0:01

Today's rewind of Stochastic City is presented

0:03

by Radio Lab. Sponsor better Help with

0:05

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that's better Help help.com flash really

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allow. Hey,

0:34

it's like if happy New Year

0:36

luigi. You feel fresh. It's a

0:38

new you at a new me

0:41

and a new page and a

0:43

new lease on life for all

0:45

of us. And as we start

0:47

to chart out our new year.

0:50

I. Want to play an episode from

0:53

the Archives that asks an important

0:55

question that I think is helpful

0:57

to consider? You know, as we

1:00

make plans and goals and set

1:02

expectations, The here's the question. Here's

1:04

the question: How much can we

1:07

control what happens in our lives?

1:10

And how much Is it? Just

1:12

Whatever you want to call it luck

1:14

or fate, or just a random and

1:16

fickle universe having his way with

1:18

us. This. Is an episode

1:21

about that? How that applies to

1:23

he cool I bacteria how it

1:25

applies to dimes and corridors, how

1:27

it applies to blades of grass

1:29

on a golf course. And of

1:31

course how it applies. To.

1:34

You. I. Hope you enjoy. This.

1:37

Is. Still, Cast a city

1:39

of you know you were you with. Your

1:47

than listening to radio Lab. Radio

1:49

from W and Weiss

1:52

A. Lie.

1:58

Again, When start to show today with. Truly

2:00

remarkable story. Which

2:03

least initially involved this girl right

2:05

here. I'm hello. I'm nervous than

2:07

Laura Buxton surname. remember their names

2:09

and Laurel, Let's do this like

2:11

a movie. Okay, I can. Movies

2:13

I say. That

2:16

it's June. Two thousand and one. Yeah,

2:18

where we were in a little town

2:20

in Northern England called Safe and Tren

2:22

Stoke on Trent. You. Imagine.

2:25

A little English health in this

2:27

town. and the camera zooms in

2:29

and they're standing. The front lawn

2:31

is Laura Buxton. She's ten years

2:33

old. He wow, almost ten. She's

2:35

a tall girl, pretty tall flags

2:37

exhale and in her hand she's

2:39

holding. A. Balloon. Or

2:42

read to. You. Release

2:45

Of Our Pockets Or earlier that day Laura had

2:47

taken a little card and stuff into the balloon

2:49

and on one side written. My name's personal

2:51

message is a sudden m please return to

2:53

Lower Bucks Then and then on the other

2:55

side had my address. Okay so cut back

2:58

to the outdoor scene There she is standing on

3:00

lawn. Is very windy. To

3:02

get this red balloons are name on it and she

3:04

holds it up to the sky. To.

3:07

The heavens. And

3:09

at just let it go. In

3:12

the winter. Came. Through

3:16

the last. In intensive as it is thought to get stuck

3:18

in a tree. That further down the road somewhere.

3:20

That's not what happened. The balloon kept doing.

3:23

Or it Now I'm looking at a

3:26

map here of England and stuff and

3:28

has com boom want to do so

3:30

selfless. Pound down down pass Stratford past

3:33

while so he has to Wolverhampton than

3:35

pass Birmingham as Kidderminster has worked. Sister

3:37

yes has millions of people has setting

3:39

him. They ask people with different lives

3:42

differently as glove sister lost a cluster

3:44

and all in all the red balloon

3:46

goes about one hundred forty miles. South

3:49

exactly against the prevailing wind. Really

3:51

to the Southwest. with

3:55

his of finally when this blue to the all the way

3:57

on the other side of the country. It

3:59

begins to dissent. Down

4:01

Down Down. And of all the place the

4:03

good: A Landed. In a in a river

4:05

in a factory parking lot in the

4:07

see. Instead the balloon touches down. In

4:10

the yard. Of this girl. I.

4:13

Live. I. Live in the

4:15

countryside and a little funded

4:17

schools. Nelson Lowborn. Just Your Next uses

4:19

a different girl than the first one. They do sound

4:21

the same. But. They live on opposite ends of

4:24

the country. The blame got. Stuck in our heads for

4:26

our next door neighbor. found it and he thought

4:28

has disappeared of rubbish and he collected it up

4:30

say the cows wouldn't eat it because he didn't.

4:35

And he was about to that in the been

4:37

I literally. And then he saw the label saying

4:39

please send back to Nor Buxton and he was

4:41

like. Oh. My. God. Why

4:45

we resume Riga. Okay, so check this out. Rimmer,

4:47

I tell ya. The first girl who sent the

4:49

balloon with ten. The. Second girl who

4:52

received it and ten years old. She's ten. A

4:54

game. Or current winner is more

4:56

than one bit of remember. I told

4:58

the first girl's name was Laura Buxton.

5:01

Well girl number Two Country, introduce yourself.

5:04

Hi I'm love. And was

5:07

they both were of of

5:09

suggests now guess oath named

5:11

Laura Buxton. Yes, You

5:14

heard me? write. A ten year old girl

5:16

named Laura bucks and let's go over balloon.

5:19

The balloon float a hundred and forty miles

5:21

and lands. In

5:23

the yard of a ten year old girl named

5:25

Laura Buxton is is for real. I think I

5:27

might be the strange thing have ever heard in

5:29

my life. It's pretty with. Been. About eight

5:31

years since the balloon incident dolores each other a

5:33

lot. We managed to get them both and a

5:35

studio. So

5:37

like who can hear Americans through these? Okay,

5:40

back to the story. Yeah, I got the

5:42

Berlin. that's lore number two on I would

5:44

seek at that point. On

5:47

well I. I

5:49

was quite young. I didn't really know what to think.

