --> 01:03:53,840
Speaker 1: be happier with over guessing or happier with under guessing

1161
01:03:54,000 --> 01:03:57,240
Speaker 1: just in general, like people guessing too high or guessing

1162
01:03:57,240 --> 01:03:59,960
Speaker 1: too low? Yeah, how does that make you feel when

1163
01:04:00,080 --> 01:04:04,640
Speaker 1: someone get over or under estimate something about you? Now?

1164
01:04:04,720 --> 01:04:07,680
Speaker 1: Is this limited to certain types of factors? Are they

1165
01:04:07,760 --> 01:04:10,880
Speaker 1: trying to get a general effect for any sorts of numbers?

1166
01:04:11,520 --> 01:04:15,360
Speaker 1: Um general? But like they're they're focusing around very specific

1167
01:04:15,440 --> 01:04:19,439
Speaker 1: questions as as we'll discuss. So they predicted that over

1168
01:04:19,520 --> 01:04:23,400
Speaker 1: guessing would reign supreme. Uh, though obviously not with guessing

1169
01:04:23,520 --> 01:04:25,680
Speaker 1: another person's age, because that one kind of stands out

1170
01:04:25,680 --> 01:04:27,760
Speaker 1: generally you want people to get through you're younger than

1171
01:04:27,800 --> 01:04:31,320
Speaker 1: you are, Okay, So naturally the research has conducted a

1172
01:04:31,320 --> 01:04:33,360
Speaker 1: few tests to try this out, and it's important to

1173
01:04:33,400 --> 01:04:36,600
Speaker 1: note culturally, as we'll get into that. Some experiments were

1174
01:04:36,600 --> 01:04:39,800
Speaker 1: conducted into China and others in the US, and that's

1175
01:04:39,880 --> 01:04:43,600
Speaker 1: especially important with experiment one, which concerns asking friends how

1176
01:04:43,680 --> 01:04:46,400
Speaker 1: much money they make, which I don't know about about you,

1177
01:04:46,520 --> 01:04:49,120
Speaker 1: but generally that that's not something that is done at

1178
01:04:49,120 --> 01:04:52,160
Speaker 1: dinner parties. Did I go to where people say, hey,

1179
01:04:52,160 --> 01:04:53,680
Speaker 1: how much money do you make in a year? Not

1180
01:04:53,800 --> 01:04:57,560
Speaker 1: my friends. I asked my enemies how much? Yeah, you know,

1181
01:04:57,600 --> 01:05:00,560
Speaker 1: I guess with family members, maybe it's more practical. Originally,

1182
01:05:00,640 --> 01:05:03,120
Speaker 1: friends and contemporaries are not asking that question. It's kind

1183
01:05:03,120 --> 01:05:06,880
Speaker 1: of taboo, but according to the research in China, it

1184
01:05:07,120 --> 01:05:10,720
Speaker 1: is was more common. So they used forty employees from

1185
01:05:10,760 --> 01:05:13,520
Speaker 1: multiple companies in a large city in China, and I'll

1186
01:05:13,560 --> 01:05:16,880
Speaker 1: spare you the monetary details of the study, but the

1187
01:05:16,920 --> 01:05:20,200
Speaker 1: finding was quote contrary to what common wisdom and existing

1188
01:05:20,240 --> 01:05:24,160
Speaker 1: literature would suggest. The study revealed a happier with under

1189
01:05:24,200 --> 01:05:27,800
Speaker 1: guessing effect. So someone thinks, oh, well, you just you

1190
01:05:27,920 --> 01:05:30,000
Speaker 1: probably make thirty thousand a year, but you actually make

1191
01:05:30,080 --> 01:05:32,600
Speaker 1: thirty five, but you feel happy, So I guess it's

1192
01:05:32,640 --> 01:05:34,760
Speaker 1: like like, oh, you get to prove them wrong. You

1193
01:05:34,800 --> 01:05:36,720
Speaker 1: get to prove them wrong. Yeah, you're like, oh, you

1194
01:05:36,720 --> 01:05:38,479
Speaker 1: think I'm only worth that much, but I'm actually worth

1195
01:05:38,520 --> 01:05:42,160
Speaker 1: this much. I'm fantastic. That's kind of the response. No,

