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Speaker 1: Welcome to Stuff to Blow your Mind from House Stop

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

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Speaker 1: My name is Robert Lamb, I'm showing performant, and I'm

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Speaker 1: Christian Sager. Hey. So we have been doing periscope for

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Speaker 1: two weeks now. We've had a bunch of our listeners

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Speaker 1: sign in and ask us questions and hang out with

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Speaker 1: us on Friday afternoons. So we wanted to remind you

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Speaker 1: that we're going to keep doing that as long as

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Speaker 1: you are interested. So every Friday at noon we will

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Speaker 1: be on the periscope, and if you want to find

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Speaker 1: out more about that, you can of course visit us

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Speaker 1: on all of our social media channels, which is Facebook, Twitter,

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Speaker 1: or Tumbler, all of those. We are blow the mind.

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Speaker 1: Don't forget that website Stuff to Blow your Mind dot com. Wait,

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Speaker 1: what's that? What's on that website? All the stuff, all

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Speaker 1: of it? Yeah, is that where the videos are. That's

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Speaker 1: where the videos are, and that's where the blog posts are,

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Speaker 1: That's where all the podcasts. Like some of you listen

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Speaker 1: to this and think all we do is the podcast,

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Speaker 1: but we've got a lot going on blogging. There's Robert

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Speaker 1: does some amazing content about butts. Yeaheah, there's a there's

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Speaker 1: some science on that galories and there's a monster science

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Speaker 1: both in video form and blog form. The latest one

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Speaker 1: I liked a lot, the cat Bus one. Oh yeah, yeah,

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Speaker 1: I just did one on the cat Bus from Totoro.

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Speaker 1: Just rewatched that film with my family and uh, yeah,

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Speaker 1: there's some cool science behind cat Bus. I've actually never

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Speaker 1: seen it. You've got to get on there. Well, you

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Speaker 1: know the other new thing that we've got going on,

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Speaker 1: new music at the top of the show, guys, Yeah,

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Speaker 1: how about that welcome a welcome change that was created

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Speaker 1: by our fabulous producer Noel Brown. Bravo to you, sir.

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Speaker 1: But also I didn't notice this until I had heard

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Speaker 1: it a few times. It reminds me of the Doctor

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Speaker 1: Who theme, you know, it's with the with the lead part. Well,

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Speaker 1: I like to think of us as that that. You

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Speaker 1: know what the Three Doctors is. It's when three friend

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Speaker 1: doctors from different time periods. I'll meet up together with

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Speaker 1: their tartises and hang out and and stop bad things

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Speaker 1: from happening. So we're like the three Doctors, but just

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Speaker 1: obviously the angry doctor. Yeah, yeah, I'm I'm Yeah, I'm

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Speaker 1: the curmudgeon doctor. That would be I guess I'm most

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Speaker 1: like Peter Capaldi. Yeah, that would be the angry doctor. Yeah.

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Speaker 1: So we've all met up and here we are to

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Speaker 1: talk about, of course what doctor the doctor would talk

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Speaker 1: about the ig Nobel Prizes, that's right. Yeah, So that

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Speaker 1: we all three decided to come together on this one

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Speaker 1: because there are a lot of studies we want to

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Speaker 1: We want to roll through them in two episodes, each

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Speaker 1: of us taking the head on a different study and

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Speaker 1: not doing a super deep dive, but giving you a

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Speaker 1: nice overview about what this particular study is, why it's ridiculous,

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Speaker 1: why in many cases it's actually important, because that's one

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Speaker 1: of the keys here. The Ignailed Bell Prizes have been around,

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Speaker 1: uh since I believe, and each year they award ten

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Speaker 1: prizes to various bits of research, pure viewed studies that

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Speaker 1: you know, it's not the kind of stuff you're gonna

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Speaker 1: see capturing the headlines necessarily any scientists do, blind of

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Speaker 1: American not winning an actual Nobel prize. Right here at

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Speaker 1: how Stuff Works. We've sort of developed a like sub

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Speaker 1: theme for studies like this, which is sometimes we publish

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Speaker 1: these on our Now how stuff works channel, which is

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Speaker 1: there's a study for that, which is the kind of

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Speaker 1: like can you believe somebody did this? But at the

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Speaker 1: same time, I think the thing about the ignobles is

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Speaker 1: that there's always some value and importance to the research.

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Speaker 1: So yeah, like I always think of in cases like this,

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Speaker 1: I think of science as a slime mold amaze. Right now,

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Speaker 1: the slime mold is expanding outward, exploring its terrain, trying

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Speaker 1: to find food, and so it's going down all the

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Speaker 1: hallways to explore. And that's what science does. And just

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Speaker 1: because a particular area of study seems kind of pointless

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Speaker 1: at first glance, or it seems just very very specific,

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Speaker 1: or it's trying to prove something that we already kind

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Speaker 1: of know in our gut, it's still important that science

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Speaker 1: go there because science is is mapping the universe. Yeah,

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Speaker 1: and some of the corners of that map are watching

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Speaker 1: elephants p exactly. Well, Okay, I mean I slime mold.

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Speaker 1: Can we stick with the gelatinous cube here? Can we

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Speaker 1: use a gelatinous cube or is a slim mold like

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Speaker 1: a very specific metaphor for ignoble science. Um, I like

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Speaker 1: the slime because slime old this swarm intelligence. Oh okay,

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Speaker 1: I see what you're saying. Okay, I'll allow it. Uh. So,

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Speaker 1: let's af familiarize the audience with what these things are,

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Speaker 1: because the first time I heard about it was in

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Speaker 1: one of our episodes when we were talking about necrophilia,

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Speaker 1: and we uh, there was a study that had previously

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Speaker 1: won the Ignoble, which was about a guy watching a

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Speaker 1: duck try to have sex with a dead duck. Some

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Speaker 1: of the big ones. It was like seventy minutes of

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Speaker 1: of him watching that with a notebook. If you really

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Speaker 1: want to hear the whole full story, go back and

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Speaker 1: sense that necrophilia episode. But uh, it's weird, but it

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Speaker 1: also had value to it, and so uh, let's kind

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Speaker 1: of encapsulate what these things are for our audience and

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Speaker 1: then we can dive into the tent studies. Well, the

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Speaker 1: principal individual here is Mark abrams Um. He's the editor

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Speaker 1: and co founder of Improbable Research magazine, which you can

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Speaker 1: find at their website. Let a link to that on

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Speaker 1: the landing page for this episode. And they regularly cover

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Speaker 1: research of this nature stuff. It's weird stuff that's a

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Speaker 1: little wacky, but it's still science and it's always covered

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Speaker 1: in essentially a loving way. Yeah. They say that it's

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Speaker 1: supposed to make you laugh but also make you think. Yeah.

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Speaker 1: I found this was true about a lot of the

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Speaker 1: studies we looked at this time. I I like the

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Speaker 1: sense that, um, it is certainly not stroking the ego

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Speaker 1: of the scientists who perform these studies, but it's not

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Speaker 1: really making fun of them either, or maybe sort of

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Speaker 1: making fun of them, but at least not denigrating their work.

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Speaker 1: I think there have only been a couple of cases

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Speaker 1: where because for one thing, you can nominate your own work,

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Speaker 1: and they get about five thousand nominations each year. But

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Speaker 1: I can only think of two cases offhand where the

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Speaker 1: individuals did not receive it warmly, one of which was

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Speaker 1: the U. S. Air Force over their gay bomb, which

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Speaker 1: they won a prize four two thousand seven. They did

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Speaker 1: not remember hearing about this. Yeah, they did not see

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Speaker 1: the humor in it. No, hold on what were the

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Speaker 1: details of the gay bomb. The gay bomb, as I recall,

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Speaker 1: was that proposed weaponized afrodisiac that you would be able

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Speaker 1: to throw into the enemy trenches and harmonomy morale by

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Speaker 1: making them, Yeah, get friendly, amorous and confused. It was

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Speaker 1: referenced nicely on an episode of thirty Rocks several years back.

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Speaker 1: But but yeah, the the the Air Force did not

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Speaker 1: see the humor in that. And there there have been

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Speaker 1: a couple of curmudgeons along you know, over the years

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Speaker 1: that have have come out and said, oh, well, this

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Speaker 1: is just you know, waste of time. For instance, the

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Speaker 1: ve Robert May, the British government's chief scientific advisor at

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Speaker 1: the time, it was a big was a big critic

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Speaker 1: of the Nobel Prizes and he was saying that it

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Speaker 1: should only be used to, you know, to ridicule anti

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Speaker 1: science and pseudo science, and he should leave the the

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Speaker 1: real scientists alone. Uh again, totally missing the point that

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Speaker 1: the scientists are the are the ones who enjoy this

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Speaker 1: the most. Don't you love people who hate humor? So

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Speaker 1: I let's address that for a second though, because right

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Speaker 1: before we came in here, I watched the almost all

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Speaker 1: of the ceremony for awards, which we just which is

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Speaker 1: what we're going to cover today. I watched a lot

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Speaker 1: of this too, and and I feel like you definitely

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Speaker 1: get the tone that they're going for if you watch

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Speaker 1: the ceremony. They did a great job filming it. It's

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Speaker 1: got multiple camera angles. It's filmed in this beautiful theater

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Speaker 1: at Harvard University. Um that I saw Spalding Gray at

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Speaker 1: that same theater, like god fifteen years ago or something

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Speaker 1: like that, So it was it was neat to see that.

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Speaker 1: But uh, the humor is very oh wacky. Yeah, it's

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Speaker 1: it's that kind of like academic like we don't want

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Speaker 1: to offend anybody humor that's just a little zany and

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Speaker 1: goofy and so at this at one on one hand,

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Speaker 1: I appreciate it, and I totally understand. Uh, I understand

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Speaker 1: that this guy who was it? Who? Who Robert may

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Speaker 1: who was so like against it in the first place. Yeah,

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Speaker 1: I don't understand where he's coming from. But I'm also

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Speaker 1: somewhat sympathetic because it's just like whacketty schmackt he do

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Speaker 1: zany like, you know, like jokes and stuff. Even if

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Speaker 1: you're not laughing with the jokes, you're still like, oh, well,

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Speaker 1: that's kind of adorable. Watch the old old scientists get

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Speaker 1: up there and kind of you know, hap uh, you know,

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Speaker 1: yuck it out. But I will confess though that it

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Speaker 1: was unbearable for me to a point that I had to.