5:51

I'll just let I'd better write the letter because he

5:53

does is someone else out local lore box and I

5:55

must see them. to learn and but you

5:57

wrote a letter to learn about one thing

6:00

Laura, I think I put, I'm 10 years old

6:02

and I live in Woodton and I found your

6:04

balloon and the thing is that my

6:06

name is Laura Buxton as well. So lots

6:08

of love from Laura Buxton. Laura

6:11

number one. Yep. You get the note. Got

6:14

it through the post. Do you remember reading it? I remember reading it

6:16

because I sort of opened it up while I was in the kitchen

6:18

and it was really quite confusing actually

6:20

because it was like to Laura Buxton from Laura

6:22

Buxton. I took it up to my mum and

6:24

we stood there arguing about it for quite a

6:26

while. What did you argue about? Well, she was

6:29

trying to tell me that it had come to

6:31

Laura Buxton and it wasn't from Laura Buxton. She

6:33

just thought I was confused. Okay,

6:36

fast forward a short while later, the two

6:38

Laura's meet. It was at one of England's

6:40

most popular TV shows, Richard and Judy. They'd

6:42

found out about the Laura Laura coincidence, invited

6:45

them on and here the story gets even

6:47

stranger because there's Laura number two standing backstage.

6:49

And down the corridor I saw this girl who looked

6:51

pretty similar to me. First thing she noticed

6:54

is, wow, with the same height. Guinea and tall. She

6:56

got the same colour hair. Brownish hair. And

6:58

we're even wearing the exact same clothes. Pink jumpers

7:00

and jeans. Yeah. Both

7:02

had on pink jumpers and jeans. Yeah. And as

7:04

they started to talk, it just kept getting weirder. Well,

7:07

we both got a three year old black Labrador. We

7:09

both got a grey rabbit. We both got guinea pigs.

7:11

Really? Yeah, and they both brought their

7:13

guinea pigs with them that day. I remember Laura took

7:15

hers out of its cage and I had mine on

7:17

my lap and we were like, oh

7:19

my God. They were identical. They

7:23

were both brown with a sort of

7:25

beigey orange patch on their bum. Like

7:28

completely the same. I was

7:32

just like, oh my gosh, how is this happening? Do

7:36

you believe in miracles? Either of you? I

7:39

don't know. Would you call this miracle? I'm not sure. I

7:41

mean, I guess it could be, but I think

7:43

it's more of a case of fate. Yeah,

7:45

I'd say it's more fate than a miracle. So

7:47

you don't think that wind that blew the balloon

7:50

was just wind? Well, if it was

7:52

just wind, it was a very, very lucky

7:55

wind. The

7:57

chances just so likely there must be some.

8:00

What kind of reason? Maybe

8:03

we were meant to meet, I don't know.

8:05

But meant by whom? Or what? I don't

8:08

really. Every time we'll tell

8:10

it could actually be like, preparedness

8:12

for something else later in life. Maybe

8:16

when we're old grannies, we'll find out. No,

8:18

I'm just young and I'm just

8:20

enjoying life. Oh,

8:24

Jed, I mean, what do you look like? You know what you

8:26

are. What? You're a

8:28

destiny bully. What are you doing?

8:30

A destiny bully? Yes, because you... Something like a pop band

8:33

or something. No, it's what you're doing to those girls. No,

8:35

I wasn't trying to force God on them, if that's what

8:37

you mean. Yes, you're the one who says, oh... No,

8:40

no, I was trying to get to the

8:42

question of how should we think about

8:44

that story? Is our world

8:46

full of magic and meaning and coolness,

8:49

or is it all just chance? In

8:51

fact, that's what we're going to do with this whole

8:53

hour of Radio Library, when you discuss the role that

8:56

chance plays in so many things. In

8:58

the lottery, in the flipping of coins, and

9:00

deep as a ball. In us. Yes.

9:04

On Radio Library. I'm Jad Abumrod. I'm Robert Krowich. We're

9:06

about to get random, so stay with us. So

9:14

let's start with a very basic question. Let's.

9:17

Random sounds like it means

9:19

random. That is, anything can

9:21

happen at the next turn of the wheel.

9:23

Like your phone ringing, for example. Oh, sorry.

9:26

Random. Sorry. Although

9:28

it happened so many times, it is no longer random.

9:30

It's completely predictable. But it does have

9:32

a very nice kind of lilt to it, don't you

9:34

think? I'm going to sing with it now. And

9:38

now back to our

9:40

regularly scheduled program. So

9:54

let's say that something remarkable

9:56

happened. Like Dolores. Like

9:58

Dolores. Can you tell? Whether

10:00

this is just the random act

10:02

of an indifferent universe, or is

10:05

there something truly miraculous and wonderful about

10:07

it? Excellent question. Thank you very much.

10:09

Hi, we found you. So

10:12

this is Chad. Hi, this is Robert.

10:14

Hi, I'm Debra Nolan. I'm a professor

10:16

of statistics at the University of California, Berkeley.

10:19

The reason we'd come to see Deb Nolan at Berkeley

10:21

is because we'd heard that she plays this game. I

10:23

like to incorporate lots of classroom

10:25

activities and demos. One in particular has

10:28

to do with randomness. It's a game that helps

10:30

her students understand what real randomness

10:32

actually looks like. I don't know anything about

10:34

that. And it doesn't look like what you

10:36

would think. They called it a...

10:38

In any case, she takes us into her

10:40

classroom. That's a nice student. And

10:43

she sits us down. We all sit down.

10:45

We sit in a semi-circle. That sounds good.

10:48

And then she explains. Okay, I'm gonna divide the

10:50

group up into two. I'm gonna divide it

10:52

right here. She splits us up so that group one is

10:54

three of her students. I'm Joe Cheng. Richard

10:56

Liang. Margaret Taub. Group two, Chad

10:59

Abumrod. Robert Krollwich. It's us. And

11:01

the group here... She's pointing at us.

11:03

I'm gonna give you a penny. And I'm

11:05

gonna ask you to flip the coin a hundred

11:08

times. And the three

11:10

of you... She points to her students. Your

11:12

job is to pretend to flip a coin.

11:15

Meaning they just have to flip the coin in their heads. How do

11:17

you think that coin might land? Produce

11:20

a hundred fake coin flips. And

11:22

then, Deb leaves

11:24

the room. So

11:30

her students start whipping through their imaginary

11:33

fake flip... Tails. Tails. Tails. Echocorp,

11:36

hallelujah. While we...

11:44

Actually flip the coin. A hundred times. Head.