1196
01:05:42,320 --> 01:05:46,840
Speaker 1: that makes sense to me. So. Experiment to tackled academic

1197
01:05:46,880 --> 01:05:50,040
Speaker 1: performance with American test subjects a hundred and seven business

1198
01:05:50,080 --> 01:05:54,320
Speaker 1: students guessing each other's GMAT scores as a graduate Management

1199
01:05:54,360 --> 01:05:58,600
Speaker 1: admissions test. The results the under guest was most pleasing,

1200
01:05:58,880 --> 01:06:02,120
Speaker 1: the over guests was least pleasing, and the accurate guest

1201
01:06:02,400 --> 01:06:05,360
Speaker 1: was in between. I think it's interesting here that the

1202
01:06:05,480 --> 01:06:09,080
Speaker 1: accurate guess is somewhere in between, Like, nobody really wants

1203
01:06:09,080 --> 01:06:13,200
Speaker 1: to be pinned down completely. No, it doesn't feel good, yeah, even,

1204
01:06:13,440 --> 01:06:17,160
Speaker 1: but it also feels bad to be overestimated, Like it's

1205
01:06:17,200 --> 01:06:20,680
Speaker 1: the the inner Like if you're underestimated, you you get

1206
01:06:20,720 --> 01:06:23,760
Speaker 1: that that feeling of oh, I'm actually I'm actually better

1207
01:06:23,760 --> 01:06:26,120
Speaker 1: than you think I am. But if they if you're overestimated,

1208
01:06:26,160 --> 01:06:28,840
Speaker 1: there might be like this superficial feeling of oh they

1209
01:06:28,880 --> 01:06:31,439
Speaker 1: think I'm they think I'm better than I am, but

1210
01:06:31,840 --> 01:06:34,720
Speaker 1: but I'm actually not. It might be nice to have

1211
01:06:34,720 --> 01:06:37,800
Speaker 1: people guess, like what your favorite movies are or something.

1212
01:06:38,320 --> 01:06:40,120
Speaker 1: But it does not seem like it's nice to have

1213
01:06:40,160 --> 01:06:44,280
Speaker 1: people correctly guess what numbers are true about you. Yeah,

1214
01:06:44,320 --> 01:06:48,200
Speaker 1: it's it's a quantitative aspect that makes accuracy unpleasant. It's

1215
01:06:48,200 --> 01:06:50,520
Speaker 1: like being pinned down to a chart. So then came

1216
01:06:50,560 --> 01:06:52,880
Speaker 1: Experiment three two thousand and nine. Business students from a

1217
01:06:52,920 --> 01:06:56,560
Speaker 1: large university in the United States engaged in imagined scenario. Okay,

1218
01:06:56,600 --> 01:06:59,200
Speaker 1: you work at a large company. Your annual bonus will

1219
01:06:59,200 --> 01:07:02,800
Speaker 1: be between three thousand and thirty thousand dollars. Exact amount

1220
01:07:02,840 --> 01:07:05,960
Speaker 1: will be confidential. So participants were then told, in this

1221
01:07:06,040 --> 01:07:10,440
Speaker 1: imaginary experience experiment here uh scenario that they'd receive fifteen

1222
01:07:10,640 --> 01:07:13,480
Speaker 1: thousand dollars, and they were asked to imagine that they

1223
01:07:13,520 --> 01:07:16,920
Speaker 1: heard a colleague guessing about their bonus. The guests was

1224
01:07:17,040 --> 01:07:20,280
Speaker 1: thirty thousand in the over guest condition and three thousand

1225
01:07:20,280 --> 01:07:22,440
Speaker 1: in the under guest condition, And then they were asked

1226
01:07:22,480 --> 01:07:25,000
Speaker 1: to indicate whether they felt better or worse about hearing

1227
01:07:25,000 --> 01:07:28,440
Speaker 1: the guests The results. Again, the overguess resulted in the

1228
01:07:28,480 --> 01:07:31,760
Speaker 1: most happiness, But the researchers drive home that a lot

1229
01:07:31,840 --> 01:07:33,880
Speaker 1: of that results boils down to what's more important. To

1230
01:07:33,960 --> 01:07:37,240
Speaker 1: the individual individual truth or impression. So really, really, what

1231
01:07:37,360 --> 01:07:40,640
Speaker 1: ends up mattering more to a specific individual the actual