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Speaker 1: I watched it on YouTube, and I had to watch

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Speaker 1: it on double speed, so everybody was talking really fast

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Speaker 1: like chipmunks. But I got the gist of everything, and

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Speaker 1: I paid a special Please stop, Christian, I'm bored. Yeah, yeah,

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Speaker 1: but please please stop. Joe tell them about what's her name,

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Speaker 1: Pootie Pie or I think it's called Sweetie poop child.

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Speaker 1: Pot Pie is the guy on YouTube. Oh, I don't

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Speaker 1: know anyway. Yeah, they have a little character who is

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Speaker 1: a child who comes up. It's like the Oscar music

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Speaker 1: that starts to play when people get a little too

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Speaker 1: much gratitude in their speech at the Oscars. Uh. And

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Speaker 1: they want to get you off the stage at this ceremony.

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Speaker 1: They've got a child who runs up to you and

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Speaker 1: proclaims boredom and tells you to stop talking. And she's precocious.

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Speaker 1: And then on top of that, then they bring back

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Speaker 1: all the former Sweetie poos. Did you watch that part? Didn't?

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Speaker 1: Every former Sweetie pooh is a part of the procession. Uh.

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Speaker 1: And they are like the van Ol White that kind

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Speaker 1: of like escort out the winners of the awards and

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Speaker 1: then escort them back in. And they do this whole

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Speaker 1: cute see poo thing where they have the sweetie poo's

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Speaker 1: a'll tease one another in front of the audience. It's

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Speaker 1: a little much. Um. Also, I grew up you have

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Speaker 1: a you have a very dark heart that has no

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Speaker 1: room for for cuteness. Well I do, and I'll allow

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Speaker 1: this as well. I'm from Boston and I'm very familiar

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Speaker 1: with Harvard University. I lived right near there for a

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Speaker 1: long time, and I'm very familiar with this kind of

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Speaker 1: humor that is, you know, it's very um yeah, twee,

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Speaker 1: I guess is one way to put it. So um.

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Speaker 1: I will warn I will warn our listeners ahead of

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Speaker 1: time that maybe I'm the dark heart here, but I

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Speaker 1: was a little put off by the zany humor. However,

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Speaker 1: the studies themselves are amazing. Well, let's launch into them here, Um, Joe,

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Speaker 1: I believe you're up first, tell us about the chemistry

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Speaker 1: prize winner, the Ignobil Chemistry Prize winner for two thousand. Okay,

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Speaker 1: so the Chemistry prize went to I'm gonna say their names,

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Speaker 1: and there's a lot of them, so bear with me

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Speaker 1: for a second. Here, Callum Ormond, Colin Raston, Tom Yuan,

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Speaker 1: Stephen Kudla check Samir and couldn't cha Joshua In Smith,

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Speaker 1: William A. Brown, Caitlin Pouglisi, Tivoli, Olson, Mariam eft Car,

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Speaker 1: Greg Weiss. Oh, And that's it them for demonstrating a

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Speaker 1: mechanical process to unboil an egg. So yes, these scientists

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Speaker 1: all work together. Now, I think the main scientist who

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Speaker 1: has involved in this process, the one at least who

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Speaker 1: has spoken to the media the most about it, was

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Speaker 1: Greg Weiss. But they came up with this mechanical process

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Speaker 1: to unboil an egg. Well, hold on part of an egg,

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Speaker 1: the egg white, and note that this is not uncooking

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Speaker 1: an egg, and that you can't unfry an egg or

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Speaker 1: unscramble an egg or on. I wonder if you could

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Speaker 1: unpoach and egg maybe, but at least what you can

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Speaker 1: do is unboiled an egg white. So the paper is

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Speaker 1: called and I love the academic alienation of this title

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Speaker 1: sheer stress mediated refolding of proteins from aggregates and inclusion bus. Yes,

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Speaker 1: I can call the paper is a colon and then

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Speaker 1: another long sentence after that. And that's one of the

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Speaker 1: beautiful things about the Ignabel Prizes is this is a

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Speaker 1: great example of where they really got to the heart

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Speaker 1: of what this study is in the way that the

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Speaker 1: rest of us can enjoy. Yeah, and so it was

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Speaker 1: published in January in Kim Bio Kim And here's what happened.

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Speaker 1: The researchers took an egg and then they divided the

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Speaker 1: whites from the yolk, and then they boiled the egg

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Speaker 1: whites for twenty minutes at about a hundred ninety four

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Speaker 1: degrees fahrenheit or ninety degrees c. And if you have

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Speaker 1: ever seen the white part of a hard boiled egg,

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Speaker 1: you know what we're dealing with is the final product here.

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Speaker 1: And so egg whites in their natural state are a

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Speaker 1: mixture of water and proteins, including the protein lysozyme, which

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Speaker 1: is a protein associated with immune function and more on

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Speaker 1: that in a minute. When you boil egg whites, the

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Speaker 1: folded proteins that are in the egg whites get d natured,

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Speaker 1: meaning that they sort of lose their precisely folded structure.

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Speaker 1: And that folded structure is what makes a protein do

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Speaker 1: the things that it does, sort of like what determines

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Speaker 1: its role in the in the tiny bio world that

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Speaker 1: it inhabits. And they also get all tangled together, and

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Speaker 1: they end up forming this solid block of bio jello

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Speaker 1: that we recognize as cooked egg white. Um, so that's

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Speaker 1: what normally happens when you when you boil an egg white.

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Speaker 1: It of course happened in this case as it always does.

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Speaker 1: And how do you fix this problem? So the researchers

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Speaker 1: took the boiled egg whites and dissolved them in a

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Speaker 1: urea solution to turn basically all of it into an

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Speaker 1: untangled up liquid of of unfolded proteins. So that's pretty gross.

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Speaker 1: It's a cooked egg white slurry. Then the solution was

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Speaker 1: placed into what they called a vortex fluid device. And

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Speaker 1: what this does is it's kind of like a centerfuge

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Speaker 1: but different. It spins a test tube a really high intensity.

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Speaker 1: I read one source saying it was about five thou

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Speaker 1: rpm s, so very very fast for a short time,

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Speaker 1: introducing an amazing amount of sheer stress to the film

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Speaker 1: of fluid, which is about one micron thick that built

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Speaker 1: up on the walls of the tube. And in a

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Speaker 1: quote given to the Washington Post, Greg Wise said about

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Speaker 1: the proteins in this process, they're like little elastic bands.

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Speaker 1: This stretches and unstretches them and gives them their shape back.

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Speaker 1: So it's kind of like if you, you know, stretch

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Speaker 1: out a tangled up slinky and then get it to

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Speaker 1: reform back into its original shape. But help me out

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Speaker 1: here for a second. So my understanding of the I

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Speaker 1: didn't read this study, but my understanding of what how

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Speaker 1: you're presenting it here is the dn turing process. By

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Speaker 1: zipping around at this high speed, are they they're renaturing.

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Speaker 1: They're refolding back into the original shape that the proteins

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Speaker 1: were before they were denatured by the heat. So it's

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Speaker 1: not just that they're moving something at such a high

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Speaker 1: speed that it like liquefies. No, it's it's actually re

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Speaker 1: applying the process. Yeah, they're they're undoing the work of

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Speaker 1: cooking by applying this mechanical stress. And so at the

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Speaker 1: end they were able to refold some of the proteins

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Speaker 1: back into their original shapes without it becoming tangled up

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Speaker 1: with one another in the process. That's another problem that

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Speaker 1: can happen, and it worked at the end. The solution

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Speaker 1: contained proteins comparable to uncooked egg whites, and that immune

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Speaker 1: related protein I mentioned lycenseyme that was also found to

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Speaker 1: be present in the final product and functional. Okay, So

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Speaker 1: what I want to know though, is did they make

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Speaker 1: a quiche with this afterwards? And how did it take?

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Speaker 1: You know, you couldn't because it's just the egg whites

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Speaker 1: and you really need some miolks for keysh We could

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Speaker 1: otherwise that would be pretty gross and you could make

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Speaker 1: a few different mixed drinks though, right, Oh, you probably could.

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Speaker 1: The New Orleans was sour fizz. What's it? What's it

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Speaker 1: called when you put an egg white in there? Something

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Speaker 1: jim fizz something something fizz. Gross. That's like when people

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Speaker 1: drink like oyster shots and stuff like that. It's nasty.

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Speaker 1: I have no idea what an oyster? You add oysters,

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Speaker 1: like raw oysters to it, like like a real oyster. Yeah,

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Speaker 1: that's about like having an oyster though. I mean, you

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Speaker 1: need to stop talking. You just mix it with like

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Speaker 1: booze and I think like tomato juice or something like that.

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Speaker 1: Disgusting study. Okay, So so they did it. They managed

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Speaker 1: to unboil egg whites and get them back to the

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Speaker 1: original state. A couple of things we I guess, in

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Speaker 1: case it needs stating, Why is this ridiculous? Well, I

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Speaker 1: guess it just sort of seems to contradict the folk

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Speaker 1: understanding of entropy that you can't fix what's broken. You

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Speaker 1: can't unscramble an egg, I think is an actual expression.

