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at quantummagazine.org/hyperjumps. Throughout

0:34

the animal kingdom, from

0:37

tiny gnats to fish, birds,

0:39

gazelles, even primates like us,

0:42

creatures tend to organize into large

0:44

moving patterns that pursue

0:46

a seemingly spontaneous collective goal. Often

0:50

no individual creature appears to act

0:52

as the leader orchestrating these mass

0:54

movements. Rather,

0:56

the animals just seamlessly fall into

0:58

line. And even though it feels

1:00

like such systems would teeter into

1:03

chaos or instability, these collectives

1:05

somehow manage to move in

1:07

ways that appear

1:09

extraordinarily well-coordinated and purposeful,

1:12

as anyone who has watched a murmuration of

1:14

starlings or a school of fish can attest.

1:18

But what's the driving force behind this

1:20

behavior? I'm

1:25

Steve Strogatz, and this is The Joy of Why, a

1:27

podcast from Quantum Magazine, where

1:30

my co-host, Jana Levin, and I take

1:32

turns exploring some of the biggest unanswered

1:34

questions in math and science today. In

1:40

this episode, we're going to be getting to

1:42

the heart of why animals flock, swarm, and

1:44

school. How

1:47

are the latest technologies, like artificial intelligence

1:49

and 3D cameras, providing new insight? And

1:53

what can studying animal group

1:55

dynamics tell us about

1:57

ourselves, both individually and collectively?

2:00

as collectives. Here

2:02

to shed light on these mysteries

2:04

is evolutionary ecologist Ian Cousin. Ian

2:07

is the director of the Department of

2:09

Collective Behavior at the Max Planck Institute

2:11

of Animal Behavior and a

2:13

full professor at the University of

2:15

Konstanz. Among the

2:18

many honors he's received are the

2:20

National Geographic Emerging Explorer Award, the

2:23

Lagrange Prize, the highest honor in the

2:25

field of complexity science, and

2:27

the Leibniz Prize, Germany's highest research

2:29

honor. Ian, we're so happy to

2:32

have you with us today. It's

2:34

great to be here, Steve. Well,

2:37

I'm very happy to see you again. We're

2:39

old friends and this is going to be

2:41

a real treat to hear about the latest

2:43

in collective behavior. But let's begin.

2:45

I suppose we should talk about who are

2:47

your specimens. Could you tell us a

2:49

little about some of the animals and the variety

2:51

of forms that their

2:53

collective behavior take in the systems that

2:56

you've studied? Well, that's

2:58

one of the most amazing things about

3:00

studying collective behavior is that it's central

3:02

to so many processes on

3:04

life on our planet that we literally

3:06

study a range of

3:09

organisms from the simplest animal on the

3:11

planet. It's called a Placozoa.

3:14

It's a basal phylum, possibly

3:16

the simplest multicellular animal on

3:18

the planet. It's a swarm

3:20

of cells, thousands of cells,

3:23

much moving like a bird flock or a fish school,

3:26

up through the invertebrates like ants

3:28

that have amazing coordinated behavior or

3:31

locusts that form some of the

3:33

largest most devastating swarms to

3:35

vertebrates such as schooling fish,

3:38

flocking birds, herding ungulates, and

3:41

primates including ourselves,

3:44

humans. So it

3:46

really seems to run the whole gamut all the way from,

3:48

I have to admit, I never heard of this,

3:50

did I get it right, Placozoa? Placozoa, yes. This

3:54

little creature was found crawling around on

3:56

the glass of Aquaria, tropical Aquaria. You

3:59

can see it within a... eyes, about a millimeter, maybe

4:01

a millimeter and a half if it's very big. And

4:04

looking into this remarkable creature has

4:06

only really recently sort of drawn

4:09

the attention of scientists. And

4:11

that was largely because this strange

4:13

little quirky swarm of cells actually

4:17

has the genetic complexity that you

4:19

would associate with a much more

4:21

sophisticated organism. For example, it has

4:24

a large range of neurotransmitters, yet

4:26

it doesn't have neurons. It

4:30

has what I call Hox genes.