11:46

Head. Tail.

11:48

Tail. Tail.

11:50

Tail. Tail.

11:53

Head. Tail. Head. Tail.

11:57

Tail. and

12:00

put our strings of H's and T's on

12:02

point zero. Point zero. Yes. Well,

12:05

and then, Deb came back. Hello.

12:09

Here they are, huh? Let's see

12:11

what you look. Okay,

12:15

so on the board, you've got two sets of H's

12:17

and T's, which look pretty much the same to

12:20

us. But she looked at their list, the fakers,

12:22

and then she looked at our list, and

12:25

right away, she says, pointing at our list? This

12:27

is the real world. We

12:29

were like, how

12:32

did she do that? Well,

12:34

amazingly, the way she knew had to

12:36

do with one particular moment. Right.

12:39

Roll the tape back, and pipe it to a

12:41

moment right at the beginning of Power Coin. Nails

12:43

in here.

12:48

Nails. Nails.

12:51

Nails. They're in a room.

12:54

What a name. Another

12:57

nail. Nails. We

13:02

have way too many nails. Seven

13:14

tails in a row. It

13:17

was really spooky. Completely. Like

13:19

at any moment, a unicorn was going to come galloping in. That's

13:22

how weird it was. But as magical and unrandom

13:24

as it felt to us, that's

13:27

how she knew that we were the real flippers. As

13:29

soon as I saw the seven tails, and

13:31

then I looked over to the other board, and

13:34

there weren't any longer than four,

13:36

I think. That's how she knew. When

13:38

we asked one of the guys on the other team, why

13:40

didn't you put more streaks in your flips?

13:42

Well, he said, what do I think we don't think?

13:45

I was thinking if we did that too much, maybe

13:47

she would recognize that we were actually doing that on

13:49

purpose. In other words, those

13:51

streaks just feel wrong. And

13:53

that's the thing about randomness. Real randomness when

13:56

you see it, just doesn't

13:58

feel random enough. Ah,

14:02

it says that the truth is strange

14:04

things do happen by chance. Well,

14:13

it is hard for us to emotionally accept this.

14:15

Well, it finally made sense to us when we

14:18

spoke to this guy. Hi, Jed. Hi, Robert. That's

14:20

Jay Kohler. I'm a professor

14:22

of finance and professor of law at Arizona State University.

14:24

So here's how the epiphany happened. We were explaining to

14:26

Jay the unicorn experience in Debs classroom. We got one

14:28

tail, then we got a second, then we got a

14:31

third, then we got a seventh. And

14:33

somewhere in the conversation we started to do the

14:35

math. Okay, what actually are the

14:37

odds? Let me see. Was it heads in a row,

14:40

tails in a row? Tails. Seven tails in a row,

14:42

that's one half raised to the seventh power.

14:44

So we started to do the calculations and at

14:46

first... It looked

14:48

pretty good. 0.00,

14:51

a little more than one percent. Just over one percent chance. Yeah.

14:54

So it seemed at first that what

14:56

had happened in Debs class was super

14:58

unlikely. Right. But

15:00

then, Soren, our producer. Yeah, Soren. Had

15:03

to go and say this. You know, to be fair,

15:05

you should tell him that you actually flipped the coin

15:07

a hundred times. Oh, now you...

15:10

Wait, wait, you were holding back on me. We're

15:13

too stupid to know that. That's why we have

15:15

Soren here. Are you saying that somewhere in the

15:17

hundred flips you got a run of seven? That's

15:19

what we're saying. That's not a particularly good coincidence.

15:21

I'm sorry to burst the voice. What did you

15:23

mean? And then Jay explained it to us. Uh,

15:27

seven... If you're just doing seven flips,

15:30

then yeah, getting seven in a row

15:32

is really unlikely. But if you're doing multiple

15:34

sets of seven, 14 of those sets of

15:36

seven, which we were doing a hundred, then

15:38

the probabilities start to add up. It starts

15:41

small, like one percent. But then that one

15:43

becomes two, which becomes four, which becomes eight,

15:45

until when it's all said and done, the

15:47

chances of getting seven tails in a row

15:49

somewhere in a set of a hundred is...

15:54

Don't hold your breath. About one

15:56

in six chance. One in six. That's it. would

16:00

have gotten a string of seven. So

16:03

what felt spooky and almost twilight zonish

16:05

in the moment is actually- Not that

16:07

improbable. Oh. See, that's

16:09

why you don't want to know it. It doesn't

16:11

confirm your goosebumps. No, I think the goosebumps are

16:14

dead now. Oh, I'm sorry to

16:16

do that. I still enjoy life. The

16:20

problem, says Jay, is that we were so

16:22

focused on those seven flips in a row that

16:24

we'd forgotten about the other 93 that

16:27

weren't seven in a row. We'd

16:29

forgotten about what he calls the

16:31

background. We were too zoomed in.

16:33

So you've got to back the camera up and

16:35

pan around and look at the complete sample space.

16:37

And when you do that, he says, what you

16:40

will realize is the thing that felt

16:42

so special. Suddenly you see that it's not so odd

16:44

in its real context. And this

16:46

sad lesson goes way beyond

16:48

coins. You gave us this

16:51

example. 1985 and 1986, Evelyn Adams of New Jersey wins

16:55

the lottery twice. Back to back years.

16:58

Crazy improbable, right? So if you zoom

17:00

in all the way in, there

17:03

she is. Evelyn Adams standing outside of a

17:05

convenience store somewhere in New Jersey. She

17:11

is completely blown away for good reason. The

17:13

odds is that those two particular tickets would

17:15

become winning lottery tickets. They're 1 in 17.3

17:17

trillion. Wow. But

17:21

Jay would say if you pan the camera back, away

17:23

from Evelyn. Bye, Evelyn. If you

17:25

look at the whole world of

17:28

people buying lottery tickets, at this

17:30

vantage point, you can begin

17:32

to ask a different question. What are the odds that

17:34

somebody somewhere would win the lottery twice?