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Speaker 1: You might not be able to unscramble an egg, but

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Speaker 1: you can unboil an egg. Well, there's so many fun

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Speaker 1: metaphors that you can reverse with this, right. I think

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Speaker 1: it's also funny because it involves eggs, which apparently some

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Speaker 1: people just think are inherently funny objects, and slightly more

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Speaker 1: when they're boiled, especially because it's life. But but this

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Speaker 1: process actually does have real, very important and interesting applications,

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Speaker 1: not just for fun. It can make a big difference

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Speaker 1: in many kinds of research involving proteins. For example, cancer

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Speaker 1: research in the lab often deals with proteins that have

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Speaker 1: exactly the same problem that cooked egg whites do. They

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Speaker 1: can become tangled in misshape, and so the proteins get

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Speaker 1: d natured, or they're they're folding gets messed up, or

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Speaker 1: they get tangled together or both, and then scientists have

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Speaker 1: to waste a lot of time trying to restore them

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Speaker 1: to to the original folded shape and structure, to get

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Speaker 1: them to be useful and by conventional means, this is

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Speaker 1: a really time consuming and wasteful process. So this sheer

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Speaker 1: stress method that they introduced with this the vortex fluid device,

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Speaker 1: could actually make cancer research much more efficient in terms

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Speaker 1: of time and cost. So it was reported in their

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Speaker 1: study that the refolding of protein by sheer stress was

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Speaker 1: faster than the conventional method of quote, overnight dialysis by

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Speaker 1: a factor of more than a hundred times, more than

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Speaker 1: a hundred times faster. So that's why it took eleven

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Speaker 1: people to to write this paper. There were different people

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Speaker 1: involved with different aspects of it. Like I know, some

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Speaker 1: of the people cited in the paper were people who

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Speaker 1: were working with the vortex fluid device itself. Like the

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Speaker 1: stories like ten more times. I want to just see

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Speaker 1: how many times we can say vortex fluid device throughout

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Speaker 1: the sentence, right, So there's some people working with that,

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Speaker 1: some people who are presumably working with the science of

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Speaker 1: the proteins. Yeah, yeah, routine labs. So probably some I'm

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Speaker 1: guessing like graduate students in there who did like the

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Speaker 1: scut work. Possibly, I don't know what every single name

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Speaker 1: corresponds to. It's one of those, uh, one of those

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Speaker 1: features of Modern Sciences. That you get a lot of

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Speaker 1: names listed. Yeah, let's see if we can find which

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Speaker 1: one of these has the most people applied to it. Okay, okay,

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Speaker 1: so I'm keeping track. We got eleven on this one. Well,

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Speaker 1: let's check out the next prize awarded in the physical science.

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Speaker 1: And this one was a lot of fun. I actually

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Speaker 1: read about this before this episode, but Robert, it was

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Speaker 1: about urine, right, oh, yes, And you're definitely uh, you're

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Speaker 1: in for a treat on this one because this is

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Speaker 1: the Physics Prize for bladder speed. And noticed I said

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Speaker 1: physics and not biology here. That's gonna be a key

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Speaker 1: as we move forward. The paper in particular is a

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Speaker 1: duration of urine does not change with body size by

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Speaker 1: Georgia Institute of Technology researchers Patricia ja Yanga, Jonathan Fama,

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Speaker 1: Jerome Choah, Dave and Dave at l Q. And this

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Speaker 1: was published in the summer two thousand fourteen edition of

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Speaker 1: Proceedings of the National Academy of Sciences. And they're just

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Speaker 1: right down the street these Yeah. I have read several

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Speaker 1: times about the research involving David who in one way

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Speaker 1: or another. Yeah, I mean we were talking before the

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Speaker 1: episode of the field of fluid dynamics. You see some

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Speaker 1: of the most amazing papers coming up and and this

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Speaker 1: one definitely uh fits the bill. Did they use a

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Speaker 1: vortex fluid device? Not as such. No. So basically, in

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Speaker 1: a nutshell, the study investigated how quickly thirty two different

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Speaker 1: animals you're inate now? Sixteen of these are live viewing

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Speaker 1: the rest or YouTube videos. Um, because they were they

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Speaker 1: were interesting to animals of varying size. It's YouTube research. Yeah.

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Speaker 1: So the interesting initial finding here though, is that it

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Speaker 1: turns out that it's all about the same at least

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Speaker 1: for animals that weigh at least six point six pounds

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Speaker 1: or three kilograms. They found all animals that weigh more

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Speaker 1: than that urinate in the same space of time. Um.

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Speaker 1: So it doesn't matter if it's an elephants bladder that

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Speaker 1: is thirty undred times larger than a cat's. Both animals

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Speaker 1: are going to get their being over with and about

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Speaker 1: uh twenty seconds. Okay. And the reason here is, the

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Speaker 1: paper explores, is that as diverse as all these different

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Speaker 1: bladder systems are, um and and all the plumbing, they

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Speaker 1: all rely on the fundamental principles of fluid mechanics. The

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Speaker 1: researchers found that quote the urethra is a flow enhancing device,

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Speaker 1: enabling the urinary system to be scaled up by a

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Speaker 1: factor of thirty six in volume without compromising its function.

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Speaker 1: One of the idea here is that it's possible that

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Speaker 1: larger animals have a longer urethras, and since the weight

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Speaker 1: of the fluid in the urethra is pushing the fluid out,

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Speaker 1: the long urethra increases the flow rate. So this is

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Speaker 1: why if you've ever gone online like our researchers and

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Speaker 1: watched a whole bunch of animals uh urinate, you might

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Speaker 1: see the larger animals just really pushing it out there

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Speaker 1: like a sheet of urine hose. Yeah, whereas the smaller creatures,

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Speaker 1: definitely the ones under that six point six pound limit. Uh,

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Speaker 1: they're just throwing out some droplets. Uh. For instance, mice

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Speaker 1: and rats less than two seconds spent urinating bats just

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Speaker 1: a fraction of the speed. So this would be important,

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Speaker 1: of course, keeping your your p speed up for an

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Speaker 1: elephant because uh, if if an elephant had a shorter

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Speaker 1: ure threat, it would take longer to urinate, and during

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Speaker 1: that time, of course, it would be more susceptible interpretation. Wow,

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Speaker 1: so okay, never, I've never really thought about the evolutionary

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Speaker 1: pressures on urination speed. That just doesn't really occur to

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Speaker 1: me as a thing having a big having a big

403
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Speaker 1: impact on your survival or reproduction. Well, it's how much

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Speaker 1: time that you're you're you have to remain stationary and

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Speaker 1: you're somewhere not paying attention to your surrounding. You have

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Speaker 1: to remain stationary and pooping on the move. I haven't tried.

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Speaker 1: I don't know. You've seen you've both seen Burdenmic, I have,

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Speaker 1: so you know what happened. It can't happen when their

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Speaker 1: predators about and you need to go potty. And I'm

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Speaker 1: thinking about my dog, who definitely breaks the twenty second rule.

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Speaker 1: I mean, he pees for a long time when we

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Speaker 1: go out first thing in the morning, and that's probably

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Speaker 1: because he's got a cushy life and he doesn't have

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Speaker 1: to worry about the pressures of survival. That's a good point.

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Speaker 1: That's a good point. So I probably don't have didn't

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Speaker 1: measure humans as well. I'm not sure if humans were

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Speaker 1: in the in the study. To be honest, you know,

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Speaker 1: I wonder one thing that could be affecting the different

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Speaker 1: times of domesticated animals versus whatever they used in the study.

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Speaker 1: I'm sure some of those were domesticated, would be whether

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Speaker 1: they are being prompted the urinate or whether they're just

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Speaker 1: urinating whenever they feel like it, because it could be

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Speaker 1: that in under natural circumstances, you will naturally let the

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Speaker 1: amount of urine and build up in your bladder that

425
00:24:01,480 --> 00:24:04,200
Speaker 1: it will take to evacuate in twenty or twenty one second.

426
00:24:04,560 --> 00:24:07,400
Speaker 1: But if you're a dog who's inside and can't pee

427
00:24:07,560 --> 00:24:10,000
Speaker 1: until it's time to go out, you might go beyond

428
00:24:10,080 --> 00:24:14,080
Speaker 1: that threshold when and accumulate a lot. You're thinking, okay,

429
00:24:14,080 --> 00:24:16,440
Speaker 1: so I probably don't have to tell anybody why this

430
00:24:16,760 --> 00:24:19,439
Speaker 1: study is ridiculous, because of course it involves urine, and

431
00:24:19,560 --> 00:24:23,840
Speaker 1: urine like eggs, is inherently funny. Um. But as to

432
00:24:23,920 --> 00:24:29,080
Speaker 1: why it's important for starters, it's an understudied topic. Not

433
00:24:29,160 --> 00:24:31,160
Speaker 1: a lot of people are putting a lot of time

434
00:24:31,200 --> 00:24:35,240
Speaker 1: and effort into the question of urination times for animals,

435
00:24:35,680 --> 00:24:38,720
Speaker 1: And additionally, the study may help to diagnose urine a

436
00:24:38,880 --> 00:24:41,080
Speaker 1: problems and animals as well as inspired of the design

437
00:24:41,119 --> 00:24:46,480
Speaker 1: of quote scalable hydro dynamic systems based on those in nature,

438
00:24:46,520 --> 00:24:49,640
Speaker 1: So there's a biomimicry potential here. Um, you don't need

439
00:24:49,840 --> 00:24:53,400
Speaker 1: to necessarily use external pressure to get rid of fluids

440
00:24:53,400 --> 00:24:56,080
Speaker 1: in a system quickly. Rather, you can let gravity do

441
00:24:56,119 --> 00:24:59,920
Speaker 1: the trick with the right bio mimicry. So possible applications

442
00:25:00,080 --> 00:25:07,080
Speaker 1: include water tanks, UM, backpacks that are used to you know, uh,

443
00:25:07,080 --> 00:25:11,199
Speaker 1: fire hoses, and UM. A couple of examples here in

444
00:25:11,240 --> 00:25:15,200
Speaker 1: which the team actually engaged with some additional experimentation. Uh.