4:33

Hox genes are in developmental biology

4:35

associated with complex body plans. It

4:38

does not have a complex body plan. And

4:40

so perhaps you may think, well, this

4:43

creature may have evolved to become more

4:45

complicated and then re-evolved to simplify itself,

4:47

and therefore it kept these characteristics of

4:49

complexity. But genetic research

4:51

has published a sort of landmark

4:54

paper in the journal Nature that

4:56

showed no. In fact, this is

4:58

one of the most primal groups

5:00

of cells. And of course,

5:02

collective behavior, what more beautiful example

5:04

than cells coming together to form

5:06

an organism. So this

5:08

is one of the reasons we study

5:10

this. Try to understand how collective behavior

5:12

was central to the origins of complex

5:14

life on our planet. Man,

5:16

this is an early stage in the interview

5:19

and you're already blowing my mind. You're also

5:21

derailing me from what I thought I was

5:23

going to be talking to you about. This

5:25

is so interesting and so new

5:27

to me that I'm stunned.

5:30

I want to come back to this part of the

5:32

story because it's so, I mean, it's really surprising that

5:34

they would have, did

5:37

I hear you right? They have things

5:39

associated with having a nervous system but have

5:41

no nervous system and have

5:43

developmental biological genes as if they

5:45

needed to evolve a whole

5:48

complicated body plan like a fruit fly

5:50

but they don't have a body like

5:52

that? Exactly. Exactly. So

5:54

they could really give us a hint

5:56

at the origins of intelligence. Our

5:59

particular study... which we published this year,

6:02

we showed that the body plan

6:04

that they have really does behave

6:06

very much like a bird flock

6:08

or a fish skull with cells

6:10

locally interacting with others intending to

6:12

align their direction of travel. So

6:14

they're attracted to each other, they're

6:16

self-connected together like an elastic sheet

6:19

but they tend to also be motile. They've got

6:21

cilia, a little cilia on their base so they

6:24

can flow along the environment

6:26

and the forces that they apply

6:28

to their near neighbors cause them

6:30

to align with each other. And so

6:32

if we track these cells under a

6:34

microscope and we look at the

6:37

alignment and we look at the attraction of

6:39

the individuals, we use very

6:41

much the same technologies, the same models,

6:43

the same thinking that we use for

6:45

collective behavior in bird flocks or fish

6:48

skulls or other types of groups but

6:50

apply it to these animals. And

6:53

so this is one of the things I

6:55

find most remarkable about collective behavior is that

6:57

even though the system properties whether you're a

6:59

cell or whether you're a bird are very

7:02

different, when you look at

7:04

the collective action, the collective properties,

7:06

the mathematics that underlie this actually

7:09

can turn out to be very similar and so we can

7:11

find these sort of what are

7:13

called universal properties that connect

7:15

these different apparently disparate systems.

7:19

Well of course now you're speaking my language

7:22

since you know

7:24

that's what drew me into my own

7:26

fascination with collective behavior is that there

7:28

are those universal mathematical principles that seem

7:30

to apply up and

7:32

down the scale from cells to well

7:34

we of course we always like to put ourself

7:36

at the top but so

7:39

okay you've raised so many different issues for us

7:41

to think about. Let me try to go back

7:43

to the beginning much as I'd love to stay

7:45

with you here with the Placozoa. So

7:47

for example you mentioned words like flocks and

7:49

schools and sometimes we

7:51

hear people talk about swarms like with

7:54

insects. Is there any reason we have

7:57

three different words for the same thing? Are they not

7:59

really the same? Same thing when we talk

8:01

about collective groups. Is there a reason

8:03

we shouldn't talk about like schooling birds

8:05

or swarming fish? No.

8:08

I think we've developed these words and

8:11

different languages have different words. In German,

8:13

which is a language that pleats with

8:15

many words, they actually have relatively few.

