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Speaker 1: Hey, everybody, it's your old pal Josh, and for this

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Speaker 1: week's select, I've chosen our twenty fifteen episode on zero population.

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Speaker 1: It's an extremely interesting episode about the upper limits of

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Speaker 1: human population that the Earth can handle, and interestingly, it's

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Speaker 1: also about just how many humans humanity can handle too.

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Speaker 1: When does eating soil and green make sense? Maybe you

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Speaker 1: can decide for yourself in this heady episode. Enjoy. Welcome

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Speaker 1: to Stuff You Should Know, a production of iHeartRadio. Hey,

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Speaker 1: and welcome to the podcast. I'm Josh Clark and there's

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Speaker 1: Charles w. Chuck Bryant's This Stuff you Should Know podcast.

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Speaker 1: Jerry's over there. Uh, it's pretty much the norm yep, yep.

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Speaker 1: How you doing, man? How are you feeling?

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Speaker 2: It is spectaculate, a little rough.

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Speaker 1: Sir, Are you you'll make it through?

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Speaker 2: What? Yeah? Yesterday we righted the uh the beginnings of

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Speaker 2: Gin and Tonic season.

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Speaker 1: It's definitely that kind of weather, for sure.

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Speaker 2: Yeah, it's hard to not sit on the deck and

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Speaker 2: have a citrusy, delightful drink.

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Speaker 1: Nice going.

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Speaker 2: So I'm just a little sleepy, but I'm feeling good.

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Speaker 2: I feel like this topic is h is all about

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Speaker 2: being sort of down in the dumps.

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Speaker 1: A little bit. All it depends. It depends on where

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Speaker 1: you land, and you just place yourself pretty squarely in

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Speaker 1: the gloom and Doom camp.

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Speaker 2: My friend, No, I'm actually not in the doom and

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Speaker 2: Gloom camp.

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Speaker 1: I was about to say, which, if I remember correctly,

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Speaker 1: in our episode, was Malthus right about carrying capacity? Yeah?

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Speaker 1: You overtly said that you are an optimist.

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Speaker 2: That's right, not a Malthusian naysayer.

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Speaker 1: You know.

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Speaker 2: Yeah, I forgot about that one. We've touched on this

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Speaker 2: a few times.

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Speaker 1: Uh huh. We talked about We did a whole profile

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Speaker 1: in Norman Borlog alone on our very short lived and

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Speaker 1: reasonably so live webcast.

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Speaker 2: Oh yeah, do you.

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Speaker 1: Remember we did basically a book report on Borlog.

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Speaker 2: Yeah, he was well, I think he's even controversial.

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Speaker 1: He is very much so.

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Speaker 2: You know, you win a Nobel Prize but.

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Speaker 1: For saving a billion lives.

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Speaker 2: Yeah, but still people are gonna poo poo you yep,

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Speaker 2: you get poop pood interesting stuff.

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Speaker 1: So if you don't know what we're talking about, you

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Speaker 1: should probably press pause, go listen to the Mauthus episode,

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Speaker 1: go to stuff you should know dot com slash podcasts.

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Speaker 1: I think it's plural slash archive. Make that your homepage

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Speaker 1: and all seven hundred and change episodes are there, and

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Speaker 1: then do control f is everybody doing this so far?

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Speaker 1: And then type in mouths M A L T h

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Speaker 1: U S. It's gonna highlight that link, click that and

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Speaker 1: press play and then come back to us. That's right,

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Speaker 1: we'll wait boom. So so we're back.

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Speaker 2: It's been an hour.

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Speaker 1: What we're talking about is carrying capacity in part. But

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Speaker 1: carrying capacity, Chuckers, is just kind of a it's a

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Speaker 1: reflection of a larger issue, and that larger issue is population,

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Speaker 1: specifically overpopulation.

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Speaker 2: Yeah, and is that a thing or not is a

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Speaker 2: big question.

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Speaker 1: Because I mean, at any given point in time, you know,

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Speaker 1: they have, like the CIA World back Book has, you know,

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Speaker 1: a pretty good assessment of how many people are alive.

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Speaker 1: It's a total guess. It's a total estimate. We could

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Speaker 1: be at ten billion right now, we could be at

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Speaker 1: one hundred million, and everybody just is really terrible at counting.

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Speaker 1: The point is, we don't specifically know. It's probably pretty accurate, sure,

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Speaker 1: but it's still a guess. The point isn't to shoot

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Speaker 1: holes in the estimates of how many people are alive,

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Speaker 1: on the planet. It's to point out that, like, there's

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Speaker 1: so many people we don't know and we can't possibly

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Speaker 1: know at any given point in time, and that has

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Speaker 1: led a lot of people to say, well, wait a minute.

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Speaker 1: There's this thing called carrying capacity, which is the Earth's

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Speaker 1: ability to support and sustain us humans and really any creatures.

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Speaker 1: But really, we're just kind of concerned with us humans

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Speaker 1: at this moment and with a quality of life, right

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Speaker 1: and sustainably. Yes, those two factors have to be met

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Speaker 1: or else you're putting a tremendous amount of stress on

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Speaker 1: Earth and you're eventually bringing about your own demise. So

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Speaker 1: a lot of people are saying, like, we're probably past

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Speaker 1: caring capacity and we just don't know it yet, right,

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Speaker 1: or other people are saying, there's really no such thing

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Speaker 1: as caring capacity. Thanks to human ingenuity, anytime we come

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Speaker 1: up against it, we'll figure out a way around it.

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Speaker 1: And Norman Borlog was a way to go. But before

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Speaker 1: Borlog really became famous, there was a lot of people

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Speaker 1: who were legitimately concerned that we were all going to die.

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Speaker 2: Yeah Borlog, if you haven't listened to that one, if

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Speaker 2: you didn't follow Josh's instructions like a good little podcast listener.

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Speaker 2: He was one of the leaders of the Green Revolution

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Speaker 2: in the sixties and seventies, in which we made great

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Speaker 2: advances in agricultural in agriculture, in yields. Yeah, new types

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Speaker 2: of wheat in Mexico, new types of rice in India

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Speaker 2: that yielded much much more than they ever had.

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Speaker 1: And plus they were drought resistant, flood resistant. They could

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Speaker 1: stand up and hold more grain.

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Speaker 2: They could stand up and say hello.

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Speaker 1: They basically they could pick the daily double at high Leaya.

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Speaker 2: So Borlog was, you know, by all standards, a very

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Speaker 2: smart guy. He cared very much about people.

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Speaker 1: He wasn't doing it for fame or riches or anything

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Speaker 1: like that. Like this guy felt like he was working

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Speaker 1: against the clock. And if he didn't and he wasn't

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Speaker 1: the only one doing this, yeah he's the most famous.

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Speaker 1: But if he didn't do it, then, yeah, a lot

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Speaker 1: of people were going to starve.

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Speaker 2: Yeah. And I think I proposed to you before this

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Speaker 2: that we do just one on the Green Revolution. Yeah,

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Speaker 2: and I think that will be a one two three

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Speaker 2: podcast suite. I can't plain this one.

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Speaker 1: I love this stuff. Yeah, Psychology population that was another

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Speaker 1: and what we did too, was how population Works. Yeah,

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Speaker 1: and it sounds so like I bleedingly boring, but it

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Speaker 1: turned out to be really interesting stuff. So go read

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Speaker 1: that too. We'll wait, go ahead, we'll pause.

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Speaker 2: And we're back, and it's nineteen sixty eight.

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Speaker 1: Yeah, and everybody's a little nervous.

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Speaker 2: Everyone is nervous. And Stanford biology professor Paul Erlick. There's

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Speaker 2: another famous Paul Erwick. This is Paul our Airlick. I believe.

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Speaker 1: Oh it's a different one.

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Speaker 2: Well there's two, dude.

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Speaker 1: I did not realize that.

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Speaker 2: What do you mean.

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Speaker 1: I mean, I'm familiar with the other Erlk. Then I guess, well,

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Speaker 1: who was the other one?

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Speaker 2: Again?

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Speaker 1: He wrote some other famous books. He's a biologist. I

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Speaker 1: think it's not the same guy.

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Speaker 2: Yeah. The other guy was a German physician who worked

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Speaker 2: in chemotherapy, immunology.

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Speaker 1: Oh yeah, that's not what I'm thinking of.

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Speaker 2: Yeah, different guy.

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Speaker 1: So this guy he wrote other things besides The Population Bomb.

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Speaker 2: Yeah, so in nineteen sixty eight, he writes the Population Bomb,

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Speaker 2: goes on the Tonight Show, it explodes, this huge hit.

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Speaker 1: Apparently he was on more than once.

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Speaker 2: Yeah, and everyone got super nervous because his book started

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Speaker 2: with these words, the battle de f he edd all

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Speaker 2: of humanity is over.

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Speaker 1: Oh good.

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Speaker 2: In the nineteen seventies, the world will undergo famines. Hundreds

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Speaker 2: of millions of people are going to starve to death

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Speaker 2: in spite of any crash programs embarked upon.

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Speaker 1: Now, oh, that's not so good.

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Speaker 2: That's how he starts his book. He basically says there's

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Speaker 2: going to be a Malthusian collapse. At one point in

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Speaker 2: the book he said, if I was a betting man,

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Speaker 2: I would wager by the year two thousand, England won't

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Speaker 2: be around.

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Speaker 1: Boom. He drops the mic. Yeah, and we should probably

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Speaker 1: mention who mauthusays. Thomas Mauthis was a very forward thinking, smart,

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Speaker 1: mathematically inclined minister, I believe in the early nineteenth century,

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Speaker 1: late eighteenth century. Yeah, an economist, and he was the

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Speaker 1: one who said we have a problem here everyone. I've

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Speaker 1: just done the math. And population grows exponentially, but our

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Speaker 1: foods apply grows linearly, and so we are destined to

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Speaker 1: outgrow our food supply. And that's where the idea of

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Speaker 1: carrying capacity came from. So Malthus and Malthusians are the

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Speaker 1: people who think like we're going to exceed the food

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Speaker 1: supply eventually and die from famines. And ERLK was one

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Speaker 1: of the most vocal and alarmist neo Malthusians around.

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Speaker 2: Yes, absolutely, and he scared the pants off of people.

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Speaker 2: Back then. In nineteen sixty eight there were about three

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Speaker 2: and a half billion people, and the birth rate. We're

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Speaker 2: going to talk a lot about birth rates and such,

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Speaker 2: because there's a lot to do with this.

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Speaker 1: Buckle up.

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Speaker 2: The American women had three and a half babies on average,

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Speaker 2: and the global birth rate was five babies per woman.