445
00:25:15,240 --> 00:25:19,320
Speaker 1: They actually created a demonstration that empties a teacup, court

446
00:25:19,560 --> 00:25:22,240
Speaker 1: and gallon of water in the same duration for each

447
00:25:22,320 --> 00:25:26,000
Speaker 1: using varying links of connected tubes, thus standing in for

448
00:25:26,040 --> 00:25:31,080
Speaker 1: the varying urethra links UM links among these animals. And

449
00:25:31,080 --> 00:25:33,680
Speaker 1: then they connected a second experiment in which the team

450
00:25:33,720 --> 00:25:36,040
Speaker 1: filled three cups with the same amount of water then

451
00:25:36,080 --> 00:25:39,760
Speaker 1: empty them at varying rates by using different lengths of tube.

452
00:25:40,240 --> 00:25:43,960
Speaker 1: So again you don't have to use you know, varying

453
00:25:44,000 --> 00:25:46,560
Speaker 1: amounts of external pressure to move water through a system.

454
00:25:46,560 --> 00:25:49,440
Speaker 1: You can use to use gravity and tube length. This

455
00:25:49,520 --> 00:25:52,560
Speaker 1: is uh, they're like modern day Da Vincis like this

456
00:25:52,600 --> 00:25:54,320
Speaker 1: is this is the kind of stuff that Da Vinci

457
00:25:54,400 --> 00:25:56,320
Speaker 1: used to sit around and ponder about and work on

458
00:25:56,359 --> 00:25:59,199
Speaker 1: in his sketch books like how to move water more Efficiently.

459
00:25:59,760 --> 00:26:03,160
Speaker 1: They're just looking to nature's way of doing it. Yeah, yeah,

460
00:26:03,200 --> 00:26:06,400
Speaker 1: it's it's basically a bio on the medic exercise. Now,

461
00:26:06,440 --> 00:26:09,200
Speaker 1: so that makes me wonder if the next thing we're

462
00:26:09,200 --> 00:26:12,480
Speaker 1: going to hear about associated with David who is an ornithopter.

463
00:26:13,960 --> 00:26:16,000
Speaker 1: That would be great. Well wait, let's define that for

464
00:26:16,040 --> 00:26:18,480
Speaker 1: our audience for a second. Oh, if you've listened to

465
00:26:18,480 --> 00:26:21,240
Speaker 1: the Dune episode, you know all about ornithopters. No, didn't

466
00:26:21,280 --> 00:26:26,880
Speaker 1: da Vinci designed some mornithopters flapping flapping wing aircraft. Now,

467
00:26:26,880 --> 00:26:29,879
Speaker 1: I'm afraid that's wrong. That did he design propeller driven

468
00:26:30,040 --> 00:26:33,320
Speaker 1: or I can't remember other than from playing an Assassin's

469
00:26:33,320 --> 00:26:35,520
Speaker 1: Creed game where Da Vinci was the guy who made

470
00:26:35,560 --> 00:26:37,720
Speaker 1: He was like your que to your to your James

471
00:26:37,720 --> 00:26:39,800
Speaker 1: Bond and he made one of those things for you

472
00:26:39,880 --> 00:26:42,720
Speaker 1: to fly around in. Oh nice. Yeah. I don't think

473
00:26:42,720 --> 00:26:46,160
Speaker 1: there's any evidence that da Vinci made a working flying machine.

474
00:26:46,440 --> 00:26:49,000
Speaker 1: I think he just yeah, I don't think right, Yeah,

475
00:26:49,000 --> 00:26:50,840
Speaker 1: I don't think it was. And we'll have to ask

476
00:26:50,920 --> 00:26:55,080
Speaker 1: Jonathan Strickland. He would probably know. Uh So The next

477
00:26:55,119 --> 00:26:58,720
Speaker 1: one here, and this is a short one, is the

478
00:26:58,880 --> 00:27:02,399
Speaker 1: Literature Prize, and it specifically goes to a paper called

479
00:27:03,080 --> 00:27:08,679
Speaker 1: is Huh a Universal Word? Conversational Infrastructure and the Convergent

480
00:27:08,720 --> 00:27:16,160
Speaker 1: Evolution of Linguistic Items? By Mark Dinge Manze, Francisco Torrieira,

481
00:27:16,520 --> 00:27:20,520
Speaker 1: and Nick j Enfield Uh. And this was in p

482
00:27:20,800 --> 00:27:25,520
Speaker 1: l OS one published. Okay, so the basics of this

483
00:27:25,800 --> 00:27:30,600
Speaker 1: study are essentially that the writers involved take a look

484
00:27:30,640 --> 00:27:35,840
Speaker 1: at the the word sound huh. Specifically throughout the piece

485
00:27:36,000 --> 00:27:40,159
Speaker 1: they spell it h u h question mark. Yeah, I

486
00:27:40,240 --> 00:27:42,919
Speaker 1: asked to have the question mark. They used a phrase

487
00:27:43,000 --> 00:27:46,439
Speaker 1: to describe it that I thought was very appropriate and

488
00:27:46,560 --> 00:27:50,640
Speaker 1: very interesting. They called it a repair initiator. Yeah. I'll

489
00:27:50,680 --> 00:27:52,520
Speaker 1: get to that in a second. But that's essentially the

490
00:27:53,040 --> 00:27:56,120
Speaker 1: gist of their argument and and why it's important. So

491
00:27:57,080 --> 00:28:03,440
Speaker 1: they argue, one that is universal, regardless of language, all

492
00:28:03,560 --> 00:28:08,280
Speaker 1: human beings use a version of huh. And then the

493
00:28:08,359 --> 00:28:11,439
Speaker 1: second thing they argue is that it is a word.

494
00:28:11,760 --> 00:28:13,960
Speaker 1: It's not just a grunt. Yeah. Apparently there was some

495
00:28:14,200 --> 00:28:18,600
Speaker 1: uh people, some conflict apparently about whether or not it

496
00:28:18,680 --> 00:28:20,800
Speaker 1: was a word or not. And they defined it because

497
00:28:20,800 --> 00:28:24,040
Speaker 1: they said it's used similarly across cultures, and it's also

498
00:28:24,080 --> 00:28:27,720
Speaker 1: a learned, conventional form. And like you said, it's not grunting,

499
00:28:27,760 --> 00:28:31,200
Speaker 1: it's not crying, it's not just a noise. It's a

500
00:28:31,240 --> 00:28:33,359
Speaker 1: word that we use that has purpose to it, right,

501
00:28:33,560 --> 00:28:36,360
Speaker 1: It has a it has a semantic definition. It it

502
00:28:36,440 --> 00:28:39,800
Speaker 1: means something in conversation. It means I didn't understand what

503
00:28:39,880 --> 00:28:43,760
Speaker 1: you just said exactly so. And that's where the other

504
00:28:43,840 --> 00:28:47,720
Speaker 1: initiated repair thing comes in, right, So huh is used

505
00:28:47,760 --> 00:28:51,040
Speaker 1: for us to express to someone else, whether they speak

506
00:28:51,080 --> 00:28:54,120
Speaker 1: the same language or not, that there is something going

507
00:28:54,160 --> 00:28:57,600
Speaker 1: on where we're having trouble understanding, right, whether it's like

508
00:28:59,720 --> 00:29:03,200
Speaker 1: some all understanding or actual problem with the noise in

509
00:29:03,280 --> 00:29:06,240
Speaker 1: your ears, you know the problem hearing it now. They

510
00:29:06,280 --> 00:29:10,120
Speaker 1: found that across different languages it wasn't always pronounced exactly

511
00:29:10,160 --> 00:29:12,880
Speaker 1: the same. That it's sort of the essence of the

512
00:29:12,920 --> 00:29:15,520
Speaker 1: word hah that comes through yeah. And it has the

513
00:29:15,600 --> 00:29:17,920
Speaker 1: same variation in form as you would find in any

514
00:29:17,960 --> 00:29:21,920
Speaker 1: other regular words. So as words kind of spread across

515
00:29:22,000 --> 00:29:26,200
Speaker 1: cultures and they change slightly, uh, Like they use dog

516
00:29:26,440 --> 00:29:29,480
Speaker 1: as an example, and how dog changes across different cultures,

517
00:29:29,560 --> 00:29:31,600
Speaker 1: huh is similar. Well, I mean even in our own

518
00:29:31,680 --> 00:29:33,920
Speaker 1: usage of it. You have huh, but then you also

519
00:29:33,920 --> 00:29:37,000
Speaker 1: have yeah. Yeah, so I mean we have I think

520
00:29:37,040 --> 00:29:39,760
Speaker 1: multiple versions in English exactly. Yeah. And the way that

521
00:29:39,800 --> 00:29:43,040
Speaker 1: they conducted the study is and I feel bad for

522
00:29:43,080 --> 00:29:46,160
Speaker 1: whoever had to sit down and go through all this data. Uh.

523
00:29:46,200 --> 00:29:49,960
Speaker 1: They compiled data from published literature and thirty one languages

524
00:29:50,080 --> 00:29:52,440
Speaker 1: to see how it was used in written form, And

525
00:29:52,480 --> 00:29:56,400
Speaker 1: then they collected data from recordings of naturally occurring conversations

526
00:29:56,440 --> 00:30:01,320
Speaker 1: from ten languages across five different continents, and basically, you know,

527
00:30:02,200 --> 00:30:06,000
Speaker 1: looked for usage of huh uh and and how it

528
00:30:06,080 --> 00:30:08,840
Speaker 1: was understood. And there's the you know, I won't go

529
00:30:08,880 --> 00:30:10,320
Speaker 1: through it here too because I think it would be

530
00:30:10,360 --> 00:30:14,160
Speaker 1: kind of boring, but there's this really interesting uh cable

531
00:30:14,360 --> 00:30:17,120
Speaker 1: visual table that they applied to the study itself. It

532
00:30:17,120 --> 00:30:19,479
Speaker 1: wouldn't work well on a podcast to talk about it,

533
00:30:19,760 --> 00:30:22,640
Speaker 1: basically showing all the different languages and all their locations

534
00:30:22,640 --> 00:30:27,400
Speaker 1: across the world and how huh is used throughout them.