8:18

Whereas in English, we

8:20

have many, many different words. Like

8:22

for example, a group of crows, it's called

8:24

a murder of crows. You

8:27

yourself earlier used the wonderful word,

8:29

the murmuration of starlings. And

8:32

I think it's that it's the beauty, the

8:34

captivating beauty of flocking and schooling

8:36

and swarming that's given rise to

8:39

these wonderful words that can be

8:41

associated with particular examples. And so

8:43

I think that's a very useful

8:45

thing because earlier on I was

8:47

emphasizing the commonalities, the mathematical commonalities,

8:49

but there are also differences. That

8:51

is a difference between a swarm

8:53

of cells and a swarm of

8:55

birds. And so to understand these

8:57

systems, we both have to consider

9:00

the principles in common, but also those

9:02

that differ between the systems. And in

9:04

a way, language kind of captures

9:06

some of that for us in the way

9:08

that humans have naturally sort

9:10

of segregated or divided

9:13

these into different categories. Interesting.

9:15

So you mentioned swarm of cells

9:18

and swarm of insects,

9:20

I guess it was. And

9:22

you said there could be some differences even though

9:24

we use the same word. What are the things

9:27

that we should distinguish between those examples? Yeah,

9:29

I think what's really exciting is why there

9:31

is a commonality because the differences are so

9:34

profound. An animal has a brain. It's

9:36

taking in complex sensory information and

9:38

trying to make decisions about its

9:40

environment. Animals are capable of much

9:43

more complex sophisticated behaviors on

9:45

average than cells, but cells

9:47

of course themselves have complex

9:49

internal processes. But their

9:52

interactions are dominated to a greater

9:54

degree by physical forces, by

9:56

the scale at which they're acting and

9:58

the tensions that fall. form, the

10:00

physical tensions that form within the

10:03

cell aggregate, whereas the animals, the

10:05

interactions between birds and a flock,

10:07

they're invisible. They have no

10:10

physical form. And so one

10:12

may initially think, well, then it's

10:14

only an analogy. In fact, I

10:16

would say until about five to 10

10:18

years ago, I thought it was just an analogy

10:20

too. I thought that these

10:23

differences must be very important. So

10:25

what we're beginning to understand is that the

10:27

common feature that they share is

10:29

computation, is that

10:31

these elements are gathering together to

10:33

compute about their environment in ways

10:36

that they can't compute on

10:38

their own. Each individual, even if you've

10:40

got a very complex human brain and

10:43

you're walking around in the world, unless

10:45

you have social interactions with others, or

10:48

even more so you build upon the

10:50

cultural complexity that we inherit when we

10:53

are born into our lives, then

10:55

we're very limited. And so

10:57

there's these deep sort of very fascinating

10:59

questions that we're just beginning to address

11:02

about computation and the emergence

11:05

of complex life. Such

11:07

an interesting point of view. I didn't know what word

11:09

you were going to say when you said there's something

11:11

they all have in common. I couldn't guess, but I

11:13

like it. Computation. It

11:15

makes me think of a famous

11:18

thing that people may have seen movies

11:21

of on YouTube or on television, where

11:23

there's a flock of birds, maybe it's a

11:25

starling and a hawk or

11:28

a falcon or something comes zooming

11:31

in toward the flock. Maybe

11:34

you should give us a visual description of

11:36

what happens next and why am I thinking

11:38

that there's anything to do with computation in

11:40

this example? Well, I mean,

11:42

if you look at these groups, when you

11:44

have these predators present and attacking these groups,

11:47

whether it's a fish skull or a bird flock, you see

11:49

the group behaving as this sort of undulating

11:52

fluid. You see these ripples of light

11:54

crossing the group or ripples of density

11:56

crossing the group. And

11:59

What this is indicative of

12:01

is that the individuals can

12:03

actually propagates information about the

12:05

location of that predator very

12:07

rapidly via social interactions. So

12:09

individuals that see the predator

12:11

for example maybe only a

12:13

few of them initially see

12:15

the predator but by turning

12:17

then this behavior been copied

12:19

by others. The chains of

12:21

density the change of turning

12:23

is propagated extremely rapidly and

12:25

if we use of I'm

12:27

Silver gets the Slater for

12:29

use. Advanced imaging tools to quantify

12:32

to measure these waves of turning.