1232
01:07:40,680 --> 01:07:43,200
Speaker 1: amount of money they take home or the amount of

1233
01:07:43,200 --> 01:07:47,000
Speaker 1: money that people think they take home. And this is interesting, right,

1234
01:07:47,040 --> 01:07:49,680
Speaker 1: because so much in life is this mixture of substance

1235
01:07:49,680 --> 01:07:52,320
Speaker 1: and perception. Do you want to be rich or do

1236
01:07:52,320 --> 01:07:55,280
Speaker 1: you want to appear rich? Do you want to be

1237
01:07:55,440 --> 01:07:58,840
Speaker 1: smart or appear smart? And and and there's kind of

1238
01:07:58,840 --> 01:08:02,680
Speaker 1: this this up is this push and pull of both factors.

1239
01:08:03,200 --> 01:08:05,560
Speaker 1: We're back to the charm effect, the James Bond effect,

1240
01:08:05,680 --> 01:08:07,480
Speaker 1: And we were talking about at the beginning some people

1241
01:08:07,560 --> 01:08:12,360
Speaker 1: might actually not be uh better, more lucky than others,

1242
01:08:12,440 --> 01:08:15,520
Speaker 1: but they can sure appear that way just by sort

1243
01:08:15,520 --> 01:08:21,080
Speaker 1: of projecting a successful latitude. Yeah. Yeah, So the soft

1244
01:08:21,120 --> 01:08:24,480
Speaker 1: science of guessing becomes even softer some more you you

1245
01:08:24,479 --> 01:08:27,679
Speaker 1: you tease at it. Okay, one last thing, I want

1246
01:08:27,680 --> 01:08:29,599
Speaker 1: to look at a totally different kind of guessing. We've

1247
01:08:29,600 --> 01:08:32,320
Speaker 1: talked about tools to make you better at guessing, but

1248
01:08:32,360 --> 01:08:34,160
Speaker 1: I want to think about what goes on in the

1249
01:08:34,240 --> 01:08:38,080
Speaker 1: human mind when we guess. When we've got absolutely nothing

1250
01:08:38,160 --> 01:08:43,200
Speaker 1: to work with, no info, no probabilities, no plausible boundaries,

1251
01:08:43,520 --> 01:08:48,000
Speaker 1: just the opaque magic of pure randomness, because this is

1252
01:08:48,160 --> 01:08:49,960
Speaker 1: this is sort of the core of guessing. When we

1253
01:08:50,000 --> 01:08:53,760
Speaker 1: say guessing, you know, a lot of what guessing conjures

1254
01:08:53,800 --> 01:08:58,879
Speaker 1: in the mind is scenarios of total uncertainty randomness. Okay,

1255
01:08:59,080 --> 01:09:01,799
Speaker 1: so I want to do an experiment with you, Robert.

1256
01:09:02,280 --> 01:09:05,360
Speaker 1: I've got a deck of cards fanned out here. Here's

1257
01:09:05,400 --> 01:09:10,719
Speaker 1: the experiment. I'm holding up a card to Robert. Okay,

1258
01:09:11,000 --> 01:09:13,680
Speaker 1: what is the suit of this card? Now you are

1259
01:09:13,720 --> 01:09:15,680
Speaker 1: not looking at the face of the card. Robert is

1260
01:09:15,720 --> 01:09:18,400
Speaker 1: looking at the back of Well, this is awesome because

1261
01:09:18,400 --> 01:09:20,720
Speaker 1: I I have a one in four chance, right right,

1262
01:09:20,720 --> 01:09:25,000
Speaker 1: I'm gonna say clubs, Nope, jack of spades. Now let

1263
01:09:25,080 --> 01:09:28,479
Speaker 1: me try it again. Now, think really hard, this time

1264
01:09:30,360 --> 01:09:33,519
Speaker 1: the exact card. No, you are guessing the suit. Okay,

1265
01:09:33,520 --> 01:09:37,639
Speaker 1: I'm gonna say clubs, nope, hearts. But here's the question.

1266
01:09:38,680 --> 01:09:42,000
Speaker 1: Where did your answers come from? Your accuracy was actually

1267
01:09:42,080 --> 01:09:45,760
Speaker 1: not important to me. There, I'm thinking about the subjective experience.