535
00:30:27,480 --> 00:30:31,280
Speaker 1: So of course, why is it ridiculous? Well, it's we're

536
00:30:31,320 --> 00:30:34,520
Speaker 1: studying such a basic part of human existence that seems

537
00:30:34,560 --> 00:30:37,000
Speaker 1: like it doesn't really require that deep of an analysis,

538
00:30:37,160 --> 00:30:40,240
Speaker 1: right uh. And and it's also, you know, just kind

539
00:30:40,240 --> 00:30:43,920
Speaker 1: of a silly The term itself means I don't understand,

540
00:30:44,600 --> 00:30:46,920
Speaker 1: and here we are speaking to understand. Yeah, but they

541
00:30:47,000 --> 00:30:49,080
Speaker 1: make a good case. I think that this is an

542
00:30:49,120 --> 00:30:53,160
Speaker 1: interesting thing to study, especially because they point out how

543
00:30:53,240 --> 00:30:58,360
Speaker 1: huh is such a useful and powerful tool in conversation

544
00:30:58,520 --> 00:31:02,240
Speaker 1: because it has this uh. It has this complex sort

545
00:31:02,280 --> 00:31:06,040
Speaker 1: of conversation management role that happens in such a short

546
00:31:06,120 --> 00:31:10,640
Speaker 1: time span and really helps keep conversations on track and

547
00:31:11,040 --> 00:31:13,920
Speaker 1: moving quickly because you don't have to stop every time

548
00:31:13,960 --> 00:31:16,840
Speaker 1: and say I don't understand what you just said. Right. Yeah,

549
00:31:17,440 --> 00:31:21,200
Speaker 1: It's It's interesting though, because it's easy to dismiss hunt

550
00:31:21,400 --> 00:31:25,200
Speaker 1: as um, it's just a needless break in a conversation.

551
00:31:25,280 --> 00:31:28,320
Speaker 1: But but I guess the the the the idea here

552
00:31:28,400 --> 00:31:31,040
Speaker 1: is that it's as small a break as is necessary.

553
00:31:31,240 --> 00:31:34,040
Speaker 1: It's not a verbal pause because it has meaning to it.

554
00:31:34,080 --> 00:31:36,600
Speaker 1: But yeah, it's a very short, quick, efficient way to

555
00:31:36,640 --> 00:31:40,680
Speaker 1: say I don't get it. Uh. It's feedback essentially, it

556
00:31:40,720 --> 00:31:45,080
Speaker 1: with within conversation. Just let the other person you're speaking

557
00:31:45,120 --> 00:31:48,280
Speaker 1: with no that you need more. Though, this also made

558
00:31:48,280 --> 00:31:50,920
Speaker 1: me think about how I feel like the word huh

559
00:31:50,960 --> 00:31:55,160
Speaker 1: has multiple different meanings in English. It certainly is what

560
00:31:55,200 --> 00:31:59,640
Speaker 1: they're talking about the repair initiators. So you're in the

561
00:31:59,640 --> 00:32:01,720
Speaker 1: middle of saying something I miss here part of it,

562
00:32:01,800 --> 00:32:05,840
Speaker 1: say huh and you repeat. But there's also I noticed

563
00:32:05,960 --> 00:32:11,360
Speaker 1: I often use huh as a punctuation that essentially means

564
00:32:11,400 --> 00:32:13,880
Speaker 1: that's curious. Yeah, that's I feel like that's my primary

565
00:32:14,040 --> 00:32:17,400
Speaker 1: usage of it is someone will say something interesting, especially

566
00:32:17,440 --> 00:32:20,560
Speaker 1: if it's like an audio interview over the phone, and

567
00:32:20,600 --> 00:32:22,560
Speaker 1: you feel like you need to give some feedback that

568
00:32:22,640 --> 00:32:27,760
Speaker 1: you are interested, and still on the line, you go, yeah.

569
00:32:28,280 --> 00:32:30,960
Speaker 1: It can also mean now I'm waiting for the listeners

570
00:32:30,960 --> 00:32:33,520
Speaker 1: to send us in like a super cut of all

571
00:32:33,560 --> 00:32:36,920
Speaker 1: the times we've all gone to each other. Can do that?

572
00:32:38,840 --> 00:32:41,280
Speaker 1: You know that Nolan makes electronic music out of our

573
00:32:41,600 --> 00:32:44,800
Speaker 1: verbal pauses and weird mouth noises. Right, No, but I'm

574
00:32:44,800 --> 00:32:47,880
Speaker 1: not surprised. Yeah, he keeps a folder and one day

575
00:32:47,920 --> 00:32:51,520
Speaker 1: he's going to get rich and famous by using all

576
00:32:51,520 --> 00:32:55,280
Speaker 1: of ours. Well, there's there's one other thing that I

577
00:32:55,360 --> 00:32:57,440
Speaker 1: just want to add about this study, and this is

578
00:32:58,040 --> 00:33:01,040
Speaker 1: they do a little kind of naval gazing dive here

579
00:33:01,040 --> 00:33:04,120
Speaker 1: in linguistic studies. But it's important because we've actually talked

580
00:33:04,120 --> 00:33:06,080
Speaker 1: about stuff like this before, especially when we did that

581
00:33:06,160 --> 00:33:11,200
Speaker 1: Feral Children episode on the Unlanguaged Mind. They have a

582
00:33:11,280 --> 00:33:16,320
Speaker 1: kind of universal grammar post structuralist argument going on, or

583
00:33:16,360 --> 00:33:19,720
Speaker 1: at least they get into that stuff by saying, huh,

584
00:33:19,840 --> 00:33:22,920
Speaker 1: isn't part of our genetic makeup? They're not. Just because

585
00:33:22,960 --> 00:33:26,040
Speaker 1: we all use it doesn't mean it's like built into

586
00:33:26,120 --> 00:33:31,560
Speaker 1: the human code, right, It's not like screaming in pain, Yeah, exactly.

587
00:33:31,720 --> 00:33:35,000
Speaker 1: And that Rather, what they're saying is that it's the

588
00:33:35,040 --> 00:33:39,240
Speaker 1: result of convergent cultural evolution, that as we as humans

589
00:33:39,320 --> 00:33:42,840
Speaker 1: have evolved together and built up our societies together, this

590
00:33:42,960 --> 00:33:46,080
Speaker 1: word has been it's it's like this one. I mean,

591
00:33:46,120 --> 00:33:48,680
Speaker 1: I'm sure there's many other constants, but it is one

592
00:33:48,760 --> 00:33:54,680
Speaker 1: constant that is between all of these cultures. Mhm. Hey,

593
00:33:54,680 --> 00:33:56,840
Speaker 1: so I think it's time to take a quick break

594
00:33:56,880 --> 00:34:00,120
Speaker 1: to hear about our sponsor for this episode. But after out,

595
00:34:00,160 --> 00:34:03,960
Speaker 1: we're gonna come right back and talk about more weird science.

596
00:34:08,320 --> 00:34:10,799
Speaker 1: All right. Well, well, let's move on to another prize here.

597
00:34:10,840 --> 00:34:14,520
Speaker 1: What do we have in terms of management? The Ignobile

598
00:34:14,920 --> 00:34:17,360
Speaker 1: Management Prize? I got a good one for you guys.

599
00:34:17,440 --> 00:34:21,040
Speaker 1: This is this is especially Oh, this is gonna hit

600
00:34:21,040 --> 00:34:22,799
Speaker 1: a little close to home for for those of us

601
00:34:22,840 --> 00:34:26,160
Speaker 1: who work in the digital media field. Uh. So this

602
00:34:26,239 --> 00:34:29,600
Speaker 1: is a paper uh that is called what Doesn't Kill

603
00:34:29,680 --> 00:34:34,240
Speaker 1: You Will Only make You More? Risk Loving colin Early

604
00:34:34,400 --> 00:34:38,719
Speaker 1: Life Disasters and CEO Behavior. It was written in September

605
00:34:39,680 --> 00:34:42,400
Speaker 1: published rather Uh and let me see if I can

606
00:34:42,400 --> 00:34:45,200
Speaker 1: pronounce these names. So I'm gonna butcher these but sorry,

607
00:34:45,239 --> 00:34:50,680
Speaker 1: guys if you're listening. Jennaro Berneil Vanite bag Wat and

608
00:34:51,040 --> 00:34:55,359
Speaker 1: p Rugga Vendra raw I believe are their names. So

609
00:34:55,440 --> 00:34:59,200
Speaker 1: this study finds that CEOs who have experienced fatal disasters

610
00:34:59,400 --> 00:35:03,960
Speaker 1: without stream negative consequences are more likely to be risky

611
00:35:04,000 --> 00:35:08,640
Speaker 1: and aggressive businessmen. So I'm assuming by extremely fatal disasters here,

612
00:35:08,680 --> 00:35:12,080
Speaker 1: that's an extremely fatal to people that are not him,

613
00:35:12,280 --> 00:35:15,040
Speaker 1: and they can they're still alive. Yeah, they have to be.

614
00:35:15,040 --> 00:35:18,160
Speaker 1: They're not undead sea. But then also they haven't lost

615
00:35:18,320 --> 00:35:21,359
Speaker 1: loved ones exactly. Yeah, that's that's the gist of it,

616
00:35:21,400 --> 00:35:25,160
Speaker 1: is that they whether the event had fatal consequences or not,

617
00:35:25,880 --> 00:35:29,399
Speaker 1: or they all do, but it didn't affect them directly. Now,

618
00:35:29,440 --> 00:35:32,400
Speaker 1: by your use of the term businessmen, does this indicate

619
00:35:32,440 --> 00:35:34,600
Speaker 1: that all of the people studied in this were male.