12:34

It results in a wave of

12:36

propagation that's around ten times faster

12:39

than the maximum speed of the

12:41

predator itself. So indifferent talk and

12:43

response to a predator that they

12:46

don't even see says the group

12:48

and the individuals in the group.

12:51

The selection Natural selection as acts

12:53

on the individuals. Typically, they can

12:55

actually response to stimuli that they

12:58

don't. Detect. It's a

13:00

little bit like you know a neuron

13:02

transmitting information by electrical signals in this

13:05

case is not electrical signals, it's really

13:07

the density in the turning of the

13:09

individuals that percolates across the group. But

13:11

it gets those individuals are far information

13:14

where the threat is so they can

13:16

begin to move away from a very

13:18

quickly. So. That is I think

13:21

a very beautiful visual. Example.

13:23

of of what computation would

13:25

mean in this context of

13:27

we can see these waves

13:29

of panic or avoidance flowing

13:31

through the flock. It's

13:33

It's so interesting that it's much faster

13:35

than the individuals. Would. Be

13:38

able to do on their own and I guess

13:40

faster than what the predator can muster on it's

13:42

own. One. Of the reasons why

13:44

this is likely to be why we think

13:46

this is. Is because

13:48

the group state natural selection

13:50

even though sensing on the

13:53

individuals stephens that matters the

13:55

such a collective benefit to

13:57

everybody is they behave in

13:59

a certain. This

14:01

again relates to what we've learnt

14:03

from physical systems. specifically physical systems.

14:05

cluster phase transition sir over system

14:08

as close to transition between different

14:10

states such as between a solid

14:12

in a liquid in X, you're

14:15

freezing water and it suddenly transitions

14:17

into a solid. The collective behavior

14:19

of that system is quite remarkable.

14:21

near that transition point, the spice

14:24

occasional to courses you're an area

14:26

of study and this is something.

14:29

That. We now know we have

14:31

very strong evidence the natural selection

14:33

process systems close to these basic

14:36

a some points because of the

14:38

collective properties, the remarkable collector properties

14:40

that are exhibited When we first

14:43

message these properties it seems like

14:45

the individuals with defying the laws

14:48

of physics, the intimate with percolating

14:50

so quickly and in then sort

14:52

of an early nineteen hundreds Edmunds

14:55

sell us to with a concerns

14:57

Darwinian of but also. So

15:00

captivated by the fascination with telepathy

15:02

in the Victorian era, he was

15:04

assumed that must be thoughts transference.

15:06

He described it off, telepathy between

15:08

birds that a lie, them to

15:10

communicate so quickly and of course

15:12

people in a thing or that's

15:14

ridiculous of course the competent apathy.

15:16

but in actual fact and Mrs

15:18

may be a little controversial. but

15:20

in as of fat I think

15:23

we still don't have a good

15:25

grasp of the century modalities in

15:27

the way in which this information

15:29

percolates. So exquisitely. Rapidly.

15:32

Across the system. I'm not suggesting

15:34

this telepathy of course, but I'm

15:36

suggesting that by tuning a system

15:39

by senior collector system close to

15:41

this critical point. Close. To

15:43

the Spicer case in point, it

15:45

could give rise to remarkable collector

15:48

properties that to an observer look

15:50

fantastical to un observer the bizarre,

15:52

because the physics in these regimes

15:55

is bizarre. Is. Fantastical is

15:57

amazing even though it is

15:59

understandable. The same. To. I'm

16:01

just wondering would now in the case of

16:03

collect his behavior if nature tunes as lock

16:05

to be near some kind of. Point.