1268
01:09:45,800 --> 01:09:52,599
Speaker 1: Try it one more time, Nope, spades. Why though, why

1269
01:09:52,680 --> 01:09:55,559
Speaker 1: did you say clubs when you have no reason to

1270
01:09:55,600 --> 01:09:59,040
Speaker 1: prefer clubs over any other I don't know, it just

1271
01:09:59,080 --> 01:10:01,599
Speaker 1: came to my mind. For I was for it's it's

1272
01:10:01,600 --> 01:10:03,559
Speaker 1: almost like not that I was at a loss for

1273
01:10:03,640 --> 01:10:06,080
Speaker 1: the words, but like that was the one that came

1274
01:10:06,160 --> 01:10:08,920
Speaker 1: up first. Yeah, I mean, it's it's a weird thing.

1275
01:10:09,040 --> 01:10:12,280
Speaker 1: It's like, next time you make a guess without a

1276
01:10:12,320 --> 01:10:16,600
Speaker 1: conscious methodology, you out there listening, look inside yourself and

1277
01:10:16,640 --> 01:10:20,040
Speaker 1: ask this question, where did that guests come from? Why

1278
01:10:20,080 --> 01:10:23,240
Speaker 1: did I say clubs and not something else when I

1279
01:10:23,280 --> 01:10:26,639
Speaker 1: had no logical reason to prefer clubs over anything else.

1280
01:10:26,720 --> 01:10:29,000
Speaker 1: I will say I stuck to clubs because I thought

1281
01:10:29,200 --> 01:10:31,599
Speaker 1: clubs has got to come up, like I might as well,

1282
01:10:31,640 --> 01:10:35,559
Speaker 1: even though I guess it's it's yeah, yeah, it seemed

1283
01:10:35,600 --> 01:10:37,120
Speaker 1: like the thing to do, like I just should should

1284
01:10:37,200 --> 01:10:40,280
Speaker 1: just stick to clubs and clubs will do me ride eventually. Well,

1285
01:10:40,439 --> 01:10:42,760
Speaker 1: that actually would be a smart strategy. If I was

1286
01:10:42,840 --> 01:10:47,240
Speaker 1: like removing cards from the deck and you were yeah, okay, okay, Well,

1287
01:10:47,320 --> 01:10:49,040
Speaker 1: then I guess the question would be, what what did

1288
01:10:49,040 --> 01:10:50,840
Speaker 1: you guess the first time? Or what would you have

1289
01:10:50,880 --> 01:10:53,519
Speaker 1: guessed if I was not removing cards from the deck,

1290
01:10:54,439 --> 01:10:57,840
Speaker 1: Because that yeah, there there's no there's just nothing you

1291
01:10:57,880 --> 01:11:00,439
Speaker 1: can do, and yet our brains still are able to

1292
01:11:00,479 --> 01:11:02,120
Speaker 1: come up with an answer. And I think this is

1293
01:11:02,160 --> 01:11:05,160
Speaker 1: one of those everyday moments that sort of passes by

1294
01:11:05,240 --> 01:11:08,960
Speaker 1: us without much fanfare. Just it's very humdrum, But if

1295
01:11:09,000 --> 01:11:11,719
Speaker 1: you force yourself to stop and examine it, it becomes

1296
01:11:11,760 --> 01:11:17,000
Speaker 1: so deeply weird and mysterious. We've we've got these voids

1297
01:11:17,040 --> 01:11:22,080
Speaker 1: inside our minds that produce information on no input. It's

1298
01:11:22,120 --> 01:11:23,920
Speaker 1: kind of like you you go somewhere in the back

1299
01:11:23,960 --> 01:11:25,679
Speaker 1: of your mind and there's one of those drive through

1300
01:11:25,720 --> 01:11:28,880
Speaker 1: bank teller boxes, you know, where it slides out and

1301
01:11:28,920 --> 01:11:31,200
Speaker 1: you open the shutter, and what you put in is

1302
01:11:31,280 --> 01:11:34,479
Speaker 1: just a request for a random response, and you push

1303
01:11:34,520 --> 01:11:37,240
Speaker 1: it in, and a split second later, the box slams

1304
01:11:37,280 --> 01:11:40,080
Speaker 1: back out, pops open with an answer for you. What

1305
01:11:40,280 --> 01:11:44,679
Speaker 1: happened inside? Where did that random answer come from? Uh?