620
00:35:35,320 --> 00:35:38,279
Speaker 1: You know, I'm pretty sure that they were. Yeah. I

621
00:35:39,680 --> 00:35:42,200
Speaker 1: can't say definitively, but I want to say throughout the

622
00:35:42,239 --> 00:35:47,120
Speaker 1: paper that they used the pronoun him a lot. But

623
00:35:47,440 --> 00:35:49,640
Speaker 1: you know, there wasn't any discussion of gender as far

624
00:35:49,680 --> 00:35:52,560
Speaker 1: as I can remember, So yeah, but it's safe to

625
00:35:52,600 --> 00:35:57,520
Speaker 1: assume that they were mostly male. Uh. There is also

626
00:35:57,600 --> 00:36:01,920
Speaker 1: another part of this argument, which is that, uh. There

627
00:36:01,960 --> 00:36:07,240
Speaker 1: they also found that similarly, CEOs who witnessed extreme negative

628
00:36:07,280 --> 00:36:11,200
Speaker 1: consequences during a natural disaster, so like probably seeing their

629
00:36:11,239 --> 00:36:14,200
Speaker 1: loved ones die in front of them, are more conservative

630
00:36:14,400 --> 00:36:18,400
Speaker 1: in business. This includes how they deal with cash holdings, leverage,

631
00:36:18,400 --> 00:36:22,719
Speaker 1: and acquisitions, whereas the other guys are more likely to

632
00:36:22,800 --> 00:36:25,840
Speaker 1: create debt rather than equity, and they're more likely to

633
00:36:25,880 --> 00:36:28,879
Speaker 1: make their companies go through bankruptcy. Hold on, how many

634
00:36:28,960 --> 00:36:31,319
Speaker 1: ceo s did they find who had been through a

635
00:36:31,400 --> 00:36:34,920
Speaker 1: natural disaster? Well, let me tell you about their methodology,

636
00:36:34,920 --> 00:36:39,760
Speaker 1: which I think is a little although okay, I should

637
00:36:39,760 --> 00:36:42,279
Speaker 1: back up with that. The way the way that they

638
00:36:42,320 --> 00:36:45,240
Speaker 1: gathered their information is a little weird to me. However,

639
00:36:45,280 --> 00:36:49,520
Speaker 1: they put it through rigorous, rigorous testing to make sure

640
00:36:49,560 --> 00:36:53,799
Speaker 1: that they their data was I guess accurate. Okay, but well,

641
00:36:54,000 --> 00:36:55,680
Speaker 1: why don't you guys judge by the time we get

642
00:36:55,719 --> 00:36:57,160
Speaker 1: to the end of this and tell me what you think.

643
00:36:57,680 --> 00:36:59,960
Speaker 1: So the first thing that they do is they look

644
00:37:00,480 --> 00:37:04,480
Speaker 1: at a sample of firms from to two thousand and

645
00:37:04,560 --> 00:37:07,520
Speaker 1: twelve and they identified the CEOs of all these companies

646
00:37:07,760 --> 00:37:10,720
Speaker 1: and found their basic demographic information, you know, their age,

647
00:37:10,800 --> 00:37:13,560
Speaker 1: where they grew up, where they were born, YadA, YadA. Right.

648
00:37:14,160 --> 00:37:17,879
Speaker 1: Then they cross reference that with a database of all

649
00:37:17,960 --> 00:37:20,719
Speaker 1: the natural disasters that happened in the United States during

650
00:37:20,719 --> 00:37:26,960
Speaker 1: those times, and these include earthquakes, volcanic eruptions, tsunamis, hurricanes, tornadoes,

651
00:37:27,200 --> 00:37:33,560
Speaker 1: severe storms, floods, landslides, and wildfires. And they specifically focused

652
00:37:33,760 --> 00:37:36,400
Speaker 1: on the period of time when these CEOs would be

653
00:37:36,400 --> 00:37:40,200
Speaker 1: between the ages of five and fifteen, because they say

654
00:37:40,280 --> 00:37:43,759
Speaker 1: that medical research shows that this is when our most

655
00:37:43,920 --> 00:37:49,720
Speaker 1: lasting childhood memories tend to start and then stop. So, okay,

656
00:37:51,120 --> 00:37:53,440
Speaker 1: the I want to pause for a second here and

657
00:37:53,520 --> 00:37:56,160
Speaker 1: say that the research itself, it's very easy to like

658
00:37:56,280 --> 00:37:59,120
Speaker 1: read and abstract and say, well, these CEOs are more

659
00:37:59,160 --> 00:38:01,560
Speaker 1: likely to do this thing, and these CEOs are less

660
00:38:01,600 --> 00:38:04,440
Speaker 1: likely to do this thing. Right, So the actual numbers

661
00:38:04,480 --> 00:38:08,360
Speaker 1: here what they call leverage ratios. They seem pretty small

662
00:38:08,400 --> 00:38:11,919
Speaker 1: to me. So the first group is three point four

663
00:38:12,040 --> 00:38:15,200
Speaker 1: percent higher than the norm, and then the second group

664
00:38:15,320 --> 00:38:19,680
Speaker 1: is negative three point seven percent lower. So to me,

665
00:38:19,719 --> 00:38:21,440
Speaker 1: this seems like it would fall into the margin of

666
00:38:21,600 --> 00:38:23,960
Speaker 1: error for many other studies that we would look at,

667
00:38:24,040 --> 00:38:26,680
Speaker 1: So you know, keep that in mind. Uh, it just

668
00:38:26,840 --> 00:38:29,560
Speaker 1: it's it seems a little low. I I was expecting

669
00:38:29,600 --> 00:38:31,400
Speaker 1: based on the conclusion that they had, it would be

670
00:38:31,440 --> 00:38:35,480
Speaker 1: a much higher percentage. To be fair, though, like I said,

671
00:38:35,560 --> 00:38:38,160
Speaker 1: they built in a lot of safeguards into this to

672
00:38:38,239 --> 00:38:41,080
Speaker 1: keep data bias and variables out of the studies results.

673
00:38:41,360 --> 00:38:45,480
Speaker 1: They even tested the robustness of their own methodology afterwards

674
00:38:45,480 --> 00:38:48,279
Speaker 1: by applying it in various forms over and over and

675
00:38:48,320 --> 00:38:52,120
Speaker 1: over again. Uh. And so they argue that this supports

676
00:38:52,160 --> 00:38:56,560
Speaker 1: the idea that CEOs who have not experienced extreme consequences

677
00:38:56,920 --> 00:39:01,759
Speaker 1: are desensitized to the negative consequences of risk. Subsequently, those

678
00:39:01,800 --> 00:39:06,040
Speaker 1: who are are more likely to be cautious. Okay, so

679
00:39:06,080 --> 00:39:09,200
Speaker 1: what do you think do you think? Like this methodology

680
00:39:09,280 --> 00:39:15,480
Speaker 1: sounds valid? You know, in the face of it, it's

681
00:39:15,520 --> 00:39:19,880
Speaker 1: weird because the study definitely matches up with the stereotype

682
00:39:19,920 --> 00:39:22,160
Speaker 1: that we a lot of us carry around for the

683
00:39:22,239 --> 00:39:25,799
Speaker 1: sort of inhuman CEOs that we that we ultimately have

684
00:39:25,960 --> 00:39:29,799
Speaker 1: very little personal interaction with. And they they're just this,

685
00:39:29,920 --> 00:39:32,640
Speaker 1: they seem to be this individual has no connection with

686
00:39:32,760 --> 00:39:35,279
Speaker 1: the suffering of others, right, And it seems like we,

687
00:39:35,880 --> 00:39:38,399
Speaker 1: I don't know, we should always be especially careful about

688
00:39:38,440 --> 00:39:42,879
Speaker 1: scientific results that seem to confirm our intuitions because we're

689
00:39:43,000 --> 00:39:46,200
Speaker 1: I don't know, we're just susceptible to being lenient with them.

690
00:39:46,640 --> 00:39:49,160
Speaker 1: That's why I thought that there was a sort of

691
00:39:49,200 --> 00:39:52,759
Speaker 1: inherent ridiculousness to this for the Ignoble prizes rights, it

692
00:39:52,800 --> 00:39:56,400
Speaker 1: seems like it's looking for causality between two seemingly unrelated

693
00:39:56,440 --> 00:40:00,120
Speaker 1: experiences just to confirm our pre existing bias. But uh,

694
00:40:00,440 --> 00:40:02,319
Speaker 1: like I said, I mean, I didn't go into it

695
00:40:02,360 --> 00:40:06,560
Speaker 1: here because again, like it's very dense uh and math

696
00:40:06,840 --> 00:40:10,080
Speaker 1: essentially that they that they went through here. I don't.

697
00:40:11,080 --> 00:40:14,120
Speaker 1: I have to respect them for the diligence that they

698
00:40:14,160 --> 00:40:17,239
Speaker 1: did in the study, but I don't know necessarily that

699
00:40:17,320 --> 00:40:20,839
Speaker 1: all that diligence adds up to being able to say

700
00:40:20,880 --> 00:40:24,880
Speaker 1: that this is accurate. What I would rather see is

701
00:40:24,920 --> 00:40:29,799
Speaker 1: a study that correlates CEO risk taking behavior with how

702
00:40:29,840 --> 00:40:34,040
Speaker 1: they play the board game risk. Well, are they a

703
00:40:34,040 --> 00:40:38,479
Speaker 1: table flipper? Oh nice? Yeah, well, you know, suggest that

704
00:40:38,680 --> 00:40:41,040
Speaker 1: to the board and maybe they can do that for

705
00:40:41,080 --> 00:40:43,640
Speaker 1: the next for next year's I Nobel Prizes, although those

706
00:40:43,640 --> 00:40:47,560
Speaker 1: are probably getting published already. Uh, but there is all right,

707
00:40:47,760 --> 00:40:52,040
Speaker 1: So all that aside is an important study though, in

708
00:40:52,080 --> 00:40:54,880
Speaker 1: the sense that they found a link between CEOs and

709
00:40:54,960 --> 00:40:59,120
Speaker 1: disaster experience and their corporate policies, and subsequently this has

710
00:40:59,160 --> 00:41:01,920
Speaker 1: real economic consequences. Right, So basically what they're trying to

711
00:41:01,960 --> 00:41:04,000
Speaker 1: do is say, like, all right, this is a predictor

712
00:41:04,560 --> 00:41:07,880
Speaker 1: I assume for hiring or you know, in in some

713
00:41:07,960 --> 00:41:11,719
Speaker 1: sense a predictor of how well this person is going

714
00:41:11,760 --> 00:41:14,239
Speaker 1: to run a company. Yeah. I do think one thing

715
00:41:14,280 --> 00:41:17,279
Speaker 1: that's useful here is it's just one more thing that

716
00:41:17,440 --> 00:41:20,759
Speaker 1: helps us remember that ceo s and the people who

717
00:41:20,800 --> 00:41:25,200
Speaker 1: make top level business decisions are human, and they're very

718
00:41:25,280 --> 00:41:30,560
Speaker 1: much subject to human biases prejudices towards certain types of actions.