16:08

Of instability or criticality. Are

16:11

you suggesting that Part of what

16:13

makes it so effective? Yeah as

16:15

exactly what I'm suggesting. And so

16:17

for example Again, a very recent

16:19

paper within the last couple of

16:21

years that we published we asked,

16:23

you know, what about getting the

16:25

best of all worlds What about

16:27

if you know under general conditions

16:29

you want to be stable you

16:31

want to be robust for sometimes

16:34

you want to become site the

16:36

sensitive As a natural selection, biological

16:38

systems have to balance this amazing

16:40

from seemingly contradictory. Status of

16:42

being pushed for bus and sensitive

16:44

actually be both for bust and

16:46

sensitive at the same time and

16:48

so we sink them in a

16:51

tuning the system close to this

16:53

critical points as the allies that

16:55

to happen because if the system

16:57

devious and I see stabilizes itself.

16:59

but as a guest post was

17:02

actually the point it becomes incredibly

17:04

flexible and sensitive to inputs from

17:06

for example inputs regarding that predator

17:08

service a fiscal it's far away.

17:10

From like critical point for example is

17:13

that very strongly aligns with each other

17:15

and they detect a predator. And as

17:17

a fact that it takes a lot

17:20

of effort to total of these individuals

17:22

the so strongly responding to each other

17:24

that is hop that external input to

17:26

change their behavior. If on the other

17:29

hand they're very disordered on their own

17:31

moving in different directions then an individual

17:33

changing direction can hardly be perceived by

17:36

others and so does propagate through the

17:38

system. And certain I'm the sort of

17:40

intermediate. Points: They can actually optimized

17:42

that ability to behave as a

17:45

group and to be flexible, but

17:47

to transmit information. And this is

17:49

a theory from physics that's been

17:52

longstanding, but it's only really within

17:54

the last year's using computer vision

17:56

technology to track animals and groups

17:59

and asked. To it seems.

18:01

You. Know your interactions when for example,

18:03

the world gets more risky. We

18:06

would always think as biologists well

18:08

as the wealth gets more risky

18:10

a more dangerous I will become

18:12

more sense to inputs. I'll be

18:14

voted to be a be more

18:16

likely to make a false alarm

18:18

and us to have animals and

18:20

isolationists to humans will be behaving

18:22

in isolation. But we tested this

18:24

an animal groups groups have evolved

18:26

within the context of the collective

18:28

resigned that's not to have them.

18:30

What they do is they changed

18:33

the network. The network of conductivity

18:35

of hide. The information flows through

18:37

the system and the senate such

18:39

as to. Optimize.

18:41

This up for flexibility.

18:44

Robustness tradeoff. I eat they take

18:46

it into this critical regime as

18:49

we had predicted. Which kinds

18:51

of animals were these Studies done on.

18:54

So we must be work with

18:56

small schooling says because they have

18:58

to solve the same kind of

19:00

problems in avoiding predators finding suitable

19:02

habitat. Jets that tractable with in

19:04

a laboratory environments. Sufis actually have

19:07

a chemical what is called suspect

19:09

stuff which in german literally just

19:11

kinda scary stuff and sex stuff

19:13

is naturally boost of a predator

19:15

a sufis it festivities this chemical

19:18

so we can put sex stuff

19:20

in the water so there's no

19:22

location of a predator. But

19:24

individuals judgments about this environment changes the

19:26

world has become more risky. So what

19:28

he did he change was going on

19:31

in your brain. Teachings. How

19:33

you interact with him I'm do you become

19:35

more scared? Which. Is the natural thing?

19:37

We may think animals do. All. if

19:40

you imagine in a network system and

19:42

a collective system teachings that the policy

19:44

of that network the social that works

19:46

the way to communicate with others because

19:48

that can also change the responsiveness to

19:51

threats because of this wave of turning

19:53

that we talked about before and so

19:55

what we find was the individuals do

19:57

not saying what happens is the networks

20:00

changes, the individuals move to change

20:02

the structure of that network and

20:04

it's that that causes the

20:06

group to suddenly become more sensitive and

20:09

more flexible. People used

20:11

to, for example, have a proxy which

20:13

is that individuals that are close to

20:15

each other must be interacting more strongly.

20:18

But as you can think about in your daily life,

20:20

you can be sitting beside a complete stranger on the

20:22

bus and not actually be

20:24

socially strongly connected to them on average.