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Speaker 2: Seems like a lot to me. It was a lot

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Speaker 2: five kids.

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Speaker 1: Supposedly, in the fifties we were at six the global

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Speaker 1: average fertility it was six babies per woman. And that's

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Speaker 1: not just per woman. That's you want to talk about

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Speaker 1: fertility rates, sure, So, fertility rate basically is the number

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Speaker 1: of live births that a population has assigned to the

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Speaker 1: population of women thought to reasonably be a reproductive age.

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Speaker 1: So fifteen to forty four times a thousand, So you

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Speaker 1: take all of those, figure out the how many women

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Speaker 1: there are, and then you multiply it by a thousand,

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Speaker 1: so you have something like fifty berths per one thousand

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Speaker 1: women age fifteen to forty four and that's your fertility rate. Yeah, okay,

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Speaker 1: that's you can figure out how many actual births are

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Speaker 1: taking place.

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Speaker 2: Yeah, with reasonable detail. Yeah. So like malthus Erlich did

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Speaker 2: the math in the sixties and said, you know what,

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Speaker 2: our food production isn't keeping up, just like Mautha said,

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Speaker 2: we're in big, big trouble, wrote the population Bomb and

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Speaker 2: co founded Zero Population Growth, which is an organization that

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Speaker 2: is now called what.

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Speaker 1: Are they called now, Population Connection?

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Speaker 2: Population Connection very a little sunnier.

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Speaker 1: Sounds electric company.

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Speaker 2: It does, and you should check out their website. It's good.

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Speaker 2: They have a lot of good information on them, just

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Speaker 2: to help you, you know, figure out what you might

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Speaker 2: want to believe. So people are scared the Zero Population

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Speaker 2: Growth group. Their aim is to uh, their big thing

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Speaker 2: is is contraception and giving women control of their reproduction

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Speaker 2: basically and their fertility. Right, that's the you decide how

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Speaker 2: many kids you want exactly they have that many.

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Speaker 1: They've identified that that there's an issue that could easily

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Speaker 1: address over population, and that is cutting out unwanted pregnancies

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Speaker 1: or pregnancies or having unwanted kids. They've identified that, you know,

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Speaker 1: plenty of people there are two different fertility rates. There's

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Speaker 1: the wanted fertility rate and then there's the unwanted fertility rate.

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Speaker 1: Pretty much across the b in any country in the world,

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Speaker 1: the unwanted fertility rate is higher, whether slightly or largely,

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Speaker 1: than the wanted fertility rate. So they're saying, like, if

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Speaker 1: the unwanted fertility rate is like three point eight babies

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Speaker 1: per woman in a given country and the wanted fertility

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Speaker 1: rate is like two point five, well, if we can

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Speaker 1: just figure out a way to only have the wanted pregnancies,

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Speaker 1: then you are doing a lot to control over population.

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Speaker 1: And the way that they figured out how to address

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Speaker 1: this is to just basically spread awareness and access to contraception.

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Speaker 2: Yeah, right, the two pronged approach. What their goal is

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Speaker 2: is they aren't saying that people should not have babies

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Speaker 2: like you said. They're saying people should only have the

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Speaker 2: babies that they want to have exactly, and their ultimate

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Speaker 2: goal is to have a sustainable global birth rate below

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Speaker 2: the replacement level, which means there's a lot of different factors,

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Speaker 2: but it basically means that the world is not growing.

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Speaker 2: When it's like working a club at a door, being

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Speaker 2: a doorman, one person goes out, one person comes in. Yeah,

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Speaker 2: you got a little clicker. Yeah, that's basically what that

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Speaker 2: means is, you know, someone dies, someone can be born, right,

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Speaker 2: and of course it's not that one to one, but

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Speaker 2: you know, well.

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Speaker 1: If you're in a big picture a way, if you're

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Speaker 1: a bouncer and you're tasked with keeping it an even ratio,

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Speaker 1: you just have to remember that you can't keep people

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Speaker 1: inside until a new person comes along, because that's called kidnapping. Yeah,

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Speaker 1: you still they still have to leave and you have

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Speaker 1: to deal with an imbalance for a little while.

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Speaker 2: That's true. Right now. The replacement level of fertility rate

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Speaker 2: in the US is two point one babies for woman

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Speaker 2: and three point zero and other developing countries because they

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Speaker 2: have higher death rates and shorter lifespans, which makes sense.

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Speaker 1: So we were onto the replacement rate basically, right, Yeah.

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Speaker 1: The replacement rate is the number of kids a woman

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Speaker 1: of reproductive age would have to have to replace herself.

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Speaker 1: And she's not just replacing herself, she's replacing herself and

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Speaker 1: her male mate who she's reproducing with. Yes, yeah, and

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Speaker 1: it's kind of gross to think that a woman is

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Speaker 1: giving birth to a boy and a girl who can

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Speaker 1: mate and reproduce her. That's not the point you want

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Speaker 1: them to go mingle with other people's babies. But the

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Speaker 1: replacement rate, you would think then is two, right for

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Speaker 1: every woman two point zero kids is what you need

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Speaker 1: to have to have an even replacement rate. That means

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Speaker 1: it is people die, new people are born, and the

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Speaker 1: population ever grows or declines, it stays the same. The

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Speaker 1: replacement rate is never actually two point zero.

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Speaker 2: Though, those two point one right now.

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Speaker 1: And the reason why is because we humans tend to

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Speaker 1: have more male offspring than female. Apparently for every one

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Speaker 1: hundred girls that are born, one hundred and seven boys

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Speaker 1: are born, So the actual replacement rate is two point

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Speaker 1: zero seven and then they round up to two point one.

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Speaker 2: Yeah. Plus there's I mean, there's a lot of other

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Speaker 2: factors too, for sure. Yeah.

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Speaker 1: Other factors include things like you said, like infant mortality rates, lifespan,

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Speaker 1: immigration into a certain area, And the thing is of

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Speaker 1: birth rates or fertility rates and replacement rates. The replacement

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Speaker 1: rate tends to be a little more stable the birth rate.

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Speaker 1: The fertility rate has a lot more to do with

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Speaker 1: social attitudes, access to healthcare, education, and it can change

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Speaker 1: dramatically from place to place, whereas say, anywhere in the

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Speaker 1: Western world, the developed world, the replacement rates about two

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Speaker 1: point one.

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Speaker 2: Yeh, exactly, that's in the three point zero for the

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Speaker 2: developing countries.

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Speaker 1: All the demographers just stood up and were clapping.

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Speaker 2: So clearly Eric was not correct in his dire predictions.

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Speaker 1: Still well off.

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Speaker 2: Here we are in twenty fifteen and there are problems,

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Speaker 2: but England is still around. That was a bad prediction,

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Speaker 2: four billion people having starved to death. But does that

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Speaker 2: mean that he was wrong altogether? No, not necessarily, because

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Speaker 2: right now, and this was a pretty startling stat to me,

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Speaker 2: over the past one hundred and ten years, we have

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Speaker 2: grown from one point six billion people to seven point

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Speaker 2: two billion people in one hundred and ten years.

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Speaker 1: Well, we're expected to get up to nine point two

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Speaker 1: in another thirty five years by twenty fifty.

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Speaker 2: And so one of the reasons we have this many people,

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Speaker 2: most of the reasons are positive because of like advances

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Speaker 2: in healthcare. The lifespan in nineteen hundred was thirty one

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Speaker 2: years old, and now it's seventy or maybe even a

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Speaker 2: little bit higher, because that was twenty twelve. Yeah, so

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Speaker 2: imagine it is a little bit higher. And the infant

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Speaker 2: mortality rate globally in nineteen hundred was one hundred and

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Speaker 2: sixty five deaths per one thousand live births. In twenty

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Speaker 2: thirteen it was down to thirty four. So that's why

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Speaker 2: there's more peoples, because we're doing better at taking care

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Speaker 2: of ourselves.

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Speaker 1: Yeah, are two huge factors when it comes to demographics

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Speaker 1: and population, because the longer you live, the more old

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Speaker 1: people you have, so therefore, the less babies you need

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Speaker 1: to replace those people, and the fewer babies that die

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Speaker 1: or that survive infancy will be adults one day exactly. Yeah,

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Speaker 1: but these are the really if you're a demographer, the

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Speaker 1: sweet spot is that working age. So when you're a demographer,

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Speaker 1: especially one that's economics minded, Chuck, Yes, that sweet spot

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Speaker 1: the reproductive working age people. That's a good sizable population

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Speaker 1: you want to have. If you have a lot of babies,

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Speaker 1: well then you have a lot of people who are

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Speaker 1: raising those babies, so those babies are dependent on So

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Speaker 1: say you have a lot fewer women in the workforce,

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Speaker 1: so your workforce is depleted. If you have a lot

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Speaker 1: of like an aging population, you have a lot of

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Speaker 1: older people who have already aged out of the workforce

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Speaker 1: and are now dependent on the taxes paid by that workforce.

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Speaker 2: Yeah.

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Speaker 1: So a large population of either babies or old people,

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Speaker 1: and god forbid both at the same time. It puts

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Speaker 1: a lot of strain on the middle. Yeah, you know

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Speaker 1: what I'm saying. Sure, So when you have a longer

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Speaker 1: life expectancy and a lower infant mortality rate like we

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Speaker 1: have now in the developed world, you want to have

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Speaker 1: something closer to the replacement rate.

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Speaker 2: Right, you know, which makes sense?

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Speaker 1: Right.

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Speaker 2: I got some more stats too that would seem to

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Speaker 2: back up Airlick's predictions, or not predictions, but at least

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Speaker 2: his gloomy outlook.

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Speaker 1: He was a gloomy dude currently.

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Speaker 2: You know, I couldn't find much on what he felt today. Yeah,

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Speaker 2: I'm curious he's still around. I'm curious. I bet there's

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Speaker 2: some good interviews. I'm going to check that out. So currently,

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Speaker 2: as of last year, an estimated eight hundred and five

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Speaker 2: million people go to bed Hungary every night, more than

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Speaker 2: half of which are in Asia. One in four people

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Speaker 2: in Sub Saharan Africa was chronically malnourished. One hundred and

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Speaker 2: fifty million people worldwide lack access to clean water, contributing

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Speaker 2: to about eight hundred and fifty thousand deaths per year.

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Speaker 2: And here's the thing, though, is we're living in cities

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Speaker 2: now more than ever. People are moving into cities, which

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Speaker 2: is a good thing in one way because it provides

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Speaker 2: a lot of opportunity, economic opportunity for people, especially in

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Speaker 2: developing countries. But when you look at these cities, a

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Speaker 2: lot of them are full of slums and sweatshops. In

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Speaker 2: these developing nations, something.