17:38

And in fact, the answer to that is it would

17:41

be very surprising if it didn't happen repeatedly. And

17:43

it has happened repeatedly. Really? For instance,

17:45

in Connecticut. Employees

17:48

of a place called the Shuttle Meadow Country

17:50

Club, they won twice. The man in Pennsylvania,

17:52

he won twice a few years later. In

17:54

California, retirees won a Fantasy

17:56

Five and the Super Lotto in the

17:59

same day. To

20:00

be totally honest, he's right. What?

20:02

What do you mean? Well, when I was

20:05

interviewing Dolores, I asked him a bunch of

20:07

questions, kind of scouting for similarities. What's your

20:09

favorite color, both of you? Blue, pink. Scrap

20:11

that. And what do you guys study in

20:13

school? Biology, chemistry, and geography. Whereas I'm

20:15

doing English and history and classical

20:17

civilization. Scrap that. What people do

20:19

is they try to make the story better by

20:22

showing more similarities. So you're saying that

20:24

somebody, I couldn't imagine who, doctored

20:27

the story? By

20:29

the way, I don't want to spoil anything. This is a trivial comment.

20:31

But I believe that one of the girls is actually nine. Well, almost

20:33

ten. And the other one was ten. Oh,

20:41

well, that's the story through it. Never mind. Yeah, I'm sorry

20:43

to be your most depressing guest. Nonetheless,

20:45

I will continue to tell the Laura

20:48

story every chance I get on the

20:50

air at parties, wherever. Because, you know,

20:53

damn, the statistics just makes me feel good. I think

20:56

Jay would agree with you. Well, first

20:58

of all, we love stories. It

21:00

connects us. It gives us insight into our

21:02

own lives. And I think it

21:05

also gives us a feeling that life is magical.

21:09

And maybe we don't have to call it

21:11

magic to enjoy the experience. In

21:13

fact, I was talking to the Laura's and I

21:15

asked them, what if a statistician were to walk

21:17

in the room right now and say to you,

21:19

this was bound to happen. Statistically, this was going

21:21

to happen sometime to

21:23

someone. Fair enough, really, because

21:25

it just happens to be us and

21:27

those statistics. Yeah, I mean, if that's

21:30

what the statistician thinks, I mean, yeah, I gave

21:32

him to him. They don't really care. The way

21:35

they see it, whatever was in that wind, whether it was

21:37

fate or just wind, doesn't

21:39

matter. We brought them together.

21:41

And now, they're friends. Radio Lab

21:52

will continue in a moment. Fact-Check

22:00

News, making science fun and

22:03

approachable. Audiences trust our

22:05

show and our fellow WNYC

22:07

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22:09

we're driven by a mission

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22:13

first and foremost, and

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our sponsors benefit from that

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halo effect. For more information

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on becoming a sponsor, visit

22:21

sponsorship.wnyc.org. Hey,

22:24

it's Lotsof

22:27

again. Just a quick note before we get back to

22:29

the episode. The story you're about to hear was reported

22:32

back in 2009 by journalist Jonah Lehrer, who years after

22:36

that got in trouble for fabricating quotes in

22:39

one of his books. We

22:41

have fact-checked this story, everything in it

22:43

held up, just so you know that

22:45

we know and now you know, you

22:48

know. Okay, back to Jad and Robert.

22:51

Hey, I'm Chad Abumrad. And I'm Robert

22:53

Krulwich. And we are talking on Radiolab

22:55

about things stochastic. Like coin flips and

22:57

lottery tickets. But let's just push this

22:59

whole argument another step forward if we

23:02

may. Which mean? Let's talk

23:04

about human beings. Pattern

23:08

rules the brain. This one

23:10

is about a woman, I believe her

23:12

name is Anne. I'm Anne

23:14

Keinsniper. I live

23:16

in a small country town where most

23:19

people know other people. Anne was

23:21

a high school English teacher. I taught

23:23

for 31 years. She now lives in

23:26

West Virginia. Wait, can you wait just a minute,

23:28

there's someone at my door. I'm sorry. No,

23:30

no, of course. Of course. Anne was

23:32

an upstanding citizen, went to church every

23:35

Sunday. Was just one of

23:37

those people who... Makes

23:39

the world go round. Makes the world

23:41

go round. I'm sorry. Not at all.

23:43

Anyway, in 1991, I would go

23:45

to the grocery store. And on

23:48

the occasions I wrote a check

23:50

for my groceries, the

23:52

woman would say, gosh, you're shaky.

23:55

And she says she began to notice that her hands

23:57

would start to tremble. Are you alright? thought

24:00

maybe it was because of working

24:03

that hard and trying

24:05

to get everything done. And

24:07

it got particularly bad when she said she

24:09

was just walking in the mall doing some

24:11

shopping. And I was

24:13

by myself walking and

24:16

it was like I stepped off a

24:18

step that wasn't there. It

24:21

was the first full-body tremor. She

24:24

fell. And then my husband

24:26

was a doctor and he

24:28

sent me to a neurologist who

24:31

diagnosed me with Parkinson's.

24:35

How old is she, by the way? She was

24:37

at that point in her early 50s. What is

24:39

Parkinson's? Parkinson's is the death of dopamine neurons

24:41

in the back of your brain, in the

24:43

part of your brain that controls bodily movement.

24:45

And so when these neurons die, the

24:48

end result is first

24:50

the shaking hand and the loss of feeling and

24:52

the loss of movement. And

24:54

of course the tremors get worse and

24:56

worse. But anyway... Well, the doctor diagnosed

24:58

with Parkinson's and he gives her a

25:01

drug called Requip. Requip was a new

25:04

medicine in 1992. It's

25:07

a pseudo dopamine. It basically mimics dopamine

25:09

in the synapse of the cells. And

25:12

it was like a

25:14

miracle drug for me. Her

25:19

tremors disappear, her symptoms disappear.