719
00:41:30,880 --> 00:41:34,520
Speaker 1: They're not. I think there's sometimes this, uh, at least

720
00:41:34,520 --> 00:41:36,840
Speaker 1: in the pro business mindset, there is this type of

721
00:41:36,880 --> 00:41:42,400
Speaker 1: thinking that these people are efficiency optimizing machines who just

722
00:41:42,600 --> 00:41:45,160
Speaker 1: you know, they make the decision that makes the most

723
00:41:45,280 --> 00:41:47,960
Speaker 1: money all the time. But I mean there are people there.

724
00:41:48,000 --> 00:41:52,680
Speaker 1: They make emotion driven the decisions they they are informed

725
00:41:52,719 --> 00:41:56,160
Speaker 1: by their experiences and their feelings about the world based

726
00:41:56,200 --> 00:41:59,160
Speaker 1: on the things that have happened to them. It's good

727
00:41:59,160 --> 00:42:00,919
Speaker 1: to keep that in mind. Then this is one more

728
00:42:01,480 --> 00:42:05,160
Speaker 1: tally in that corner. Yeah, and plus it also it's

729
00:42:05,160 --> 00:42:08,360
Speaker 1: another study that looks at how Evan shape who we

730
00:42:08,400 --> 00:42:11,440
Speaker 1: are um and in this in this case, it reminds

731
00:42:11,440 --> 00:42:14,799
Speaker 1: me a lot of discussions I've had with people about comedians,

732
00:42:14,840 --> 00:42:19,040
Speaker 1: like you have a comedian who didn't who is Jimmy

733
00:42:19,040 --> 00:42:21,920
Speaker 1: Fallon comes to mind. I remember hearing an interview with

734
00:42:21,960 --> 00:42:25,320
Speaker 1: Jimmy Fallon where he basically, you know, he wasn't picked

735
00:42:25,360 --> 00:42:27,279
Speaker 1: on as a kid. He had pretty much had a

736
00:42:27,320 --> 00:42:30,400
Speaker 1: nice rhyn of things. And maybe that explains why I

737
00:42:30,440 --> 00:42:33,960
Speaker 1: don't find Jimmy Fallon all that funny, because he's not

738
00:42:34,040 --> 00:42:38,719
Speaker 1: coming from this place of bankst or suffering like comedians

739
00:42:38,760 --> 00:42:43,000
Speaker 1: that you identify with. Yeah, he's not. He's not Schopenhauer's comedian.

740
00:42:43,400 --> 00:42:46,600
Speaker 1: So maybe he's the CEO of of comedy, you know.

741
00:42:47,280 --> 00:42:50,000
Speaker 1: As it lines up with this study, Wow, Oh man,

742
00:42:50,200 --> 00:42:52,759
Speaker 1: you just really blew my mind with that Robert like

743
00:42:52,880 --> 00:42:56,279
Speaker 1: I am. That gives me pause because I really don't

744
00:42:56,320 --> 00:42:59,560
Speaker 1: like Jimmy Fallon's humor. Um, I'm gonna have to think

745
00:42:59,600 --> 00:43:01,440
Speaker 1: further on this and come back on another episode. And

746
00:43:01,520 --> 00:43:04,320
Speaker 1: I'm not saying I dislike Jimmy Fallone. Seems like a

747
00:43:04,400 --> 00:43:07,360
Speaker 1: nice guy, but like Comedy has never spoken to just

748
00:43:07,440 --> 00:43:09,560
Speaker 1: this comedy. Yeah, I don't know him as an individual.

749
00:43:10,239 --> 00:43:12,480
Speaker 1: He's like a CEO. I don't know those guys in

750
00:43:14,160 --> 00:43:18,480
Speaker 1: he stole my car once. Really well, here's what I

751
00:43:18,520 --> 00:43:21,640
Speaker 1: think that I got from this study is that what

752
00:43:21,680 --> 00:43:24,320
Speaker 1: we need to do is make sure that all people

753
00:43:24,360 --> 00:43:27,880
Speaker 1: who become CEOs of companies saw their parents murdered in

754
00:43:27,920 --> 00:43:30,080
Speaker 1: front of them when they were children, because then they'll

755
00:43:30,120 --> 00:43:32,400
Speaker 1: be like Batman Bruce Wayne, and they will run the

756
00:43:32,400 --> 00:43:34,920
Speaker 1: company really well and clean up all the crime in

757
00:43:34,960 --> 00:43:37,720
Speaker 1: their city. Is there any evidence of support the idea

758
00:43:37,760 --> 00:43:40,919
Speaker 1: that Bruce Wayne was was Slashy is a good CEO? Though,

759
00:43:41,000 --> 00:43:43,400
Speaker 1: We're gonna have to do some contextual analysis to figure

760
00:43:43,440 --> 00:43:46,440
Speaker 1: that one out. In fact, I would I would be

761
00:43:46,480 --> 00:43:50,560
Speaker 1: interested as anyone looked into the various CEOs in the

762
00:43:50,600 --> 00:43:52,840
Speaker 1: comic book world. He's a better CEO, is it like

763
00:43:53,640 --> 00:43:56,600
Speaker 1: or is it Wayne? Yeah? Just in terms of a

764
00:43:56,680 --> 00:43:59,800
Speaker 1: business perspective, Yeah, you know Tony Star, which is a

765
00:43:59,800 --> 00:44:02,200
Speaker 1: bad or industry go to working like, who is it like?

766
00:44:02,280 --> 00:44:06,439
Speaker 1: Sometimes having an evil corporation over your shoulder if they're

767
00:44:06,560 --> 00:44:08,239
Speaker 1: if they have could benefit. I was gonna say that

768
00:44:08,360 --> 00:44:12,200
Speaker 1: the benefits at lex Corp Are great, Great, They've got

769
00:44:12,320 --> 00:44:16,560
Speaker 1: fantastic four O one K, nice hs A package. I mean,

770
00:44:16,640 --> 00:44:21,960
Speaker 1: business is good when you just manufacture toxic chemicals. Yeah, yeah, exactly. Well, okay,

771
00:44:21,960 --> 00:44:25,000
Speaker 1: getting back to the seriousness of this though. Um, the

772
00:44:25,040 --> 00:44:29,520
Speaker 1: researchers were specifically interested in the strength of the dosage of,

773
00:44:29,840 --> 00:44:32,880
Speaker 1: you know, experiencing these natural disasters and what their effects

774
00:44:32,880 --> 00:44:34,840
Speaker 1: would be. And this is a quote from the study.

775
00:44:34,840 --> 00:44:38,239
Speaker 1: They say the intensity of life experiences can result in

776
00:44:38,360 --> 00:44:41,799
Speaker 1: nonlinear effects on subsequent risk taking. All right, we have

777
00:44:41,880 --> 00:44:43,759
Speaker 1: one more study to look at in this episode, and

778
00:44:43,800 --> 00:44:46,160
Speaker 1: I believe this one to you again, Christian, the two

779
00:44:46,160 --> 00:44:50,160
Speaker 1: thousand fifteen Ignobile Prize for Economics. Yeah, so this actually

780
00:44:50,239 --> 00:44:56,560
Speaker 1: doesn't go to an academic paper. This Uh, this is

781
00:44:56,600 --> 00:44:58,360
Speaker 1: a pretty funny one. But also I can I can

782
00:44:58,400 --> 00:45:01,880
Speaker 1: see why they picked it. Uh, the organizers awarded the

783
00:45:02,000 --> 00:45:07,960
Speaker 1: Thai Land the Thailand Metro Police directly for their efforts

784
00:45:08,000 --> 00:45:13,000
Speaker 1: to combat rampant bribe taking among traffic police. So apparently

785
00:45:13,400 --> 00:45:16,880
Speaker 1: in Thailand it's not all that uncommon for you know,

786
00:45:16,920 --> 00:45:20,640
Speaker 1: if you break the traffic rules, police pulls you over

787
00:45:20,680 --> 00:45:22,640
Speaker 1: and you just throw them like three or four bucks

788
00:45:22,719 --> 00:45:24,680
Speaker 1: or whatever and say, hey, let's why do you forget

789
00:45:24,719 --> 00:45:28,080
Speaker 1: about this? Okay, So to combat the rampant bribe taking

790
00:45:28,120 --> 00:45:31,120
Speaker 1: that's going on among these traffic police, the officers in

791
00:45:31,239 --> 00:45:34,239
Speaker 1: charge said, okay, if you refuse these bribes, you will

792
00:45:34,280 --> 00:45:38,919
Speaker 1: receive a cash reward of up to ten thousand bot. So,

793
00:45:39,080 --> 00:45:41,200
Speaker 1: just for our listeners out there, ten thousand as of

794
00:45:41,280 --> 00:45:46,960
Speaker 1: today converts roughly into two eighty one dollars or comparison,

795
00:45:47,400 --> 00:45:50,759
Speaker 1: a police salary in Thailand right now is is on

796
00:45:50,880 --> 00:45:55,920
Speaker 1: average six thousand bought a month as of data, so

797
00:45:56,160 --> 00:45:59,520
Speaker 1: they make about a hundred and sixty nine dollars a month.