1306
01:11:45,160 --> 01:11:46,960
Speaker 1: That might not even occur to you as something to

1307
01:11:46,960 --> 01:11:49,400
Speaker 1: think about being odd, But I don't know. It strikes

1308
01:11:49,400 --> 01:11:51,840
Speaker 1: me as very odd. Why do our brains come up

1309
01:11:51,880 --> 01:11:57,160
Speaker 1: with random answers on command, with no logical reasoning behind them.

1310
01:11:57,880 --> 01:12:00,080
Speaker 1: One example that I do encounter with this sometimes is

1311
01:12:00,120 --> 01:12:02,320
Speaker 1: in yoga class will be and we'll be doing a plank,

1312
01:12:02,680 --> 01:12:04,479
Speaker 1: and in order to pass the time, we'll go through

1313
01:12:04,520 --> 01:12:07,120
Speaker 1: the alphabet and like name trees that begin with each letter,

1314
01:12:07,520 --> 01:12:09,719
Speaker 1: and it's curious to self reflect and be like why

1315
01:12:09,720 --> 01:12:12,600
Speaker 1: did that tree come up? Why did that animal come up? Yeah,

1316
01:12:12,640 --> 01:12:14,640
Speaker 1: and sometimes it feels like the brain just spits it

1317
01:12:14,680 --> 01:12:17,599
Speaker 1: out randomly, like a like a hand with a deck

1318
01:12:17,640 --> 01:12:20,559
Speaker 1: of cards just shooting one to the surface. Yeah, so

1319
01:12:20,640 --> 01:12:25,200
Speaker 1: what's causing one card to come up instead of another? Um? So,

1320
01:12:25,600 --> 01:12:28,479
Speaker 1: in terms of coming up with true randomness, I've actually

1321
01:12:28,479 --> 01:12:33,520
Speaker 1: read a little bit about research into people studying humans

1322
01:12:33,640 --> 01:12:37,800
Speaker 1: ability to generate random numbers on command, Like this is

1323
01:12:37,840 --> 01:12:40,200
Speaker 1: actually a field of study. It's like, can you please

1324
01:12:40,280 --> 01:12:43,919
Speaker 1: list a series of random one digit numbers? One problem

1325
01:12:43,960 --> 01:12:47,160
Speaker 1: is that people are actually very cruddy random number generators,

1326
01:12:47,240 --> 01:12:49,720
Speaker 1: Like they they either have too much symmetry in their

1327
01:12:49,760 --> 01:12:53,800
Speaker 1: answers or too little symmetry. Um Like that they get

1328
01:12:53,840 --> 01:12:56,240
Speaker 1: caught up in trying to make it random, and thus

1329
01:12:56,320 --> 01:12:59,400
Speaker 1: they make it non random. But yeah, I just think

1330
01:12:59,400 --> 01:13:02,800
Speaker 1: it's interesting, Like what's what's the biological purpose of that? Like,

1331
01:13:02,840 --> 01:13:05,479
Speaker 1: why is that something brains can do? It's something you

1332
01:13:05,520 --> 01:13:09,679
Speaker 1: specifically have to have to command computers to figure out

1333
01:13:09,680 --> 01:13:13,280
Speaker 1: how to do. Computers by nature don't generate random numbers.

1334
01:13:13,280 --> 01:13:15,000
Speaker 1: You need to come up with a way of them

1335
01:13:15,000 --> 01:13:18,080
Speaker 1: to you know, draw on some kind of vada variable

1336
01:13:18,160 --> 01:13:21,960
Speaker 1: or data to generate random numbers. UM, So like why

1337
01:13:22,000 --> 01:13:25,280
Speaker 1: do brains do that? And where do the numbers come from? Uh?

1338
01:13:25,320 --> 01:13:27,439
Speaker 1: There there was one study that I looked at that

1339
01:13:27,439 --> 01:13:30,719
Speaker 1: I thought was kind of interesting, and it's a study

1340
01:13:30,720 --> 01:13:35,160
Speaker 1: by Elliott Rees and Dolan in the journal Neuropsychologia and uh.