25:23

So she's cured her? If

25:25

you looked at her on Requip, years after she

25:27

had Parkinson's, you wouldn't notice anything. She would seem

25:29

symptom free. So

25:36

about seven or eight years go by, all the while

25:38

they're upping the doses to compensate for the cell loss

25:40

that's still taking place. And in the early years of

25:42

2000, something sort of unusual happened

25:44

to Anne. The friends of mine had

25:47

gone to Las Vegas every

25:50

year for the

25:53

basketball tournament, the

25:55

Final Four type thing. Yes,

26:00

but I like to go with them. And

26:03

I said, yes, I would. So

26:05

she went to watch basketball, but as often

26:07

happens in Vegas, one afternoon she and her

26:09

friends found themselves in a casino. Had you

26:11

ever gambled before this trip to Las Vegas?

26:14

No, I was raised in a

26:16

household that was fairly

26:18

religious, and we considered

26:20

gambling a sin. But as

26:23

she stood there in the casino in Vegas, she

26:25

had this inexplicable urge to go to the

26:27

slot machines. They had frogs

26:29

and princes and cars

26:31

and cherries and lemons. We'll

26:35

sit and see what the pictures

26:37

did. I've never taken

26:39

any drags. I don't know anything to

26:42

compare it to, but it was like

26:44

a high. That

26:47

was sort of the beginning of it. And

26:50

then when she comes back to West Virginia... I couldn't

26:52

wait to get to a machine I really

26:54

wanted to play. She discovers the dog

26:57

racing track, about

26:59

15 miles away from her house. I'd go to

27:01

the 730 video when they opened. And

27:04

that's where she would go, and they had a

27:07

white sort of slot machine. Hi,

27:11

how are you? If I had

27:13

the money, I'd play all day. From

27:15

7 to 3.30 in the morning. Whoa.

27:19

And then she would go home and

27:21

play slots... On the computer. On her

27:23

computer. Not even for money. Just

27:26

for the sheer visceral thrill. I

27:29

would play that the rest of the

27:31

night. 730 the next morning,

27:34

I'd be back at the joint. Hi,

27:36

how are you? Without

27:38

any sleep at all? No sleep, and she could keep that up

27:40

for several days in a row. At

27:43

the beginning of my gambling, I'd wake up

27:45

in the night and just scream out, oh,

27:47

God, what am I doing? Help

27:49

me, save me. But

27:52

eventually, I became too

27:54

hard-hearted, I guess, to even pay

27:56

attention to that. Her credit cards

27:58

are all maxed out. I sold my

28:01

mother's silver, I sold my silverware.

28:03

Things that should have been my

28:05

son's heirlooms. Stole from the safety

28:08

deposit box. She steals quarters from

28:10

her grandkids. Steals quarters from her

28:12

grandkids. Yeah. Anything I looked

28:14

at around the house, I thought I could

28:16

get money out of. Everyone

28:19

who knows her is watching her life fall apart. My

28:21

house was filthy dirty

28:23

and mess. I would take time

28:26

to eat in wash dishes. She

28:28

lives on peanut butter. I didn't have any

28:30

crackers or bread or anything. I just

28:32

had peanut butter. Because that's all she

28:34

can afford and still leave as much money as possible for

28:36

the slots. Even when I'd be at

28:38

church, I'd think, well, so many more

28:40

minutes or so many more hours I

28:42

can get a gamble. Her husband eventually

28:45

leaves her. I mean, I loved my husband,

28:47

but. They got divorced. There's just

28:50

no decision. Everything is gambling.

28:55

One of the neat things about gambling is

28:57

that you can do it by yourself. How

29:03

much money did you lose during those years, if

29:06

you don't mind me asking? I lost at

29:08

least $300,000. Which

29:13

to her is? Is all your life savings. And

29:15

it's one quarter at a time. Yeah,

29:17

that's the surreal part. I

29:21

tried several things. I

29:24

went to a rehab facility. My

29:28

father, I told you, I was racing

29:30

and really religious home. Sometimes

29:33

I would say my dad's watching me

29:36

for a therapy. He

29:39

wouldn't approve of this. He

29:41

wouldn't be so disappointed in me.

29:46

But seemingly, I

29:49

just couldn't stop. Let

29:54

me pause here for a second, Jed. I

29:56

want to just take a moment to try to figure

29:59

out what exactly is happening. happening to Anne. Yeah,

30:01

why can't she stop? Yeah. It

30:03

turns out there may be an explanation if you look into

30:05

her brain. Remember

30:07

earlier we talked about a little chemical called dopamine

30:09

and how she didn't have enough dopamine in her

30:12

brain so that was giving her some kind of

30:14

movement trouble, the Parkinson's. Right. It

30:16

also turns out to be the case that

30:18

any time you do something that

30:20

makes you feel good, your brain

30:23

spurts out dopamine. For years that's

30:25

how scientists saw dopamine as

30:27

the neurotransmitter of pleasure, the neurotransmitter of sex,

30:29

drugs, and rock and roll. But you

30:31

said earlier that dopamine has to do with movement. What

30:34

is the ultimate purpose of movement from the

30:36

perspective of evolution? It's to get you to

30:38

food, it's to get you to sex, it's

30:40

to get you to a reward. So that's

30:43

why the same circuits, the same chemical that

30:45

controls motivation, that controls what you want also

30:47

controls movement. But that

30:49

turned out it was a little more complicated than that.

30:53

In the mid 1970s a guy named Wolfram

30:55

Schultz decided to take a much closer look

30:57

and his subject was a monkey.

31:00

So he would put these very thin needles

31:02

that can record the activity of individual dopamine

31:04

neurons in the monkey brain. And

31:07

they'd put the monkey in a room

31:09

and then every day they would walk

31:11

down the hall to the room where

31:14

the monkey was, they'd open the door,

31:16

hello monkey. They'd flip on the light,

31:18

they'd give the monkey some juice, here

31:20

you go monkey. And then when the

31:23

monkey sipped the juice, dopamine. Happy

31:25

monkey. Right. He

31:27

soon discovered something very odd about these

31:30

neurons. As they

31:32

juiced this monkey gay, hello monkey. The

31:39

squirt of dopamine, which they were always measuring

31:41

in the monkey's brain, seemed to move forward

31:43

in time. What do you mean?

31:45

Well at first the dopamine hit when the monkey took

31:47

the sip of juice. But

31:50

after a while the monkey got the dopamine hit

31:52

when they entered the room and switched on the

31:55

light. Hello monkey.