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Speaker 1: Like half of the population and a lot of cities

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Speaker 1: live in slum conditions.

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Speaker 2: Yes, without are in Africa sixty one percent.

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Speaker 1: So you think sub Saharan Africa, I think rural in

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Speaker 1: a lot of ways. So yes, I'm aware that they

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Speaker 1: lack access to clean drinking water, and that's an issue

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Speaker 1: that Sub Saharan Africa faces. Yes, you don't think about

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Speaker 1: that being an issue in a city, But the problem

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Speaker 1: with slums is they very rarely have access to clean

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Speaker 1: drinking water in the exact same way that places like

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Speaker 1: rural Africa have the same problem.

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Speaker 2: Yeah, and we're not even I mean, that's that's clean

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Speaker 2: drinking water, and like sanitation and shelter. We're not even

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Speaker 2: talking about education and healthcare and like all the things

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Speaker 2: that people need to live a fruitful life. You know, yep,

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Speaker 2: So cities are a problem. Even if Eric was wrong,

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Speaker 2: there are clearly issues. Some people will argue, and we'll

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Speaker 2: get to the critics and stuff later, but a lot

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Speaker 2: of people argue that it's distribution of food and stuff

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Speaker 2: like that, Like, we have the resources, we're just not

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Speaker 2: dividing it out properly.

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Speaker 1: Right. And apparently, if I read that, if everyone lived

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Speaker 1: like an American and consumed like an American does, yeah,

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Speaker 1: the caring capacity would be something like two billion, So

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Speaker 1: we would have already far exceeded it. Sure, but if

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Speaker 1: everybody lived with just the minimal amount that they need

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Speaker 1: to live, the carring capacity would be something like forty billion.

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Speaker 1: We've been able to sustain the car as it is

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Speaker 1: right now because not everybody lives like an American. But

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Speaker 1: if you're an American, that means that a lot of

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Speaker 1: the other world, especially developing world, thinks that you are

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Speaker 1: over consuming by a lot. And that's really evident in

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Speaker 1: there's a graph that went around recently that shows water

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Speaker 1: use in agriculture by type of product, so everything from

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Speaker 1: like soy to beef. It showed how much water, oh yeah,

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Speaker 1: did you see that.

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Speaker 2: I didn't see that, but I've seen stuff like that,

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Speaker 2: because beef is like a huge consumer of water.

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Speaker 1: Right, one hundred and six point two eight gallons of

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Speaker 1: water used to produce one ounce of beef. That's a lot.

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Speaker 1: That's a lot of water. And so that's that's part

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Speaker 1: of the point. Whereas if everybody's and apparently in China

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Speaker 1: and India and these ascending countries with ascending economies, one

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Speaker 1: of the great benefits of being part of the developed

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Speaker 1: world is you can get steak anytime you want, baby, Yeah,

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Speaker 1: and I want a big one right now, put it

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Speaker 1: in front of me. I'll give you some money here. Here,

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Speaker 1: just take this and put in your pocket. There's some

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Speaker 1: money for you. Give me my stake, and you don't

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Speaker 1: care how much water it took. Yeah, And it's there

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Speaker 1: these people who are saying they don't necessarily agree with Aeric,

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Speaker 1: but they're saying he wasn't totally.

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Speaker 2: Off right, he was alarmed as clearly they're a problems.

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Speaker 1: They're saying, this is one of the problems, right, you know,

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Speaker 1: this is one of the problems with too many people.

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Speaker 2: Yeah, and so getting back to contraception and zero population

405
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Speaker 2: growth are now the population connection their big goal. They

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Speaker 2: say there are two hundred and twenty two million women

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Speaker 2: in the developing world who have an unmet need for

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Speaker 2: family planning. So they're not saying, you know, we want

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Speaker 2: to put our ideals on you and you shouldn't be

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Speaker 2: having kids. They're saying they are that many women that

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Speaker 2: are like, I don't want these five kids I would

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Speaker 2: have wanted to, and I either don't know about contraception,

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Speaker 2: don't have contraception, or I have literally no idea how

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Speaker 2: conception works.

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Speaker 1: Right. Sadly for a lot of them, say a lot

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Speaker 1: the first idea that women just need access to contraception

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Speaker 1: and they will use it. Yeah, and they're they're they're

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Speaker 1: working on that, right, sure, but they've found in studies

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Speaker 1: it's something like ten percent or less of the women

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Speaker 1: who are defined as having unmet contraceptive needs cite a

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Speaker 1: lack of access as to why they're having unwanted kids. Instead,

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Speaker 1: they're saying it's things like family pressure or societal pressure

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Speaker 1: to have a bunch of kids. Like you're saying, like

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Speaker 1: not understanding contraception or how conception works.

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Speaker 2: Yeah, they say they don't believe that they need contraception.

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Speaker 2: If you have sex infrequently or after birth, after I've

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Speaker 2: had one kid, we don't need to use contraception anymore,

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Speaker 2: like literally not knowing how conception works. Right, So that's

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Speaker 2: a big educational hurdle that Population Connection is trying to overcome.

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Speaker 1: Right, So they're saying it's not just getting contraception to women,

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Speaker 1: it's educating them on how to use it and changing

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Speaker 1: their social outlook.

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Speaker 2: Yeah, changing the culture. Yeah, largely men, you know, saying

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Speaker 2: one more babies, right, you know, like revolutionary road or something,

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Speaker 2: you know. All Right, so we're going to talk a

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Speaker 2: little bit after the break about what the critics of

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Speaker 2: zero population growth have to say.

438
00:23:42,040 --> 00:23:46,960
Speaker 1: So we're back. Yes, we're talking about solutions to overpopulation,

439
00:23:47,040 --> 00:23:49,680
Speaker 1: but not everybody thinks it's a problem. Yeah, some people

440
00:23:49,720 --> 00:23:52,680
Speaker 1: say over population is a myth. Yeah, they say that.

441
00:23:52,800 --> 00:23:58,320
Speaker 1: Eric in and of it himself damaged his own argument.

442
00:23:58,600 --> 00:24:00,000
Speaker 2: Yeah, he got a lot of personal heat.

443
00:24:00,240 --> 00:24:03,159
Speaker 1: Yeah, still does because of the language he used. It

444
00:24:03,240 --> 00:24:06,040
Speaker 1: was so alarmist, starting his book off with you know

445
00:24:06,080 --> 00:24:07,960
Speaker 1: that we've already lost and no matter what we do,

446
00:24:08,359 --> 00:24:11,359
Speaker 1: billions of people are going to die. And then it

447
00:24:11,440 --> 00:24:14,280
Speaker 1: not panning out, saying that England wasn't going to be

448
00:24:14,320 --> 00:24:17,240
Speaker 1: around in thirty years. I mean, that was putting a

449
00:24:17,280 --> 00:24:21,119
Speaker 1: lot on the line, and so a lot of people said,

450
00:24:20,280 --> 00:24:28,000
Speaker 1: your specific landmarks or milestones were unmet. Therefore your whole

451
00:24:28,080 --> 00:24:31,920
Speaker 1: arguments out the window. And some people believe that other

452
00:24:31,920 --> 00:24:35,840
Speaker 1: people are like, that's not necessarily true. That is alarmist

453
00:24:35,920 --> 00:24:39,600
Speaker 1: as well, possibly your reaction area at least. But some

454
00:24:39,640 --> 00:24:43,159
Speaker 1: people say I still don't agree with erlik because humans

455
00:24:43,200 --> 00:24:46,040
Speaker 1: are smart. We can figure our way out of any problem.

456
00:24:46,280 --> 00:24:49,960
Speaker 2: That's right. Critics will say that humans are not parasites

457
00:24:50,000 --> 00:24:53,159
Speaker 2: of the earth. We are the saviors of Earth, and

458
00:24:53,240 --> 00:24:55,000
Speaker 2: we are the ones that are coming up with these

459
00:24:55,000 --> 00:24:59,919
Speaker 2: solutions like the Green Revolution and longer lifespans and progress

460
00:25:00,000 --> 00:25:02,080
Speaker 2: sing medically to help people live longer.

461
00:25:02,200 --> 00:25:05,640
Speaker 1: I don't know about saviors of Earth. I think that's

462
00:25:05,640 --> 00:25:08,600
Speaker 1: stretching it a little bit. I think we abstract a

463
00:25:08,600 --> 00:25:10,880
Speaker 1: little too much to be called saviors of Earth.

464
00:25:11,040 --> 00:25:12,960
Speaker 2: Well, I guarantee you there's a lot of people that

465
00:25:13,000 --> 00:25:15,159
Speaker 2: think humans are saviors of Earth. Sure, you know.

466
00:25:15,400 --> 00:25:17,840
Speaker 1: I would see us more as like Homer with Pinchy

467
00:25:17,880 --> 00:25:20,359
Speaker 1: the lobster again in the salt water and fresh water,

468
00:25:20,800 --> 00:25:24,119
Speaker 1: trying to strike the balance I wouldn't call him a

469
00:25:24,160 --> 00:25:26,919
Speaker 1: savior of either the Goldfish or Pinchy at that moment.

470
00:25:27,000 --> 00:25:30,080
Speaker 1: He's just keeping them both in stasis.

471
00:25:30,080 --> 00:25:32,000
Speaker 2: How many times would you reference Pinchy the lot that's

472
00:25:32,000 --> 00:25:34,720
Speaker 2: probably seven seven. Yeah, it's not bad. It's one for

473
00:25:34,760 --> 00:25:39,080
Speaker 2: every one hundred shows roughly. Other critics will say that

474
00:25:39,560 --> 00:25:42,240
Speaker 2: low birth rates are no good for the economy, like

475
00:25:42,280 --> 00:25:46,719
Speaker 2: you were talking about earlier, older people and babies. Well,

476
00:25:46,760 --> 00:25:48,680
Speaker 2: I guess low birth rates wouldn't affect that, but older

477
00:25:48,720 --> 00:25:52,400
Speaker 2: people are more of a tax on society than they

478
00:25:52,400 --> 00:25:54,720
Speaker 2: are spinders right and investors right.

479
00:25:54,800 --> 00:25:57,400
Speaker 1: But in the same way, if you have too many babies,

480
00:25:57,440 --> 00:26:00,439
Speaker 1: that's a big tax. Eventually that those babies will be

481
00:26:00,480 --> 00:26:01,199
Speaker 1: a workforce.