1341
01:13:35,160 --> 01:13:37,000
Speaker 1: And what they did is they used f m R

1342
01:13:37,080 --> 01:13:39,920
Speaker 1: I to see if there were any differences in activation

1343
01:13:40,040 --> 01:13:44,960
Speaker 1: patterns in the brain between reporting on knowledge and random guessing.

1344
01:13:45,560 --> 01:13:49,400
Speaker 1: So in one group, researchers would show subjects a playing

1345
01:13:49,400 --> 01:13:52,000
Speaker 1: card on the face side. Here you go, Robert, what

1346
01:13:52,120 --> 01:13:54,640
Speaker 1: card is this? That would be a five clubs? Right.

1347
01:13:54,640 --> 01:13:57,120
Speaker 1: Because I'm showing you the card, you're just reporting. This

1348
01:13:57,200 --> 01:13:59,920
Speaker 1: is working memory in the brain. You're taking in information,

1349
01:14:00,000 --> 01:14:03,160
Speaker 1: you're spitting it back out. Not all that weird. It's

1350
01:14:03,200 --> 01:14:05,080
Speaker 1: a very different thing to hold up the back of

1351
01:14:05,080 --> 01:14:07,240
Speaker 1: a card and say what's the card here? You have

1352
01:14:07,320 --> 01:14:11,000
Speaker 1: no information at all, So you randomly guess six of diamonds,

1353
01:14:11,320 --> 01:14:14,040
Speaker 1: four of diamonds. Kind of close, kind of close. That's

1354
01:14:14,040 --> 01:14:17,760
Speaker 1: like a ballpark. Uh, you're within an order of magnitude.

1355
01:14:17,960 --> 01:14:20,280
Speaker 1: But I think I randomly said six only because I

1356
01:14:20,280 --> 01:14:23,080
Speaker 1: had just said five. Right. But when you're when you're

1357
01:14:23,080 --> 01:14:25,200
Speaker 1: guessing the front of a card, just looking at the back,

1358
01:14:25,240 --> 01:14:28,320
Speaker 1: there's no gunball logic, there's no firmi estimation to help you.

1359
01:14:28,360 --> 01:14:32,080
Speaker 1: It's just random. And yet the authors found that something's

1360
01:14:32,120 --> 01:14:35,599
Speaker 1: going on in the brain when we're generating random guesses.

1361
01:14:35,720 --> 01:14:39,439
Speaker 1: There is activity. Uh, they write, if their analysis is correct,

1362
01:14:39,439 --> 01:14:43,000
Speaker 1: they write, quote, these data suggests that while simple two

1363
01:14:43,120 --> 01:14:46,960
Speaker 1: choice guessing depends on an extensive neural system, including regions

1364
01:14:47,000 --> 01:14:50,600
Speaker 1: of the right lateral prefrontal cortex, activation of orbit of

1365
01:14:50,680 --> 01:14:55,920
Speaker 1: frontal cortex increases as the probabilistic contingencies become more complex,

1366
01:14:56,120 --> 01:14:58,960
Speaker 1: as it becomes harder to understand, you know, what's going on,

1367
01:14:59,520 --> 01:15:02,879
Speaker 1: so they say quote. Guessing thus involves not only systems

1368
01:15:02,920 --> 01:15:08,240
Speaker 1: implicated in working memory processes, but also depends upon orbitofrontal cortex.

1369
01:15:08,640 --> 01:15:12,120
Speaker 1: This region is not typically activated in working memory tasks,

1370
01:15:12,200 --> 01:15:17,160
Speaker 1: and its activation may reflect additional requirements of dealing with uncertainty.

1371
01:15:17,520 --> 01:15:21,040
Speaker 1: Their specific patterns going on in the brain when you're

1372
01:15:21,080 --> 01:15:24,920
Speaker 1: trying to generate random answers, and I just think, like,

1373
01:15:25,000 --> 01:15:28,599
Speaker 1: what's the biological function of that. Where does that come from? Why? Why?

1374
01:15:28,720 --> 01:15:31,360
Speaker 1: Why do animals have this ability with the brain to

1375
01:15:31,520 --> 01:15:35,880
Speaker 1: generate randomness? I don't know that's that. It's a wonderful question. Though.