31:57

After a few more times the dopamine hit when the monkey took the sip of

31:59

juice. When the researchers peeked, it

32:02

could be heard walking down the hall. You

32:06

see what's happening here? Hello monkey. Um,

32:08

not really? You have to bring it home

32:11

for me. A little bit. I'll do it again then.

32:14

What the monkey is trying to do

32:16

is piece together the sequence of events

32:19

that inevitably lead to juice. Exactly. That's

32:21

what these cells do. They try to

32:23

predict rewards. Oh,

32:26

so this isn't just about movement or

32:28

about feeling good. It's about finding the

32:30

pattern of the thing that makes you feel

32:32

good. Yeah. It's pattern finding. Oh,

32:34

this is pure pattern recognition. This

32:36

is essentially how your brain makes sense

32:38

of reality in some very primitive sense.

32:40

It parses reality in terms of rewards.

32:43

This is how you get more food in the wild. You

32:46

can see the reward before anyone else can. So

32:49

we're talking about like basic survival stuff here. There's

32:52

one other wrinkle though, methadopamine system

32:54

that makes casinos and slot machines

32:56

so tantalizing, which is that these

32:58

cells are all also programmed to

33:00

be very sensitive to surprising rewards.

33:02

So this seems to be, most scientists speculate that

33:05

this seems to be your brain's way of telling

33:07

you, pay attention. You just got something for free.

33:09

This must be good. Sit here in this nice,

33:11

comfy velvet chair and try to figure out this

33:13

reward. So now imagine Anne sitting

33:15

there at the slot machine. She

33:18

pushes the button on the machine. And

33:25

sirens and bells go off, coins

33:27

clang. And inside her head, her

33:31

dopamine, they're saying, this

33:34

is wonderful. But

33:36

now how did this happen? Where did this big

33:38

reward come from? What did you do this time?

33:40

Why are you so happy all of a sudden?

33:42

And start searching for something. Dead frogs and

33:44

fences and cherries. Was it the

33:46

number of cherries that she had just

33:48

before? Was it that this machine had

33:50

13 hits and this was the 14th?

33:53

I thought I could tell. It has

33:55

all kinds of pattern like things. It

33:57

has bells. It has lights. But

34:00

the problem is, is that there is

34:02

no pattern to find. There is no pattern.

34:04

It's inherently random. It's inherently unpredictable. And while

34:06

the rest of us might just, you know,

34:08

give up and walk away. God, I just

34:10

wasted a hundred bucks on this stupid machine.

34:13

I should go get lunch. Ann can't

34:15

go to lunch. Her dopamine system is

34:18

too powerful, too potent. Oh,

34:20

because of that drug she's taking.

34:22

Right. It keeps surging and surging,

34:24

forcing her neurons to fight, fight

34:26

hard, to find a pattern. That's

34:28

what's gripping her. Her brain is

34:30

intoxicated at the possibility that it

34:32

will learn how to succeed. That

34:35

it will crack an uncrackable code. She

34:41

told me a story about she would go

34:43

to buy milk and then spend

34:45

the next 12 hours with

34:48

the milk rotting next to her as

34:50

she puts quarter after quarter after quarter

34:52

into this machine. Were you surprised when

34:54

you learned that the medication might be

34:56

responsible for your gambling addiction? I mean,

34:59

someone said to me, this

35:02

medicine will cause compulsive gambling.

35:05

I thought they were crazy. It's

35:07

just at that time where the first studies

35:09

come out showing that this is actually a

35:11

common side effect of rec-rip. Really? So there

35:13

were other Anns appearing in other places? Same

35:16

deal? Absolutely. Basically,

35:19

after my neurologist sent me off

35:21

the recrip. Her compulsion

35:23

disappeared instantaneously. Almost immediately. That

35:26

fast. Well, it was been a

35:28

week I'd say. Wow. It was

35:30

gone. I haven't gambled for nearly

35:32

three years. Did her Parkinson's

35:35

return? Yeah. I have

35:37

a dreamers of that worth. I've

35:39

recently gotten a cane after walking.

35:42

I use a wall. So the price

35:44

of not being a gambling addict

35:46

is living with debilitating Parkinsonian symptoms.

35:48

About my son. Let me finish

35:50

about my son. When I told

35:52

him after the quick gambling I

35:54

said, son, I just sold

35:57

things that belong to you. that

36:00

you should have happen he said mom

36:05

that's just things that's

36:08

just really great to have you back

36:33

radio lab will continue in a moment hello

36:39

I'm jana boom rod and i'm robert krolwick

36:41

radio lab and our topic today is you

36:44

want to say the word

36:46

stochasticity stochasticity which is a

36:48

wonderful and fancy word that

36:50

essentially means randomness chance like

36:52

the kind built into flipping

36:55

a coin or playing lottery

36:57

or to take things deeper when

36:59

you breathe crow

37:02

which thing about the air that's flowing around your head right now

37:05

full of atoms and molecules they're

37:07

flying about smashing to each other

37:09

and colliding and shooting off different

37:12

trajectories can't be predicted it's totally

37:14

chaotic right mm-hmm until

37:17

you breathe it all in when

37:22

you do things get predictable the

37:31

point is when you breathe in all

37:34

of those chaotic fluxing molecules come in

37:36

and become a part of

37:38

the machinery that is you they

37:41

go into your blood they go into your cells

37:43

which are themselves these little factories full of even

37:46

tinier factories of mitochondria what are mitochondria

37:49

I'm not really sure but

37:51

I do know that's Jonah lara again himself

37:53

a factory of insight after he's full of

37:56

intricate things which which

37:58

work you can understand You know

38:00

this gene makes this protein which makes this

38:03

organ out which does this

38:05

thing for the cell This

38:10

process is Jonah taking in Flux

38:15

and giving it a shape Giving

38:19

it order that is what

38:21

life Does in fact you might

38:23

say it is the definition of life

38:25

the closer you get the more you kind of

38:27

stand in awe at the exquisite engineering

38:35

There is a sense of life is simply

38:37

the world's most elegant Now

38:50

if life is a machine you would

38:52

think that the most Clock

38:55

like most machining part of life would be

38:58

all the way down at the bottom I

39:00

would think so which for our purposes is

39:02

when a gene makes a protein gene This

39:07

is the basis of life so you would

39:09

think it's got to be orderly. It's got to

39:11

be predictable otherwise none

39:13

of us would be alive

39:17

It is a very predictable orderly

39:20

system, so we all

39:22

believe pretty amazing

39:26

But then we spoke to that guy am I talking

39:28

have I been have I been talking yeah, okay, and

39:30

he Mucked

39:32

things up. I'll be looking this way. Well.