482
00:26:01,400 --> 00:26:03,240
Speaker 2: Yeah, like we spend money exactly.

483
00:26:03,320 --> 00:26:09,679
Speaker 1: So the baby boom and the post war boom economic

484
00:26:09,720 --> 00:26:12,719
Speaker 1: boom in the United States, it's not coincidental that they

485
00:26:12,760 --> 00:26:14,720
Speaker 1: went hand in hand. There are a bunch of people

486
00:26:14,720 --> 00:26:18,640
Speaker 1: having babies and eventually they grew into the workforce and

487
00:26:18,760 --> 00:26:21,359
Speaker 1: they made a bunch of money in the eighties for

488
00:26:21,440 --> 00:26:22,360
Speaker 1: the United States.

489
00:26:22,680 --> 00:26:26,360
Speaker 2: Yeah, and it's also supported in developing countries. More than

490
00:26:26,400 --> 00:26:30,160
Speaker 2: seventy countries are categorized now as low fertility with two

491
00:26:30,240 --> 00:26:35,120
Speaker 2: babies or less per woman, and those areas are expected

492
00:26:35,160 --> 00:26:38,520
Speaker 2: to make big economic gains in the coming decades because

493
00:26:38,920 --> 00:26:42,280
Speaker 2: they're going to be people to spend money right and

494
00:26:42,320 --> 00:26:43,040
Speaker 2: be in the workforce.

495
00:26:43,240 --> 00:26:48,200
Speaker 1: And there's kind of a few ways that the workforce

496
00:26:48,359 --> 00:26:52,479
Speaker 1: and wealth in the economy and birth rates are all

497
00:26:52,560 --> 00:26:55,400
Speaker 1: kind of tied together too. It turns out that if

498
00:26:55,440 --> 00:27:01,160
Speaker 1: you give a woman rights to her own contraceptive decisions,

499
00:27:01,240 --> 00:27:05,160
Speaker 1: ye sure, the birth rate tends to inevitably fall as

500
00:27:05,200 --> 00:27:09,000
Speaker 1: a result. And then when that happens, it happens because

501
00:27:09,080 --> 00:27:11,760
Speaker 1: some women have more babies than they want to when

502
00:27:11,800 --> 00:27:16,000
Speaker 1: they don't have right to their own contraceptive decisions. Another

503
00:27:16,040 --> 00:27:19,320
Speaker 1: reason is when they have those kind of rights, they

504
00:27:19,400 --> 00:27:22,040
Speaker 1: usually also have the right to an education. When they

505
00:27:22,320 --> 00:27:25,600
Speaker 1: enter school, they will tend to put off having kids

506
00:27:26,040 --> 00:27:29,000
Speaker 1: because once they graduate from school, they'll usually enter the workforce,

507
00:27:29,359 --> 00:27:31,600
Speaker 1: and so just by nature of getting to the whole

508
00:27:31,640 --> 00:27:34,520
Speaker 1: thing later on in life, they're having fewer kids as well.

509
00:27:35,520 --> 00:27:38,639
Speaker 1: And when you have more educated women in the workforce,

510
00:27:38,680 --> 00:27:42,520
Speaker 1: your economy is stronger too, So directly and by proxy,

511
00:27:43,760 --> 00:27:47,159
Speaker 1: lower birth rates are associated with the stronger economy. But again,

512
00:27:47,480 --> 00:27:49,439
Speaker 1: you don't want to get too low, because if you

513
00:27:49,480 --> 00:27:51,960
Speaker 1: get too low, then all of a sudden, the generation

514
00:27:52,359 --> 00:27:55,399
Speaker 1: before it started to taper off is going to be

515
00:27:55,440 --> 00:27:58,920
Speaker 1: bigger than the generation that's working. And if it costs

516
00:27:59,040 --> 00:28:02,399
Speaker 1: fifty thousand dollars in tax money to keep the average

517
00:28:02,400 --> 00:28:07,120
Speaker 1: retiree afloat, say in the United States, well that divided

518
00:28:07,119 --> 00:28:10,120
Speaker 1: by a thousand people is a lot easier to bear

519
00:28:10,359 --> 00:28:13,600
Speaker 1: than divided by one hundred people one hundred working people,

520
00:28:13,920 --> 00:28:14,480
Speaker 1: you know what I mean.

521
00:28:14,560 --> 00:28:16,800
Speaker 2: Yeah, we got to keep the old folks and keep

522
00:28:16,800 --> 00:28:18,719
Speaker 2: them in stake and ovaltine right. You know.

523
00:28:19,000 --> 00:28:24,000
Speaker 1: So if you're an economist, a demographer, whatever, everybody's kind

524
00:28:24,000 --> 00:28:26,800
Speaker 1: of saying like, you want to get a country developed,

525
00:28:27,040 --> 00:28:28,480
Speaker 1: and you want to get them at that two point

526
00:28:28,560 --> 00:28:31,320
Speaker 1: one replacement rate, and everything will be hunky dory from there.

527
00:28:31,720 --> 00:28:34,360
Speaker 2: Yeah. And the other thing a critic might say, too,

528
00:28:34,480 --> 00:28:36,720
Speaker 2: is and this is what we were talking about earlier

529
00:28:36,760 --> 00:28:40,400
Speaker 2: about the environment, the impact on the environment, like we're

530
00:28:40,400 --> 00:28:42,280
Speaker 2: just going to destroy our world with so many people.

531
00:28:44,000 --> 00:28:48,680
Speaker 2: It turns out that impact carbon emissions aren't really tied

532
00:28:48,720 --> 00:28:52,760
Speaker 2: to population growth rates. It's tied to per capita income levels.

533
00:28:53,320 --> 00:28:55,880
Speaker 2: By evidence that China and the US have some of

534
00:28:55,880 --> 00:28:59,760
Speaker 2: the lowest fertility rates right now, and we are the worst.

535
00:29:01,200 --> 00:29:03,320
Speaker 2: So it's not because we have all these people, right

536
00:29:03,400 --> 00:29:08,520
Speaker 2: it's because we're consuming too much as Americans exactly, and

537
00:29:08,560 --> 00:29:09,880
Speaker 2: I guess in China as well.

538
00:29:10,400 --> 00:29:14,360
Speaker 1: Which actually makes it seem kind of nerve racking that sure,

539
00:29:14,640 --> 00:29:18,760
Speaker 1: India and China with these enormous populations are starting to

540
00:29:18,800 --> 00:29:22,880
Speaker 1: become wealthier and wealthier, because that's just going to make

541
00:29:22,920 --> 00:29:25,240
Speaker 1: it even worse as far as the environment goes.

542
00:29:25,960 --> 00:29:28,080
Speaker 2: Did you check out the Population Connection site?

543
00:29:28,920 --> 00:29:29,440
Speaker 1: No? I didn't.

544
00:29:29,600 --> 00:29:32,920
Speaker 2: They have a pretty interesting faq that if you don't

545
00:29:32,960 --> 00:29:35,200
Speaker 2: know where you stand, I mean, it's helpful to read.

546
00:29:36,000 --> 00:29:39,040
Speaker 2: Like they say things like, instead of we want to

547
00:29:39,080 --> 00:29:42,320
Speaker 2: focus on quality of life, not quantity, and instead of

548
00:29:42,360 --> 00:29:45,280
Speaker 2: saying how many people can the earth support, maybe how

549
00:29:45,280 --> 00:29:48,840
Speaker 2: many people can't orth support? Because right now, all these

550
00:29:49,000 --> 00:29:52,360
Speaker 2: people are dying from lack of you know, clean water

551
00:29:52,520 --> 00:29:57,240
Speaker 2: and sanitation and food. And there's the counter argument that

552
00:29:57,240 --> 00:29:59,120
Speaker 2: you hear from critics a lot. I've seen a stat

553
00:30:00,080 --> 00:30:04,560
Speaker 2: run around that the entire world's population could live in Texas, Texas.

554
00:30:04,760 --> 00:30:07,880
Speaker 1: It's so mind boggling. I have trouble like believing it. Well,

555
00:30:08,000 --> 00:30:09,960
Speaker 1: I think somebody forgot to carry a one or something.

556
00:30:10,080 --> 00:30:15,800
Speaker 2: No, it's true. Population Connection says, sure they can. You

557
00:30:15,840 --> 00:30:18,240
Speaker 2: could fit everyone in Texas. You could also fit forty

558
00:30:18,240 --> 00:30:21,960
Speaker 2: people in a phone booth. Yeah, but Texas, they said,

559
00:30:22,040 --> 00:30:25,160
Speaker 2: in no way has the carrying capacity to take care

560
00:30:25,200 --> 00:30:27,280
Speaker 2: of those people. So it's a little bit of a

561
00:30:27,320 --> 00:30:30,080
Speaker 2: hollow you know, fact that you throw out when you

562
00:30:30,120 --> 00:30:32,600
Speaker 2: say that, right, Like, sure you can jam everyone in there.

563
00:30:34,360 --> 00:30:35,360
Speaker 2: Texas would be like.

564
00:30:35,640 --> 00:30:38,120
Speaker 1: What are you guys doing here?

565
00:30:38,320 --> 00:30:42,000
Speaker 2: Yeah, exactly, But it's pretty interesting stuff. I recommend people

566
00:30:42,040 --> 00:30:44,960
Speaker 2: read their FAQ. It seems like they definitely have the

567
00:30:45,040 --> 00:30:50,680
Speaker 2: right mindset because what they want to do is, you know,

568
00:30:50,760 --> 00:30:52,360
Speaker 2: make sure people have a good quality of life all

569
00:30:52,400 --> 00:30:52,960
Speaker 2: over the world.

570
00:30:53,040 --> 00:30:55,520
Speaker 1: Well, I will go read their FAQ because I suddenly

571
00:30:55,520 --> 00:30:59,160
Speaker 1: feel underprepared. But I will tell you that the impression

572
00:30:59,200 --> 00:31:01,760
Speaker 1: that I have from research them without going on their

573
00:31:01,800 --> 00:31:07,440
Speaker 1: website was I didn't find anything like beware population Connection

574
00:31:07,800 --> 00:31:11,280
Speaker 1: or the population Connection myth or anything like that. There's

575
00:31:11,280 --> 00:31:15,720
Speaker 1: definitely debate on the other side saying overpopulation is a myth,

576
00:31:15,760 --> 00:31:18,280
Speaker 1: but no one seems to be attacking Population Connection as

577
00:31:18,320 --> 00:31:19,760
Speaker 1: like a nefarious organization.

578
00:31:19,840 --> 00:31:22,080
Speaker 2: Yeah, because they're not saying don't have babies.