1376
01:15:36,360 --> 01:15:39,920
Speaker 1: We've been talking a lot about cognitive tools rules of thumb,

1377
01:15:40,080 --> 01:15:42,160
Speaker 1: But there is another way of thinking about people who

1378
01:15:42,200 --> 01:15:45,280
Speaker 1: are good at guessing. As we said, you know, obviously

1379
01:15:45,320 --> 01:15:48,160
Speaker 1: some people are better at guessing and guestimating than others,

1380
01:15:48,520 --> 01:15:51,519
Speaker 1: but obviously not all of them are using these tools.

1381
01:15:51,640 --> 01:15:53,479
Speaker 1: Right when you think about people you know who are

1382
01:15:53,560 --> 01:15:58,200
Speaker 1: very good guessers, they're not necessarily doing firm me calculations,

1383
01:15:58,200 --> 01:16:02,080
Speaker 1: coming up with numbers in their head, uh, exploring boundaries,

1384
01:16:02,160 --> 01:16:05,760
Speaker 1: taking geometric means, and multiplying things together. A lot of

1385
01:16:05,800 --> 01:16:08,639
Speaker 1: times it seems to be just intuitive. So I wonder

1386
01:16:08,680 --> 01:16:12,040
Speaker 1: if there's another way to think about differential skill levels

1387
01:16:12,080 --> 01:16:15,879
Speaker 1: and guessing, and if it's more like finesse at certain

1388
01:16:15,960 --> 01:16:20,320
Speaker 1: sports and athletic activities, meaning that when you think about

1389
01:16:20,320 --> 01:16:24,000
Speaker 1: somebody who's good at hitting shots in basketball, what is

1390
01:16:24,080 --> 01:16:27,759
Speaker 1: that skill? It's obviously not an issue of raw strength.

1391
01:16:27,880 --> 01:16:30,840
Speaker 1: It's not speed, it's not endurance. If somebody can't hit

1392
01:16:31,040 --> 01:16:34,280
Speaker 1: three pointers, it's usually not because they're not strong enough

1393
01:16:34,320 --> 01:16:36,960
Speaker 1: to get the ball to the hoop. When you shoot

1394
01:16:36,960 --> 01:16:39,719
Speaker 1: in basketball, at some level, what you're doing is math.

1395
01:16:39,960 --> 01:16:44,200
Speaker 1: Obviously you're not consciously making calculations, but you're you're trying

1396
01:16:44,240 --> 01:16:49,000
Speaker 1: to calculate and execute a precise arc trajectory, factoring the

1397
01:16:49,080 --> 01:16:53,000
Speaker 1: distance and the distance to the hoop, presence the backboard,

1398
01:16:53,000 --> 01:16:55,240
Speaker 1: the bounciness of the ball. It's kind of like you're

1399
01:16:55,240 --> 01:16:56,920
Speaker 1: playing you do you ever play that old game the

1400
01:16:56,960 --> 01:17:00,559
Speaker 1: gorilla throwing the banana at each other? No, but it's

1401
01:17:00,560 --> 01:17:04,960
Speaker 1: it sounds fun. Yeah, but yes, well it's an old game,

1402
01:17:05,280 --> 01:17:07,759
Speaker 1: like an old basic game. You'd have two guerillas standing

1403
01:17:07,800 --> 01:17:10,800
Speaker 1: on rooftops and you'd enter the angle and the velocity

1404
01:17:10,880 --> 01:17:14,400
Speaker 1: of this bomb banana throw. Yeah. I like though that

1405
01:17:14,439 --> 01:17:16,240
Speaker 1: this is like it's like you're just throw bananas at

1406
01:17:16,280 --> 01:17:19,639
Speaker 1: each other in Virginia Guerrillas. But well, that would involve

1407
01:17:19,720 --> 01:17:22,960
Speaker 1: calculating precise arc trajectories too, I mean, trying to hit

1408
01:17:23,040 --> 01:17:26,559
Speaker 1: something by throwing it. In a sense, you are doing math,

1409
01:17:26,640 --> 01:17:31,120
Speaker 1: even if you're not consciously doing math. Um. So perhaps

1410
01:17:31,120 --> 01:17:33,920
Speaker 1: in some ways I wonder if certain kinds of skill

1411
01:17:34,160 --> 01:17:36,479
Speaker 1: in sports should be thought of as having less to