39:34

What's your name? My name is Carl Zimmer He's

39:37

a science writer like Jonah I relit for the

39:39

New York Times and Scientific American and discover

39:41

I blog and he told

39:43

us that this whole genes making

39:45

protein situation As

39:48

tick tocky and affairs we've always assumed it to be in

39:51

fact Scientists have never actually

39:54

seen it. I mean it's very small,

39:56

but finally scientists have figured out a

39:58

way to turn on a

40:00

light when it happens so they now can see

40:02

a gene turning on a

40:05

protein. Literally say it with their own

40:07

eyes. Yeah. And what they saw was

40:10

astonishingly un-clock-like. At

40:13

the fundamental level, it's just sloppy.

40:16

Sloppy. And that's the best word for

40:19

it. In fact, in our interview, he

40:21

used that word like 42 times. Sloppy,

40:24

sloppy, sloppiness. Sloppiness.

40:26

Sometimes he uses this word. Random, fluctuating,

40:29

noise, chaos, noise. Definitely

40:32

use that one a lot.

40:34

No, no, noise, noisy, accident,

40:37

noisy, noise, noise, noise, noisy,

40:40

sloppy, chaotic noise, sloppiness, sloppy

40:42

and fluctuating. It fluctuates. It's

40:44

really crazy in there. He

40:48

started by telling us about this experiment that happened

40:50

in California at Caltech, involving a

40:52

little tiny bacteria called E. coli,

40:54

which is Carl's favorite. Indeed.

40:57

Yeah, so these are E. coli. These

41:00

are harmless residents of our gut.

41:03

And they're also- Would you call them creatures?

41:05

They're creatures, sure. They sense their world.

41:07

They make decisions. They feed. They reproduce.

41:10

They have genes like us. They've got

41:12

4,000 genes. I think they earn the

41:14

title creature. And these creatures are actually

41:16

very similar to our own cells. Their

41:18

genes make proteins just like ours. So

41:20

what these scientists did was they took

41:22

some E. coli that were exactly the

41:24

same. Clones. In every single

41:26

way. They're identical. And then they put the whole batch in

41:29

a dish. And they said, okay, everyone, we're

41:31

going to turn on your genes. Start making

41:33

proteins. Now. And

41:36

they watched. Because like you said

41:38

earlier, they had found this new way of getting the E. coli

41:40

to flow. Every

41:45

time it's genes, they made

41:47

a protein. It

41:49

seems like it ought to be like just flicking

41:51

a switch. Yeah, you turn on the genes. Click.

41:54

Protein, protein, protein, protein, protein, protein. Turn it off. Turn

41:57

it on. Protein, protein, protein, protein, protein, protein.

41:59

Turn it on. Couldn't get simpler. This is

42:01

like a basic function of biology. Yeah, this

42:03

is biology 101 and again These

42:05

are genetically identical E. coli meaning they've

42:08

got the same genes. They're making the same

42:10

protein So they should go the same

42:12

right? You just expect a steady glow And

42:17

all of them nice and steady And

42:21

that's not what happened You

42:24

could start with like an individual E. coli and

42:26

say okay. Well what happened with that one? It

42:29

didn't start to glow it started to

42:36

There'd be a little bit of light No

42:38

light a little bit more light Then

42:41

maybe a sudden flash Then

42:44

dark again and a little

42:46

bit of light Hmm and

42:49

they were expecting What

42:52

they got thing was Oh

42:55

It was completely defective like a

42:58

car with no muffler going More

43:02

troubling still when they looked at the cola number

43:04

two two was defective

43:08

except in its own unique way Two

43:12

had his own thing going same

43:14

with number three He

43:17

had his own thing going. I mean

43:19

they're genetically identical Same in

43:21

number four. This is essentially

43:23

the same creature in many different

43:25

copies and five Six

43:28

two five and seven each

43:30

one with click rings in its own

43:32

break Hey

44:03

Now this noise would not

44:05

be a problem if it's just bacteria we're talking about.

44:09

But according to Carl, it's everywhere. Everywhere

44:12

in us. We are built,

44:14

he says, on a foundation of

44:17

chaos. This is very puzzling to me because

44:20

if down at a deep

44:22

level of our DNA, there's

44:24

just this random mayhem. How

44:27

do you go from bedlam up

44:29

to the organization that I think I represent?

44:32

I wake up in the morning, I go

44:34

to sleep at night, I get hungry, I

44:36

eat, I breathe in, I breathe out. Listen

44:38

to my heart. I

44:43

am very, very orderly. I

44:48

don't know how you get from this to this.

44:56

That's right. I mean, so somehow,

44:59

all of this sloppiness has

45:01

got to be somehow tamed

45:04

because we're alive. I

45:06

mean, it's not total chaos in our bodies. But

45:09

you keep the sentence, never seems to quite fit it. But

45:12

we don't know how that happens? We

45:14

have some ideas of how it happens. As

45:16

scientists start to understand how genes

45:19

work with other genes, they can

45:21

see ways in which you can

45:25

filter out the noise

45:27

and keep the good signal, keep

45:29

the music. Okay, so do you want to sit

45:31

for a minute? Anywhere

45:35

really. Now, this I find really cool. The

45:37

research on this stuff is really

45:40

new, but Carl says one of

45:42

the ways that the body may

45:44

do this testing, hello, hello, may

45:46

go from like, to, is by

45:49

doing something that I actually do on the show all the time,

45:51

which is use a noise filter.