579
00:31:21,880 --> 00:31:24,520
Speaker 1: Right, And that's a really sticky situation to be in

580
00:31:24,600 --> 00:31:27,080
Speaker 1: because a lot of people are like, well, God wants

581
00:31:27,120 --> 00:31:29,240
Speaker 1: us to have as many babies as we possibly can.

582
00:31:29,600 --> 00:31:31,760
Speaker 1: Who are you to be meddling in that kind of thing.

583
00:31:32,640 --> 00:31:34,840
Speaker 1: It's a fine line that a group like that has

584
00:31:34,880 --> 00:31:38,040
Speaker 1: to walk, and they seem to be walking at fine. Yeah,

585
00:31:38,160 --> 00:31:41,840
Speaker 1: they're just saying like, here's some contraception. Maybe let's not

586
00:31:41,920 --> 00:31:45,120
Speaker 1: have unwanted babies. Let those little angels stay in heaven

587
00:31:46,320 --> 00:31:48,520
Speaker 1: and we'll just go from there.

588
00:31:48,680 --> 00:32:20,520
Speaker 2: Yeah. I think that's their homepage, all right, the Behavioral Sink.

589
00:32:20,960 --> 00:32:22,719
Speaker 2: What where did you find this?

590
00:32:23,800 --> 00:32:27,160
Speaker 1: I don't remember where I ran across it, but I'd

591
00:32:27,200 --> 00:32:28,800
Speaker 1: read it a while back. But I have to give

592
00:32:28,840 --> 00:32:31,680
Speaker 1: a shout out to Josh from Jersey, the original Jersey,

593
00:32:31,720 --> 00:32:34,760
Speaker 1: not New Jersey, who recently wrote in to suggest we

594
00:32:35,120 --> 00:32:37,560
Speaker 1: do an episode on that, and had perfect timing because

595
00:32:37,600 --> 00:32:40,520
Speaker 1: he wrote in after you'd selected this one, oh yeah,

596
00:32:40,560 --> 00:32:43,120
Speaker 1: and I was like, these two would go great together,

597
00:32:43,240 --> 00:32:46,080
Speaker 1: hand in hand. Yeah, So thanks Josh for reminding us.

598
00:32:46,080 --> 00:32:50,120
Speaker 2: Well, thank you Josh for thanking Josh, which Josh, I'm

599
00:32:50,120 --> 00:32:53,080
Speaker 2: thinking all the Josh's Okay. So in nineteen seventy two,

600
00:32:53,160 --> 00:32:57,280
Speaker 2: this dude named John B. Calhoun. This is one of

601
00:32:57,280 --> 00:32:59,720
Speaker 2: his experiments. This guy, what he liked to do was

602
00:32:59,720 --> 00:33:03,600
Speaker 2: bill rat and mouse utopias.

603
00:33:03,800 --> 00:33:05,320
Speaker 1: You've been doing it since the forties.

604
00:33:05,400 --> 00:33:08,520
Speaker 2: Yeah, And basically with the aim to see what would

605
00:33:08,560 --> 00:33:11,840
Speaker 2: happen to a population, in this case mice or rats

606
00:33:12,480 --> 00:33:15,400
Speaker 2: if you gave them a perfect mouse world.

607
00:33:15,680 --> 00:33:17,680
Speaker 1: Right, And he called these world universes.

608
00:33:17,920 --> 00:33:18,480
Speaker 2: Yeah.

609
00:33:18,520 --> 00:33:20,560
Speaker 1: And the one in nineteen seventy two, the one that

610
00:33:20,600 --> 00:33:24,800
Speaker 1: really like made all the headlines, I guess, was called

611
00:33:24,880 --> 00:33:27,640
Speaker 1: Universe twenty five. So he had twenty four under his

612
00:33:27,680 --> 00:33:30,400
Speaker 1: bell already, and it was pretty good size. It was

613
00:33:30,600 --> 00:33:34,320
Speaker 1: one hundred over one hundred inches square, The walls were

614
00:33:34,320 --> 00:33:39,760
Speaker 1: fifty four inches high. It had space for let's see,

615
00:33:40,040 --> 00:33:43,080
Speaker 1: what's two hundred and fifty six times fifteen, chuck.

616
00:33:44,080 --> 00:33:47,800
Speaker 2: I'm gonna go with about in my head. I'm gonna say,

617
00:33:47,800 --> 00:33:49,360
Speaker 2: like close to thirty thousand.

618
00:33:50,640 --> 00:33:52,959
Speaker 1: It is exactly thirty eight hundred.

619
00:33:53,480 --> 00:33:54,320
Speaker 2: Yeah, that's what I meant.

620
00:33:54,400 --> 00:33:58,200
Speaker 1: I meant three thousand, thirty eight hundred and forty okay, okay,

621
00:33:59,080 --> 00:34:01,840
Speaker 1: So there was enough room comfortably for thirty eight hundred

622
00:34:01,880 --> 00:34:07,200
Speaker 1: and forty mice. Yes, And long before that he introduced

623
00:34:07,360 --> 00:34:11,800
Speaker 1: four breeding pairs, so eight mice he first is introduced

624
00:34:11,840 --> 00:34:12,919
Speaker 1: to Universe twenty five.

625
00:34:13,040 --> 00:34:14,520
Speaker 2: Yeah, and it was well stocked by the way.

626
00:34:14,600 --> 00:34:16,160
Speaker 1: They had everything they want, food.

627
00:34:15,960 --> 00:34:18,680
Speaker 2: Water that was cleaned out. They were all disease free,

628
00:34:18,760 --> 00:34:21,040
Speaker 2: no predators, yeah, no, yeah. He threw a cat in

629
00:34:21,080 --> 00:34:21,520
Speaker 2: there once.

630
00:34:21,760 --> 00:34:23,600
Speaker 1: Right, just to keep them on their toes or something.

631
00:34:23,800 --> 00:34:25,840
Speaker 2: Yeah, I mean it was it was mouse heaven is

632
00:34:25,880 --> 00:34:26,520
Speaker 2: what they called it.

633
00:34:26,640 --> 00:34:29,600
Speaker 1: Yes, and he actually did in papers about these universes.

634
00:34:29,680 --> 00:34:31,920
Speaker 1: He would refer to them as heaven or utopia, and

635
00:34:32,000 --> 00:34:33,239
Speaker 1: he would use words like that.

636
00:34:33,400 --> 00:34:34,520
Speaker 2: Yeah.

637
00:34:34,560 --> 00:34:37,400
Speaker 1: So he introduces these four breeding pairs of mice to

638
00:34:38,400 --> 00:34:42,960
Speaker 1: Universe twenty five, and after one hundred and four days,

639
00:34:43,040 --> 00:34:45,920
Speaker 1: it took them to finally settle down and be like, Okay,

640
00:34:45,960 --> 00:34:48,160
Speaker 1: this place is actually pretty great. It's not too good

641
00:34:48,200 --> 00:34:51,200
Speaker 1: to be true, despite the fact that it seems to

642
00:34:51,200 --> 00:34:54,640
Speaker 1: be built by human hand, which is weird, and the

643
00:34:54,680 --> 00:34:59,080
Speaker 1: temperature never changes, but we're just gonna say it's probably fine, right,

644
00:34:59,200 --> 00:35:03,040
Speaker 1: and start breeding. And they started breeding pretty quickly. Oh yes,

645
00:35:03,320 --> 00:35:06,600
Speaker 1: they started doubling in population every fifty five days after.

646
00:35:06,320 --> 00:35:09,239
Speaker 2: That, right, Yeah, like you said, because it was so

647
00:35:09,400 --> 00:35:12,520
Speaker 2: great there, they were just like Hey, let's eat and

648
00:35:12,920 --> 00:35:16,279
Speaker 2: do it and make little baby mice. Like you know,

649
00:35:16,320 --> 00:35:19,000
Speaker 2: there is no end in sight, so you're doubling every

650
00:35:19,040 --> 00:35:21,960
Speaker 2: fifty five days. This was all a big study to

651
00:35:22,000 --> 00:35:25,680
Speaker 2: study what overpopulation, what would happen. And what he found

652
00:35:25,680 --> 00:35:29,560
Speaker 2: time after time was that things went bad.

653
00:35:30,320 --> 00:35:34,480
Speaker 1: Yeah, which is really something because remember Paul Erlick released

654
00:35:34,480 --> 00:35:38,440
Speaker 1: the population bomb in nineteen sixty eight, but for decades

655
00:35:38,480 --> 00:35:44,839
Speaker 1: before that, John Calhoun saw firsthand what the real problem was.

656
00:35:45,080 --> 00:35:50,400
Speaker 1: The real problem wasn't overpopulation leading to scarcity of food

657
00:35:50,480 --> 00:35:55,640
Speaker 1: and conflicts, conflict and resource wars and famine starvation. What

658
00:35:55,800 --> 00:35:59,440
Speaker 1: he found was that the real problem was overpopulation itself.

659
00:35:59,680 --> 00:36:03,680
Speaker 2: Yeah, but just too many, too many mice and not

660
00:36:03,880 --> 00:36:06,960
Speaker 2: enough valuable roles for mice to play exactly.

661
00:36:07,400 --> 00:36:09,919
Speaker 1: So there comes to be a point in any mouse

662
00:36:09,960 --> 00:36:13,239
Speaker 1: population as far as Calhoun was concerned. And again this

663
00:36:13,280 --> 00:36:15,799
Speaker 1: is Universe twenty five, and he wasn't making like one

664
00:36:15,840 --> 00:36:18,799
Speaker 1: a week or something. These were detailed, smart studies. He

665
00:36:18,880 --> 00:36:21,000
Speaker 1: was hired by the National Institutes of Health. He spent

666
00:36:21,040 --> 00:36:23,040
Speaker 1: like twenty or thirty years working there. He's like a

667
00:36:23,040 --> 00:36:29,080
Speaker 1: bona fide legitimate researcher, and he would find that at

668
00:36:29,120 --> 00:36:34,200
Speaker 1: some point the abundance would lead to overpopulation rather than

669
00:36:34,239 --> 00:36:37,480
Speaker 1: scarcely like he never ran out of food. They always

670
00:36:37,520 --> 00:36:39,680
Speaker 1: had enough food and water and everything. What came to

671
00:36:39,719 --> 00:36:42,680
Speaker 1: be an issue was space and social interactions. There were

672
00:36:42,719 --> 00:36:45,360
Speaker 1: just too many people. There are too many mice, I

673
00:36:45,360 --> 00:36:49,160
Speaker 1: should say to the mice. There people, sure, and they're

674
00:36:49,440 --> 00:36:53,680
Speaker 1: rubbing shoulders up against one another, constantly moving past one another.