798
00:45:59,800 --> 00:46:02,439
Speaker 1: So you know, if they would refuse these bribes, they'd

799
00:46:02,440 --> 00:46:05,560
Speaker 1: be getting more than a month's paycheck. This raises so

800
00:46:05,600 --> 00:46:10,520
Speaker 1: many questions about it makes you wonder how much they're

801
00:46:10,520 --> 00:46:15,279
Speaker 1: making off bribes in the first place. Oh yeah, yeah, definitely. Um,

802
00:46:15,320 --> 00:46:17,120
Speaker 1: I guess I can preface this by saying that my

803
00:46:17,200 --> 00:46:19,759
Speaker 1: family lived in Thailand for a couple of years. I

804
00:46:19,800 --> 00:46:22,200
Speaker 1: didn't live there with them, but I went and visited

805
00:46:22,280 --> 00:46:25,880
Speaker 1: and stayed with them. Um, so I'm I visited Thailand.

806
00:46:25,880 --> 00:46:28,040
Speaker 1: I'm somewhat familiar with Thailand, but I can't really speak

807
00:46:28,040 --> 00:46:30,880
Speaker 1: to its you know, overall culture and the corruption and

808
00:46:30,960 --> 00:46:34,040
Speaker 1: the government. I do know, as many people do that

809
00:46:34,080 --> 00:46:39,560
Speaker 1: you know, Bangkok is infamous for uh, drugs, prostitution, you

810
00:46:39,680 --> 00:46:41,759
Speaker 1: name it, you know, and so I think that that

811
00:46:41,800 --> 00:46:44,000
Speaker 1: was kind of part of the impetus here for this

812
00:46:44,560 --> 00:46:48,160
Speaker 1: kind of weird policy that they enacted, was to clean

813
00:46:48,239 --> 00:46:53,319
Speaker 1: up the reputation of Bangkok. But of course, so it's

814
00:46:53,400 --> 00:46:56,520
Speaker 1: it's sort of ridiculous, so ridiculous that after two weeks

815
00:46:56,560 --> 00:47:00,439
Speaker 1: the police canceled this policy. Uh. There were so there's

816
00:47:00,480 --> 00:47:03,720
Speaker 1: so much back to your bribe social media criticism about

817
00:47:03,760 --> 00:47:07,719
Speaker 1: this that they thought it was damaging the forces reputation. Uh,

818
00:47:07,760 --> 00:47:13,359
Speaker 1: and two cops received the maximum award based on them

819
00:47:13,360 --> 00:47:16,600
Speaker 1: taking three dollar bribes. Yeah, well it's not rather not

820
00:47:16,680 --> 00:47:18,839
Speaker 1: taking those bribes. It's it's kind of like the whole

821
00:47:18,880 --> 00:47:23,160
Speaker 1: scenario where somebody unloads the dishwasher and then expects praise

822
00:47:23,239 --> 00:47:26,239
Speaker 1: for it, like you're supposed to unload the dishwasher. You

823
00:47:26,600 --> 00:47:30,040
Speaker 1: did your job. Yeah, you did your job. Congratulations. Yeah,

824
00:47:30,080 --> 00:47:33,480
Speaker 1: there shouldn't be a monetary reward for not taking the bribe,

825
00:47:33,760 --> 00:47:38,080
Speaker 1: which is what you're supposed to do to Kenwin. Unsurprisingly,

826
00:47:38,120 --> 00:47:41,200
Speaker 1: the Metropolitan Police Bureau did not send any representatives to

827
00:47:41,280 --> 00:47:44,480
Speaker 1: Harvard to receive the award at the ceremony. I wish

828
00:47:44,480 --> 00:47:47,080
Speaker 1: they did because that would have probably made it quite

829
00:47:47,080 --> 00:47:50,680
Speaker 1: a bit funnier. Did they not even send a video acceptance? Well,

830
00:47:50,719 --> 00:47:52,759
Speaker 1: you know what, I didn't catch that they might have,

831
00:47:53,120 --> 00:47:55,640
Speaker 1: but they didn't get to have what's her name sit

832
00:47:55,680 --> 00:47:59,440
Speaker 1: there and tell them that they're boring. Oh yeah, it's sweetie. Pooh.

833
00:47:59,680 --> 00:48:04,600
Speaker 1: I g I forgot, I forgot to Okay. Uh So,

834
00:48:04,880 --> 00:48:09,799
Speaker 1: just another thing is that Thailand also received the Ignoble

835
00:48:09,840 --> 00:48:13,680
Speaker 1: Award for Public Health because they had research that was

836
00:48:13,719 --> 00:48:19,280
Speaker 1: published about surgical solutions to involuntary penis amputation. I remember

837
00:48:19,280 --> 00:48:22,759
Speaker 1: this one. Yeah, so you know, they're racking up the

838
00:48:22,760 --> 00:48:26,440
Speaker 1: points in the Ignoble category. But let's get serious for

839
00:48:26,480 --> 00:48:29,560
Speaker 1: a second here. Okay, here's why this is important. So

840
00:48:29,600 --> 00:48:32,520
Speaker 1: the state officials and the police in Thailand are supposed

841
00:48:32,600 --> 00:48:35,400
Speaker 1: to face life in jail if they're convicted of taking

842
00:48:35,400 --> 00:48:40,240
Speaker 1: a bride. Life in jail. Yeah, but it's pretty common,

843
00:48:40,400 --> 00:48:45,560
Speaker 1: it's extremely common. Uh. Though. The Thai army took power

844
00:48:45,680 --> 00:48:50,879
Speaker 1: after ousting the first female prime minister uh and they

845
00:48:50,920 --> 00:48:53,600
Speaker 1: claimed that they had to restore order to the nation

846
00:48:53,840 --> 00:48:58,400
Speaker 1: because nearly thirty people were killed in political protests against

847
00:48:58,440 --> 00:49:01,520
Speaker 1: her being elected. Okay, so that's where this all came from.

848
00:49:02,200 --> 00:49:05,640
Speaker 1: Uh And after they took power, that's when they launched

849
00:49:05,640 --> 00:49:09,480
Speaker 1: this campaign to clean up Thailand's image. So this measure,

850
00:49:10,040 --> 00:49:14,600
Speaker 1: the traffic bribe measure, was part of that campaign. Uh

851
00:49:14,600 --> 00:49:19,640
Speaker 1: And specifically because that prime minister's brother is an ousted

852
00:49:19,719 --> 00:49:23,960
Speaker 1: and influential politician who is also a former police officer,

853
00:49:24,320 --> 00:49:26,800
Speaker 1: and he's known to have allies in the police force

854
00:49:26,880 --> 00:49:29,359
Speaker 1: and he's got some you know, political influence with them.

855
00:49:29,600 --> 00:49:31,680
Speaker 1: He fled the country in two thousand and eight. But

856
00:49:31,920 --> 00:49:34,800
Speaker 1: really the speculation is that, like, if you look deeper

857
00:49:34,840 --> 00:49:37,200
Speaker 1: at this, this is more about the military trying to

858
00:49:37,239 --> 00:49:41,239
Speaker 1: take back control of the police from this you know,

859
00:49:41,320 --> 00:49:46,040
Speaker 1: allegedly corrupt politician. All right, Well, there you have it.

860
00:49:46,280 --> 00:49:48,520
Speaker 1: We're gonna cut off right there, and we're gonna pick

861
00:49:48,600 --> 00:49:51,440
Speaker 1: up in the next episode of Stuff to Blow Your Mind,

862
00:49:51,440 --> 00:49:54,160
Speaker 1: where will roll through five more of the winners from

863
00:49:54,200 --> 00:49:57,719
Speaker 1: the two thousand fifteen Ignoble Prizes. Uh, there's some gonna

864
00:49:57,719 --> 00:49:59,840
Speaker 1: be some fun stuff in there, So give you joined

865
00:49:59,840 --> 00:50:02,160
Speaker 1: this come back from what we're gonna talk about kissing

866
00:50:02,640 --> 00:50:05,600
Speaker 1: and how many babies you can have in a lifetime. Yeah,

867
00:50:05,680 --> 00:50:09,520
Speaker 1: we're talking about sticking essentially plungers on the butts of chickens.

868
00:50:09,640 --> 00:50:11,960
Speaker 1: It's it's gonna be. It's gonna be a golden bees

869
00:50:12,040 --> 00:50:17,200
Speaker 1: to your genitals. Science. Hey, So in the meantime, be

870
00:50:17,200 --> 00:50:18,759
Speaker 1: sure to check out stuff to bow your Mind dot com.

871
00:50:18,800 --> 00:50:20,960
Speaker 1: That's we'll find all the podcasts. You'll find videos, you'll

872
00:50:20,960 --> 00:50:23,040
Speaker 1: find blog posts, you'll find links out that those social

873
00:50:23,040 --> 00:50:26,600
Speaker 1: media accounts such as tumbler, Facebook, and Twitter. Yeah, and

874
00:50:26,840 --> 00:50:30,719
Speaker 1: that is where you can also find the address to

875
00:50:30,840 --> 00:50:34,120
Speaker 1: write into us if you were, you know, particularly inspired

876
00:50:34,160 --> 00:50:37,040
Speaker 1: by one of these papers that we discussed, or maybe

877
00:50:37,040 --> 00:50:38,600
Speaker 1: you're one of the authors of one of them and

878
00:50:38,680 --> 00:50:41,040
Speaker 1: want to communicate to us what was actually going on

879
00:50:41,120 --> 00:50:44,720
Speaker 1: with your research, or perhaps you're involved in the Ignoble Prizes.

880
00:50:44,920 --> 00:50:47,080
Speaker 1: If you want to contact us directly, Joe, what's the

881
00:50:47,080 --> 00:51:00,000
Speaker 1: address below the mind at how stuff works dot com

882
00:51:00,080 --> 00:51:02,480
Speaker 1: For more on this and thousands of other topics. Is

883
00:51:02,520 --> 00:51:22,880
Speaker 1: a house stuff works dot com U