45:53

The body may have engineered some noise filters. I'll just

45:55

give you an example from my

45:57

world. This is the honest to God's truth. named

46:01

Little Wing Lee. Hello, Dad. In

46:04

my hands, I have two audio tapes. Little

46:06

Wing just recently called me up. She

46:08

said, I've got these two cassette tapes. They're really old. I

46:10

think they were made in the 70s. My

46:13

mom found them in her attic, and they're of my grandmother. One's

46:16

labeled Mima Singing. Singing? Singing

46:22

old slave songs, an old hymn. Now,

46:24

Little Wing's grandmother died last year. She

46:26

was 99 years old. Wow.

46:29

And they were really close. Yeah, very close. They

46:31

used to call me Little Mima when I was

46:34

a kid. So she's got these tapes. She wants to

46:36

hear them. The problem is, if you put it on for more than three

46:38

minutes, you get annoyed. And there's that weird,

46:40

like... It's too

46:42

noisy. She

46:45

wanted to know if I could do something about it. So,

46:47

real quick, here's what I did. I

46:49

put it into a computer, launched an

46:51

EQ program, found the bass noisiness, which

46:54

was around 600 hertz, dialed that down,

46:57

like so. Then I found the high frequencies,

47:00

which are around 2,000 hertz, dialed

47:03

that down. Now,

47:05

as a final step, I just kind of located the voice around

47:07

1,000 hertz and dialed

47:09

it up. Singing old slave

47:12

songs, yeah. Okay,

47:16

so it's not a flawless process. I mean, now she sounds like

47:18

she's coming out of a well. But for

47:20

the first time, you can hear her voice. I

47:22

don't know. This is the first time I'm

47:24

hearing this song. But it

47:26

seems like she's describing the night

47:30

that my grandfather passed away, talking

47:33

about the doctors telling

47:36

her that my grandfather has passed. And

47:39

then she's describing putting a fern in

47:41

his hand, and she said it should

47:44

be a rose. Singing

47:51

old slave songs, yeah. The

47:55

thing that's applicable here is that we started with this.

47:58

And Then just by bringing certain frequencies in,

48:00

the down and others up, we ended up

48:02

with this. This.

48:08

Might be how it is in the body. That. You've

48:11

got this noise all the way in the

48:13

bottom these genetic circus which were spitting out

48:15

messiness but some are just some top of

48:17

that are other of genetic circuits. Which.

48:19

Are cleaning it all up? Giving. It a

48:21

sat. Way what is

48:23

it? is an hour. I'm

48:25

not quite carrot cyan success.

48:29

And what was wrong with that?

48:31

Well in ourselves, there's no Grandma.

48:33

When you mean there's no grandma

48:35

who don't start off with some

48:37

very clear signal that gets masked

48:39

by noise. The. Noises Their from

48:41

the start. It's noise. And.

48:43

There would have a sudden you have this

48:45

beautiful song. Carl went on to explain it

48:48

to killing an hour for us to finally

48:50

get. This does nothing but noise down there

48:52

at the bottom and yet somehow the song

48:54

emerges like a phantom. Is. It

48:56

seems like the noise is somehow

48:59

filtering itself. Into music so

49:01

if to your gets eat algae right

49:03

little we would he job a tape

49:05

with just fragmented sound, little bit some

49:08

little bits of me my and all

49:10

kinds random ways maybe she gave you

49:12

eight or nine teeth. And

49:15

somehow he says it all starts. the gonna get

49:18

into a network with this one filters that one

49:20

and about one for the the other one. Young

49:22

ones will does that. Ninety one out of all

49:24

of that. Hum

49:27

grammar. Com

49:29

The song. To

49:31

Song of a Living

49:34

Regular Organism Nema literally

49:36

I mean grandma's are

49:38

made from chaos. Ah

49:40

a love that. Movie

49:43

like this one was like it's seems like

49:45

a miracle that like that stands up and

49:47

walks see. The thing is you've hit. I

49:49

mean we are talking about something that scientists

49:52

don't understand so I don't have so there's

49:54

none of. if if

49:56

you want to have a part of the show you

49:58

say has this be both is hop It

50:00

all works can't do that. No, but

50:02

here's the thing if you want to get

50:04

fruity about this You could say

50:06

and I put this to Carl that if all

50:09

the way down at the bottom of us There

50:11

is this fuzz it cannot be predicted then

50:14

in some sense We're free

50:17

to be whatever we want. Hmm. Well,

50:19

I Mean

50:22

look I can sit here and concentrate and I can

50:24

think any thought I want to

50:26

right Now

50:28

and you know sure but you can't think

50:31

about a poem from second century China Do

50:33

you do you think that do you think

50:35

could you make an equivalence between? loose

50:38

mechanics and sense of freedom

50:41

well You

50:44

know, I mean does the sloppiness and

50:46

the floppiness of a protein

50:49

clamping onto your DNA scale

50:52

up to What you're

50:54

gonna be when you grow up on radio love.

50:56

Yes Okay Hello,

51:17

this is Carl Zimmer The stochastic theme

51:19

song was created by Josh Kurtz and

51:21

Shane winter special. Thanks to little

51:23

wingly and me mom Visit

51:26

radio lab online at radio lab org

51:29

where you can comment on this show

51:31

ask random questions and hear the entire

51:34

Stochastic theme song. Anyways, this is

51:36

little wing Hi,

51:42

I'm Hazel and I'm from Silver Springs

51:45

Radio lab was created by Chad a bone map

51:47

is edited by Soren Wheeler Lulu

51:49

Miller and Latiff Nasser are co-hosts

51:52

Dylan Keith is our director of sound design Our

51:54

staff includes Simon Adler

51:57

Jeremy bloom Becca Bressler it

51:59

can Eddie Foster Keese, W. Harry

52:02

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52:04

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52:08

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52:13

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52:15

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52:17

fact checkers are Diane Kelly,

52:20

Emily Krueger, and Natalie Middleton.

52:23

Thank you. Hi,

52:26

this is Tamara from Pasadena,

52:28

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