675
00:36:53,760 --> 00:36:57,880
Speaker 1: There's not enough room. And like you said, there wasn't enough.

676
00:36:59,040 --> 00:37:03,440
Speaker 1: There were too many mice to fulfill the number of

677
00:37:03,520 --> 00:37:06,279
Speaker 1: social roles needed. Right.

678
00:37:06,640 --> 00:37:08,759
Speaker 2: Yeah, it says by day three point fifteen, so this

679
00:37:08,800 --> 00:37:12,080
Speaker 2: is close to a year, a lot of mice are

680
00:37:12,120 --> 00:37:14,320
Speaker 2: living in there. And they said there were more peers

681
00:37:15,040 --> 00:37:19,640
Speaker 2: to defend against, so males were stressed out and stopped

682
00:37:19,640 --> 00:37:23,240
Speaker 2: defending their territory. Yeah, they abandon it. It said normal

683
00:37:23,280 --> 00:37:29,920
Speaker 2: social discourse broke down completely, Social bonds broke down. There

684
00:37:30,000 --> 00:37:33,680
Speaker 2: was like randomized violence for no reason. It seemed like

685
00:37:35,000 --> 00:37:38,360
Speaker 2: the female mice, the mothers saw this and would attack

686
00:37:38,440 --> 00:37:43,600
Speaker 2: their own babies, and it was procreation slumped, infant abandonment

687
00:37:43,680 --> 00:37:47,960
Speaker 2: increased mortality. Sword Then he talked about the beautiful ones,

688
00:37:48,000 --> 00:37:50,760
Speaker 2: which I thought was hysterical. There were these male mice

689
00:37:50,800 --> 00:37:54,080
Speaker 2: that just they never fought, They never sought to reproduce

690
00:37:54,200 --> 00:37:56,440
Speaker 2: or have sex. All they did was eat, sleep, and

691
00:37:56,480 --> 00:37:59,080
Speaker 2: groom and just sort of loaf around.

692
00:37:59,280 --> 00:37:59,520
Speaker 1: Yeah.

693
00:38:00,000 --> 00:38:03,080
Speaker 2: All these social barriers are completely being destroyed, right, these

694
00:38:03,120 --> 00:38:04,359
Speaker 2: social norms, I should say.

695
00:38:04,480 --> 00:38:07,839
Speaker 1: Yeah, and these the females that could reproduce went off

696
00:38:07,880 --> 00:38:11,520
Speaker 1: by themselves, sequestered themselves away from society, and the males

697
00:38:11,560 --> 00:38:14,560
Speaker 1: that were capable of reproducing became those beautiful ones yeah,

698
00:38:14,600 --> 00:38:18,280
Speaker 1: and didn't seek sex either. So over time they lost

699
00:38:18,320 --> 00:38:21,880
Speaker 1: their ability to carry out these complex social interactions that

700
00:38:22,000 --> 00:38:25,640
Speaker 1: lead to reproduction, and they just stopped reproducing it in general.

701
00:38:26,040 --> 00:38:29,040
Speaker 2: Yeah. By day five sixty, and this is I guess

702
00:38:29,120 --> 00:38:33,319
Speaker 2: that's the close to two year mark. Well, I guess

703
00:38:33,320 --> 00:38:36,880
Speaker 2: eighteen months they had twenty two hundred mice and then

704
00:38:37,120 --> 00:38:38,440
Speaker 2: growth ceased.

705
00:38:38,239 --> 00:38:40,720
Speaker 1: Yeah, which isn't even close to the thirty eight hundred

706
00:38:40,719 --> 00:38:43,759
Speaker 1: and forty that this place could could conceivably hang on to.

707
00:38:44,120 --> 00:38:46,520
Speaker 2: Yeah, So it was how many, was it, thirty eight hundred,

708
00:38:46,640 --> 00:38:48,640
Speaker 2: thirty eight to forty? Yeah, so at twenty two hundred

709
00:38:48,640 --> 00:38:52,960
Speaker 2: they stopped reproducing. Very few mice survive pass weaning. At

710
00:38:52,960 --> 00:38:58,280
Speaker 2: that point, the beautiful ones were still secluded the females.

711
00:38:58,560 --> 00:39:01,600
Speaker 2: They basically called this the first death of two deaths.

712
00:39:01,640 --> 00:39:05,000
Speaker 2: He did specifically call it a social death essentially.

713
00:39:04,680 --> 00:39:07,160
Speaker 1: Exactly like the death of the spirit, the death of

714
00:39:07,200 --> 00:39:12,000
Speaker 1: the society, and then eventually the physical death the second death.

715
00:39:12,000 --> 00:39:15,120
Speaker 2: Well, yeah, the one leads to the second, like there

716
00:39:15,200 --> 00:39:17,880
Speaker 2: is a point that you pass, and he came up

717
00:39:17,880 --> 00:39:19,840
Speaker 2: with a great name for it, called the behavioral sink

718
00:39:21,680 --> 00:39:24,000
Speaker 2: where they I think they refer to it as the

719
00:39:24,040 --> 00:39:26,080
Speaker 2: event horizon. Once you pass that, it's all.

720
00:39:25,960 --> 00:39:29,120
Speaker 1: Over, right, there's no coming back from that. And once

721
00:39:29,160 --> 00:39:31,839
Speaker 1: there's no coming back from that, not only has your

722
00:39:31,880 --> 00:39:37,640
Speaker 1: society collapsed, or does your society collapse, your population becomes

723
00:39:37,640 --> 00:39:42,080
Speaker 1: extinct because reproduction becomes impossible. Even he found, which is

724
00:39:42,120 --> 00:39:46,560
Speaker 1: pretty startling. He found that even after enough of the

725
00:39:46,560 --> 00:39:50,240
Speaker 1: population dies off that it returns to those factible, ideal

726
00:39:50,320 --> 00:39:53,040
Speaker 1: numbers of the early days in Universe twenty five or

727
00:39:53,080 --> 00:39:56,560
Speaker 1: any of the universes, they still don't reproduction doesn't start

728
00:39:56,640 --> 00:40:00,600
Speaker 1: up again because remember, social norms and bonds have broken down, Yeah,

729
00:40:00,600 --> 00:40:03,560
Speaker 1: they were, so they can't even figure out how to

730
00:40:03,719 --> 00:40:06,640
Speaker 1: reproduce once there's room for people enough.

731
00:40:06,680 --> 00:40:09,040
Speaker 2: Again, it's crazy, it is so interesting.

732
00:40:09,120 --> 00:40:13,040
Speaker 1: He said that he wrote this really kind of blockbuster

733
00:40:13,120 --> 00:40:16,640
Speaker 1: paper called Population Density and Social Pathology, and it was

734
00:40:16,680 --> 00:40:19,840
Speaker 1: published in Scientific American in nineteen sixty two, and he

735
00:40:19,920 --> 00:40:23,600
Speaker 1: said that the individuals that are born under these circumstances

736
00:40:23,960 --> 00:40:26,520
Speaker 1: will be so out of touch with reality as to

737
00:40:26,520 --> 00:40:28,400
Speaker 1: be incapable even of alienation.

738
00:40:28,800 --> 00:40:29,160
Speaker 2: Wow.

739
00:40:29,360 --> 00:40:31,760
Speaker 1: So like they can't even feel like they're not connected

740
00:40:31,760 --> 00:40:34,719
Speaker 1: as society anymore because there's no society for them to

741
00:40:34,719 --> 00:40:37,640
Speaker 1: ever connect or disconnect from.

742
00:40:38,040 --> 00:40:39,560
Speaker 2: It's frightening, it really is.

743
00:40:39,600 --> 00:40:43,120
Speaker 1: And a lot of people jumped on this and said, WHOA,

744
00:40:43,680 --> 00:40:45,840
Speaker 1: what's going on here? Because if you look at his data,

745
00:40:46,600 --> 00:40:50,399
Speaker 1: every time he ran this experiment, the results became the same.

746
00:40:50,960 --> 00:40:54,560
Speaker 1: There was an abundance of resources, there was never scarcity.

747
00:40:54,960 --> 00:40:58,600
Speaker 1: Population became overpopulation. Once it reached the point of the

748
00:40:58,640 --> 00:41:03,759
Speaker 1: behavioral sink, pulation slid into extinction. And on the way

749
00:41:03,800 --> 00:41:10,600
Speaker 1: there was violence, cannibalism and sexualism. Yeah yeah, in fan aside,

750
00:41:11,640 --> 00:41:17,000
Speaker 1: just like all the horrible things you can possibly think of, right,

751
00:41:17,040 --> 00:41:19,560
Speaker 1: you know, on the way toward extinction. And so a

752
00:41:19,560 --> 00:41:22,480
Speaker 1: lot of people said, you know, these mice kind of

753
00:41:22,520 --> 00:41:27,160
Speaker 1: are reflective of our own society, don't you think? And

754
00:41:27,320 --> 00:41:29,359
Speaker 1: Calhoun was kind of like, yeah, I would say that's

755
00:41:29,400 --> 00:41:30,320
Speaker 1: probably correct.

756
00:41:30,840 --> 00:41:32,760
Speaker 2: Yeah, And there was a big boom at the time

757
00:41:33,120 --> 00:41:36,560
Speaker 2: because of this experiment in literature and movies with a

758
00:41:36,600 --> 00:41:41,680
Speaker 2: lot of doomsday scenarios. Tom Wolfe, the Great writer wrote

759
00:41:42,040 --> 00:41:45,640
Speaker 2: in The Pumphouse Gang in nineteen sixty eight he actually

760
00:41:45,640 --> 00:41:50,480
Speaker 2: referenced the behavioral sink in reference to New York City,

761
00:41:51,040 --> 00:41:53,360
Speaker 2: and he said, it got to it was easy to

762
00:41:53,360 --> 00:41:56,040
Speaker 2: look at New Yorker's as animals, especially looking down from

763
00:41:56,040 --> 00:41:58,920
Speaker 2: someplace like a balcony at Grand Central at the rush

764
00:41:58,920 --> 00:42:01,680
Speaker 2: hour Friday afternoon floor was filled with poor white humans

765
00:42:02,080 --> 00:42:04,600
Speaker 2: running around, dodging, blinking their eyes, making a sound like

766
00:42:04,640 --> 00:42:07,840
Speaker 2: a pin full of starlings or rats or something. And

767
00:42:07,840 --> 00:42:09,759
Speaker 2: there are all these movies that came out. There was

768
00:42:09,800 --> 00:42:16,120
Speaker 2: one called ZPG with Oliver Reed and Geraldine Chapman chaplain.