1412
01:17:36,520 --> 01:17:39,559
Speaker 1: do with the power of the body and being more

1413
01:17:39,600 --> 01:17:42,639
Speaker 1: like an unconscious version of the mind of a highly

1414
01:17:42,720 --> 01:17:46,360
Speaker 1: skilled guesser, like an intuitive for me. And in the

1415
01:17:46,400 --> 01:17:49,760
Speaker 1: same way, I wonder if there's something unconscious in your

1416
01:17:49,800 --> 01:17:54,080
Speaker 1: nervous system that's able to make good guesses about precise

1417
01:17:54,160 --> 01:17:57,920
Speaker 1: angles and velocity to sink a three pointer. Uh, there

1418
01:17:58,000 --> 01:18:00,560
Speaker 1: might be other ways in which we have un conscious

1419
01:18:00,560 --> 01:18:04,679
Speaker 1: intuitions that are nevertheless doing some kind of math. Math

1420
01:18:04,840 --> 01:18:07,960
Speaker 1: is is being calculated in the brain, even if we're

1421
01:18:08,000 --> 01:18:11,479
Speaker 1: not aware of it, in some cases, giving some people

1422
01:18:11,520 --> 01:18:14,920
Speaker 1: better intuitions about guessing than others, even without doing all

1423
01:18:14,960 --> 01:18:18,599
Speaker 1: this math. I don't know, just something to think about.

1424
01:18:19,280 --> 01:18:21,160
Speaker 1: All right. Well, on that note, we're gonna go ahead

1425
01:18:21,200 --> 01:18:24,360
Speaker 1: close out here. Hey, as always, check out stuff to

1426
01:18:24,360 --> 01:18:26,040
Speaker 1: Blow your Mind dot com. That's where you'll find a

1427
01:18:26,120 --> 01:18:28,160
Speaker 1: landing page for this episode, as well as all they

1428
01:18:28,240 --> 01:18:32,080
Speaker 1: passed episodes, videos, blog posts, you name. It also links

1429
01:18:32,080 --> 01:18:34,840
Speaker 1: out to our various social media accounts such as tumbler

1430
01:18:34,880 --> 01:18:38,360
Speaker 1: and Facebook, Instagram and Twitter. And hey, if you are

1431
01:18:38,400 --> 01:18:41,800
Speaker 1: a Twitter user, take advantage of this initiative that we're

1432
01:18:41,800 --> 01:18:44,840
Speaker 1: a part of. Here this a tripod initiative. Simply use

1433
01:18:44,920 --> 01:18:49,160
Speaker 1: the hashtag tripod with a Y and recommend some of

1434
01:18:49,240 --> 01:18:52,200
Speaker 1: your favorite podcasts that you listen to on a regular basis.

1435
01:18:52,280 --> 01:18:54,599
Speaker 1: For me, I'm a big fan of Radio Lab. Of course,

1436
01:18:54,960 --> 01:18:57,840
Speaker 1: I think most of our listeners probably are, so hit on.

1437
01:18:58,080 --> 01:19:01,320
Speaker 1: Go on to Twitter use that hashtag again, hashtag tripod

1438
01:19:01,400 --> 01:19:04,519
Speaker 1: with the Y and recommend some of your favorite shows. Hey,

1439
01:19:04,560 --> 01:19:06,240
Speaker 1: and why don't you email us with some of your

1440
01:19:06,280 --> 01:19:08,160
Speaker 1: favorite podcast Maybe we can try them out and put

1441
01:19:08,200 --> 01:19:10,320
Speaker 1: them on our rotation. Anyway, if you want to get

1442
01:19:10,360 --> 01:19:13,040
Speaker 1: in touch with us about that, or about feedback on

1443
01:19:13,080 --> 01:19:15,479
Speaker 1: this episode or any other, or let us know about

1444
01:19:15,479 --> 01:19:17,639
Speaker 1: what you're thinking future episodes you might want to hear,

1445
01:19:17,880 --> 01:19:20,679
Speaker 1: you can always email us directly at blow the Mind

1446
01:19:20,760 --> 01:19:33,200
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1447
01:19:33,400 --> 01:19:35,920
Speaker 1: and thousands of other topics. Is that how stuff works

1448
01:19:35,920 --> 01:19:59,160
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