769
00:42:16,680 --> 00:42:18,480
Speaker 2: It was called Zuropopulation Growth.

770
00:42:18,560 --> 00:42:21,080
Speaker 1: Yeah, like for a generation the government said no one's

771
00:42:21,080 --> 00:42:23,239
Speaker 1: allowed to have babies. Here's your robot.

772
00:42:22,960 --> 00:42:24,360
Speaker 2: Baby, right, and they're like, no, we're gonna have a

773
00:42:24,440 --> 00:42:28,120
Speaker 2: real baby, and they're like, no, you're not. I think

774
00:42:28,360 --> 00:42:30,160
Speaker 2: I didn't see it, but I'm sure it ended very poorly.

775
00:42:30,600 --> 00:42:32,759
Speaker 1: I didn't see it either. Yeah, I saw it on

776
00:42:32,800 --> 00:42:34,040
Speaker 1: IMDb though.

777
00:42:33,960 --> 00:42:39,319
Speaker 2: And of course, of course Soilent Green. Yeah, great, great movie.

778
00:42:39,120 --> 00:42:43,239
Speaker 1: From the novel make Room, Make Room, And there wasn't

779
00:42:43,239 --> 00:42:45,320
Speaker 1: no idea it's called that I didn't either. There's another

780
00:42:45,480 --> 00:42:50,279
Speaker 1: novel called stand On Zanzibar and there were people called

781
00:42:50,360 --> 00:42:54,319
Speaker 1: Muckers who ran amuck and just suddenly went crazy and

782
00:42:54,360 --> 00:42:57,280
Speaker 1: started killing a bunch of people. Oh no, it happens

783
00:42:57,280 --> 00:42:59,600
Speaker 1: from time to time in the news a lot of

784
00:42:59,640 --> 00:43:03,800
Speaker 1: people wood. We're saying, yeah, the stuff that Calhoun's finding

785
00:43:04,280 --> 00:43:11,359
Speaker 1: is clearly extrapolatable onto human society. And at the time too,

786
00:43:11,520 --> 00:43:13,440
Speaker 1: there was a lot of discussion about what to do

787
00:43:13,600 --> 00:43:19,840
Speaker 1: about inner city over population, crime, housing projects. There's this

788
00:43:19,960 --> 00:43:23,319
Speaker 1: really great documentary called The pruit I Go Myth and

789
00:43:23,360 --> 00:43:27,960
Speaker 1: it's about there was this the prud I Go project

790
00:43:28,400 --> 00:43:31,719
Speaker 1: in Saint Louis. This became I think we've talked about

791
00:43:31,760 --> 00:43:37,240
Speaker 1: it before, but it became like the the the poster

792
00:43:37,440 --> 00:43:40,279
Speaker 1: child for how no matter what you do for poor

793
00:43:40,320 --> 00:43:42,719
Speaker 1: inner city people, they're going to screw it up and

794
00:43:42,760 --> 00:43:45,120
Speaker 1: it's going to become crime ridden. And it's them, it's

795
00:43:45,160 --> 00:43:48,920
Speaker 1: not it's not their their their quality of life or education,

796
00:43:49,000 --> 00:43:52,200
Speaker 1: or anything like that. It's them and this this, this

797
00:43:52,360 --> 00:43:56,000
Speaker 1: documentary just totally demolishes that idea, but it's still a

798
00:43:56,040 --> 00:43:58,680
Speaker 1: long standing idea. And there were a group of police

799
00:43:59,160 --> 00:44:03,120
Speaker 1: policymakers who looked at Calhoun's research and said, clearly, we

800
00:44:03,760 --> 00:44:06,600
Speaker 1: need to do something. There's there's too many people, and

801
00:44:06,600 --> 00:44:11,040
Speaker 1: there's a lot of people who don't have valuable social

802
00:44:11,120 --> 00:44:14,719
Speaker 1: roles and they're turning to crime and everything. It was

803
00:44:15,239 --> 00:44:18,120
Speaker 1: very much open to interpretation because Calhoun, even though he

804
00:44:18,200 --> 00:44:21,320
Speaker 1: was putting these things in terms like heaven and utopia

805
00:44:21,400 --> 00:44:24,560
Speaker 1: and hell and behavioral sink and that kind of stuff,

806
00:44:24,760 --> 00:44:27,040
Speaker 1: he was still just kind of putting data out there

807
00:44:27,239 --> 00:44:29,640
Speaker 1: and it was up to society at large you interpret it,

808
00:44:29,680 --> 00:44:31,680
Speaker 1: And it really said a lot about your attitudes towards

809
00:44:31,680 --> 00:44:34,920
Speaker 1: your fellow human how you interpreted it. Yeah, but Calhoun

810
00:44:35,040 --> 00:44:39,160
Speaker 1: himself actually took something of an optimistic view of all

811
00:44:39,160 --> 00:44:41,399
Speaker 1: of this data, which is kind of mind boggling. Yeah,

812
00:44:41,400 --> 00:44:44,239
Speaker 1: I was surprised to read this. Actually it makes sense though,

813
00:44:44,239 --> 00:44:45,080
Speaker 1: if you think about it.

814
00:44:45,200 --> 00:44:48,000
Speaker 2: Yeah, he found that there were outliers and that not

815
00:44:48,280 --> 00:44:54,480
Speaker 2: all the mice descended into hellish violence and looting, right

816
00:44:54,520 --> 00:44:57,560
Speaker 2: and mouse looting. He found that some could actually handle this,

817
00:44:58,080 --> 00:45:00,359
Speaker 2: and what he called the ones that could had high

818
00:45:00,400 --> 00:45:04,640
Speaker 2: social velocity, mice that fared well with a lot of

819
00:45:04,719 --> 00:45:06,400
Speaker 2: high number of social interactions.

820
00:45:06,520 --> 00:45:09,200
Speaker 1: That is not me, And he said, I'm a type,

821
00:45:09,280 --> 00:45:12,200
Speaker 1: A blood type, blood personality type.

822
00:45:12,760 --> 00:45:17,000
Speaker 2: He said that basically, these mice will thrive. And he said,

823
00:45:17,080 --> 00:45:19,880
Speaker 2: and even the ones who don't, what he termed the losers,

824
00:45:21,040 --> 00:45:22,520
Speaker 2: found ways to be more creative.

825
00:45:22,680 --> 00:45:24,799
Speaker 1: Yeah, and he's so sufficient. Yeah.

826
00:45:24,800 --> 00:45:27,080
Speaker 2: He had a son of your outlook, basically saying that

827
00:45:27,640 --> 00:45:31,880
Speaker 2: man is essentially a positive animal, and we will create

828
00:45:31,960 --> 00:45:34,360
Speaker 2: and design our own solutions.

829
00:45:34,600 --> 00:45:37,040
Speaker 1: Right. And his solution was since and it makes sense

830
00:45:37,120 --> 00:45:41,400
Speaker 1: because he found that it's not scarcity or famines or

831
00:45:41,440 --> 00:45:46,359
Speaker 1: anything that leads to trouble. Yeah, it's overpopulation itself. His

832
00:45:46,440 --> 00:45:48,240
Speaker 1: idea was, well, let's go find more space.

833
00:45:48,520 --> 00:45:48,880
Speaker 2: Yeah.

834
00:45:48,920 --> 00:45:50,839
Speaker 1: And so he was a member of this group called

835
00:45:50,840 --> 00:45:52,759
Speaker 1: the Space Cadets, which was a group of thinkers that

836
00:45:52,760 --> 00:45:55,400
Speaker 1: were trying to figure out how to establish colonies on

837
00:45:55,840 --> 00:45:59,000
Speaker 1: Mars or the moon or wherever, right, which is exactly

838
00:45:59,520 --> 00:46:02,279
Speaker 1: what cal Who's point was is that we just need

839
00:46:02,280 --> 00:46:05,480
Speaker 1: more space. As long as we can sustain ourselves, that's fine.

840
00:46:05,840 --> 00:46:10,800
Speaker 1: But even if we don't stress agriculture, the planet or whatever.

841
00:46:11,000 --> 00:46:13,200
Speaker 1: We're still going to run into problems. So let's go

842
00:46:13,760 --> 00:46:15,040
Speaker 1: off to other worlds.

843
00:46:15,120 --> 00:46:16,919
Speaker 2: And terror form.

844
00:46:17,239 --> 00:46:19,000
Speaker 1: Oh and did you see the thing about the rats

845
00:46:19,000 --> 00:46:19,680
Speaker 1: of nim.

846
00:46:20,320 --> 00:46:21,400
Speaker 2: Oh was that taken?

847
00:46:21,560 --> 00:46:23,680
Speaker 1: Uh? It was based inspired by this, It was based

848
00:46:23,680 --> 00:46:25,960
Speaker 1: directly on his research. Oh really and that cool? Very

849
00:46:26,480 --> 00:46:29,440
Speaker 1: missus Brisbee and the Rats of nim nice. Yeah, so

850
00:46:29,560 --> 00:46:33,080
Speaker 1: go see that again and also go read the Behavioral Sink.

851
00:46:33,320 --> 00:46:33,960
Speaker 2: Super interesting.

852
00:46:34,040 --> 00:46:38,040
Speaker 1: Read an article on Cabinet by Will Wiles that informed

853
00:46:38,040 --> 00:46:39,120
Speaker 1: a lot of this episode.

854
00:46:39,320 --> 00:46:42,240
Speaker 2: Yeah, this stuff is fascinating to me. I agree because

855
00:46:42,440 --> 00:46:45,880
Speaker 2: I see kind of both sides. Clearly, there are some

856
00:46:45,960 --> 00:46:49,319
Speaker 2: issues going right now, but I also think that there

857
00:46:49,360 --> 00:46:51,360
Speaker 2: are solutions around the corner.

858
00:46:51,440 --> 00:46:54,759
Speaker 1: Yeah. I ultimately don't have a strong opinion either way,

859
00:46:54,760 --> 00:46:57,560
Speaker 1: And I think if I think about it, it's because

860
00:46:58,080 --> 00:47:01,960
Speaker 1: I think humans will yeah, becomeing ingenuitive.

861
00:47:02,640 --> 00:47:07,719
Speaker 2: You can have steak tonight, tons me too grasped.

862
00:47:07,800 --> 00:47:11,400
Speaker 1: Only you know it doesn't make it any better. I

863
00:47:11,400 --> 00:47:13,719
Speaker 1: mean that's why beef is so It uses so much

864
00:47:13,840 --> 00:47:16,920
Speaker 1: because it eats so much food that also requires water.

865
00:47:17,000 --> 00:47:19,560
Speaker 1: Yeah right, it requires water like two times over at

866
00:47:19,640 --> 00:47:23,359
Speaker 1: least Dumb cows yeah, I should feel bad about our

867
00:47:23,400 --> 00:47:25,440
Speaker 1: steak consumption, Chuck, I don't eat much steak.

868
00:47:25,719 --> 00:47:28,560
Speaker 2: Good for you, buddy, it's because Emily doesn't eat beef.

869
00:47:28,640 --> 00:47:30,960
Speaker 2: So oh yeah, you know, usually I just will cook

870
00:47:31,040 --> 00:47:33,040
Speaker 2: chicken because it's not like I'll have a steak and

871
00:47:33,080 --> 00:47:35,720
Speaker 2: I'll cook her chicken every now and then. But usually

872
00:47:35,719 --> 00:47:37,759
Speaker 2: it's just easier, yeah, because chicken comes in like a

873
00:47:37,800 --> 00:47:39,200
Speaker 2: two or three pack, right, you know.

874
00:47:39,400 --> 00:47:42,319
Speaker 1: Yeah, Plus you cook it until as dry as a bone,

875
00:47:42,360 --> 00:47:44,200
Speaker 1: so you can feel better about the water consumption.

876
00:47:44,320 --> 00:47:45,000
Speaker 2: Right, that's right.

877
00:47:45,600 --> 00:47:48,360
Speaker 1: If you want to know more about population growth and

878
00:47:48,520 --> 00:47:51,959
Speaker 1: specifically zero population growth, type those words into the search

879
00:47:52,000 --> 00:47:54,759
Speaker 1: bar housetiforks dot com. And since I said search bar

880
00:47:54,840 --> 00:47:56,479
Speaker 1: in there somewhere, it's time for listener mail.

881
00:47:58,719 --> 00:48:01,160
Speaker 2: I'm going to call this linguist sticks up for us.

882
00:48:01,640 --> 00:48:05,960
Speaker 2: Oh right, right, Hey, guys, I studied linguistics in college,

883
00:48:05,960 --> 00:48:07,560
Speaker 2: so it always tickles me when you guys go on

884
00:48:07,560 --> 00:48:10,279
Speaker 2: tangents about words and language. The main reason I'm writing

885
00:48:10,360 --> 00:48:12,080
Speaker 2: is because I want to offer you a counterpoint to

886
00:48:12,160 --> 00:48:16,720
Speaker 2: the language police that have been harshing your vibe. Grammar

887
00:48:16,800 --> 00:48:21,160
Speaker 2: nuts are what we call in the biz prescriptivists. Yeah,

888
00:48:21,440 --> 00:48:24,920
Speaker 2: who like to dictate how people should speak. Linguists, on

889
00:48:24,960 --> 00:48:29,319
Speaker 2: the other hand, are descriptivists who make their careers out

890
00:48:29,320 --> 00:48:32,560
Speaker 2: of how people actually speak in real world situations.

891
00:48:32,760 --> 00:48:35,200
Speaker 1: Oh, I didn't realize. I thought linguists could be one

892
00:48:35,280 --> 00:48:38,359
Speaker 1: or the other. I didn't realize that, like linguists tend

893
00:48:38,440 --> 00:48:40,200
Speaker 1: to be descriptivists.

894
00:48:40,520 --> 00:48:41,320
Speaker 2: That's what she says.

895
00:48:41,520 --> 00:48:46,040
Speaker 1: What is who wrote Infinite Jess David Foster Wallace. Yeah,

896
00:48:46,080 --> 00:48:48,920
Speaker 1: he was a big time prescriptivist. Oh really, you used

897
00:48:48,920 --> 00:48:51,000
Speaker 1: to drive him crazy, like how people should speak?

898
00:48:51,360 --> 00:48:52,680
Speaker 2: Yeah, yeah, like that.

899
00:48:52,680 --> 00:48:56,000
Speaker 1: There is a specific way that humans are supposed to

900
00:48:56,080 --> 00:48:58,760
Speaker 1: speak and write right and communicate, and if you deviate

901
00:48:58,800 --> 00:49:01,680
Speaker 1: from that, you're about as bad a human.

902
00:49:01,480 --> 00:49:03,000
Speaker 2: Being as you can be, and that would be like

903
00:49:03,040 --> 00:49:06,719
Speaker 2: the downfall of society or pretty much. Come on, we

904
00:49:06,760 --> 00:49:09,120
Speaker 2: don't use the terms good or bad grammar. Instead, we

905
00:49:09,160 --> 00:49:13,719
Speaker 2: prefer standard and non standard Linguists recognize the social functions

906
00:49:13,719 --> 00:49:17,719
Speaker 2: of non standard grammars and observe their uses and functions,

907
00:49:18,120 --> 00:49:21,320
Speaker 2: rather than to try and micromanage them. A final point,

908
00:49:21,640 --> 00:49:24,000
Speaker 2: I'm certain your listeners still know what you mean when

909
00:49:24,000 --> 00:49:26,600
Speaker 2: you say things like there's a lot of something, even

910
00:49:26,640 --> 00:49:29,640
Speaker 2: if it isn't standard grammar and the laws of linguistics.

911
00:49:30,000 --> 00:49:34,600
Speaker 2: As long as you're interlocutor, which is a.

912
00:49:34,560 --> 00:49:37,880
Speaker 1: Listener interlocutor interlocutor.

913
00:49:38,400 --> 00:49:42,520
Speaker 2: Yeah, as long as they accurately understand what you mean,

914
00:49:42,560 --> 00:49:46,360
Speaker 2: you have successfully communicated. Okay, And that's why humans invented language,

915
00:49:46,360 --> 00:49:49,080
Speaker 2: isn't it. So go be free and know that I

916
00:49:49,080 --> 00:49:51,080
Speaker 2: will always love your show no matter how you speak.

917
00:49:51,120 --> 00:49:52,240
Speaker 2: And that is from Kristin.

918
00:49:52,480 --> 00:49:56,200
Speaker 1: Thanks Kristin. The supportive linguists appreciate that. That's funny that

919
00:49:56,520 --> 00:50:00,719
Speaker 1: Kristin mentions that as long as you're interlocutor understands what

920
00:50:00,760 --> 00:50:04,359
Speaker 1: you're saying, you're communicating correctly. Sure, someone else I don't

921
00:50:04,360 --> 00:50:06,200
Speaker 1: remember who it was they wrote in and suggested we

922
00:50:06,239 --> 00:50:07,600
Speaker 1: do an episode on shorthand.

923
00:50:07,920 --> 00:50:09,760
Speaker 2: Oh interesting. I was just talking about that with Emily

924
00:50:09,840 --> 00:50:10,239
Speaker 2: last night.

925
00:50:10,440 --> 00:50:11,800
Speaker 1: Damn, it's all over the place.

926
00:50:11,840 --> 00:50:13,879
Speaker 2: I took speed writing in high school and she was did.

927
00:50:13,880 --> 00:50:15,040
Speaker 2: She very surprised at that.

928
00:50:15,400 --> 00:50:17,960
Speaker 1: So like speedwriting with hand.

929
00:50:17,760 --> 00:50:21,800
Speaker 2: Speed writing is like like stenography, No, write with your hand.

930
00:50:22,960 --> 00:50:27,760
Speaker 2: It's basically a version of shorthand, but not exact shorthand. Gotcha,

931
00:50:28,080 --> 00:50:29,560
Speaker 2: it's a kind of shorthand.

932
00:50:29,960 --> 00:50:33,200
Speaker 1: It sounds like shorthand, but like more aggressive. Yeah, like

933
00:50:33,400 --> 00:50:34,520
Speaker 1: max power or something.

934
00:50:35,000 --> 00:50:38,200
Speaker 2: The joke was my friend Shannon, I won't say her

935
00:50:38,280 --> 00:50:41,520
Speaker 2: last name, but she would cheat in class because she

936
00:50:41,840 --> 00:50:45,239
Speaker 2: didn't learn the shorthand. So the test where they would

937
00:50:45,280 --> 00:50:48,040
Speaker 2: just read a long passage quickly and you would have

938
00:50:48,080 --> 00:50:52,040
Speaker 2: to do it and then transcribe that into long hand.

939
00:50:52,440 --> 00:50:55,480
Speaker 2: She was just super good at writing really fast, so

940
00:50:55,480 --> 00:50:58,200
Speaker 2: she would just write down everything in longhand super fast

941
00:50:58,239 --> 00:50:59,920
Speaker 2: and then figure out how to transcribe it back to

942
00:51:00,080 --> 00:51:02,920
Speaker 2: shorthand and then back to long hand. And she got

943
00:51:02,920 --> 00:51:05,400
Speaker 2: caught doing that. Yeah, and the teacher's like, that's cheating.

944
00:51:05,680 --> 00:51:07,839
Speaker 1: Yeah, it sounds like it. She was like, well, mnd

945
00:51:07,960 --> 00:51:12,520
Speaker 1: me with fast still liven't. Nope, that's not speedwriting. That's

946
00:51:12,560 --> 00:51:13,440
Speaker 1: just writing fast.

947
00:51:13,600 --> 00:51:13,880
Speaker 2: Yep.

948
00:51:15,280 --> 00:51:16,919
Speaker 1: If you want to get in touch with us, either

949
00:51:16,960 --> 00:51:20,280
Speaker 1: to show us support, criticize us, and even something neutral

950
00:51:20,360 --> 00:51:23,120
Speaker 1: is fine, you can tweet to us at sysk podcast.

951
00:51:23,440 --> 00:51:25,600
Speaker 1: You can join us on Facebook dot com slash Stuff

952
00:51:25,600 --> 00:51:27,480
Speaker 1: you Should Know. You can send us an email to

953
00:51:27,600 --> 00:51:30,920
Speaker 1: Stuff Podcast at HowStuffWorks dot com and has always joined

954
00:51:31,000 --> 00:51:33,239
Speaker 1: us at our luxurious home on the web. Stuff you

955
00:51:33,320 --> 00:51:36,960
Speaker 1: Should Know dot com.

956
00:51:37,080 --> 00:51:39,399
Speaker 2: Stuff you Should Know is a production of iHeartRadio.

957
00:51:39,880 --> 00:51:43,080
Speaker 1: For more podcasts, My heart Radio, visit the iHeartRadio app,

958
00:51:43,280 --> 00:51:46,200
Speaker 1: Apple podcasts, or wherever you listen to your favorite shows.