20:26

So the social network that an individual

20:29

experience might be very different from

20:31

the one that's easy to measure. So

20:33

what we've done is, well,

20:36

it's quite complicated, but what we

20:38

can do is we reconstruct the

20:40

world from their perspective and we

20:42

use a technique that comes from

20:44

video games and computer graphics called

20:46

ray casting, where we cast rays

20:48

of light onto the retina of

20:50

the individuals so we can see

20:52

a sort of computerized representation of

20:54

what they see for each moment

20:56

in time. But what we don't

20:58

know is how on earth do they process that? And

21:01

so again, we can use machine learning

21:03

methods because every brain has evolved to

21:05

do the same thing. It's

21:07

taken complex sensory information like people listening

21:09

to us today is a complex acoustic

21:12

information, but they may be driving or

21:14

maybe cooking. So they've also got complex

21:16

visual and or factory information, but that

21:18

brain has to take all this complexity

21:21

and reduce it down into what's called

21:23

dimensionality reduction into a decision or

21:25

into what am I going to do next? And

21:28

we've known very, very little about how

21:30

real animals do this, but we can

21:32

reconstruct their visual fields and

21:34

then we can use the same types

21:36

of techniques to reduce the dimensionality to

21:38

understand how does the brain reduce this

21:41

complexity to movement decisions. And

21:43

the fish that we studied, they have a

21:46

very small number of neurons in the

21:48

back of the brain that dictates all

21:50

of their movements. So the

21:52

brain has to take in all of this complexity

21:54

and it has to reduce it down and it

21:56

has to make decisions. And I

21:59

think it's a wonderful. Question in biology

22:01

as to how to bring do

22:03

that? First of all, I can

22:05

tell that I need to be reading your

22:07

papers more frequently. You said something about shining

22:09

lights on the retinas of the fish to

22:11

then see what they're seeing or to have

22:14

a feeling that you know what they're looking

22:16

at. Did I hear that Rights here? It's

22:18

not literally Sony like I squeeze old, that

22:20

Scully don't see much and you have a

22:22

Cisco as a snapshot in time for us.

22:24

A moment in time a software tracks the

22:27

position and also the body posture of it's

22:29

of this this ah, what we can do

22:31

speech and nitrates a three dimensional computer version

22:33

of. That seem like in a video

22:35

game. We can then asked what does

22:38

he eats individuals sea ice of the

22:40

cameras in the eyes of the individuals

22:42

and so recasting is a bit like

22:44

retracing disease and computer graphics which is

22:46

just the policies of like falling on

22:48

the retina and we do all the

22:51

stitch to submission created that still analog

22:53

of reality. We. Could then look

22:55

to see how lights would fall on

22:57

the retina in that that so seen

22:59

as a photo realistic that to see

23:01

and so that gives us the first

23:04

layer. what is the information coming into

23:06

the individual and then of course the

23:08

big question that we want to ask

23:10

his side of the brain process that

23:12

at the press think that complexity time

23:14

and how does that make decisions I

23:17

do for example fluids, slugs and Cisco's

23:19

move so effortlessly so beautifully with so

23:21

few listens and yet cause on a

23:23

highway tends to. Struggled to have Collectively

23:25

Most and Meat is the something

23:27

we can learn from millennia of

23:29

natural selection that we can then

23:31

apply to vehicles and to robots.

23:33

The So there's also apply the

23:35

elements trying to understand. I want

23:37

to understand it largely because I

23:39

find it fascinating, but also it

23:41

does actually translates to real applications

23:43

in certain cases. Will.

23:46

Be right back. Did.

23:49

You know that quite a magazine has a You Tube

23:51

channel? Every. Year our video team

23:53

produces dozens of videos that eliminate the

23:55

frontiers of math and science. Search quite

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a magazine on you tube and fine.

24:00

Repeal, Visual Explain or Is and short

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24:16

Welcome. Back to the joy of why. I'd.

24:19

Like to return to something you said back

24:22

in the introduction when you are going across

24:24

the scales from cellular of the primates insolence,

24:26

people may not be so familiar with

24:28

the locust. Example on I wonder if we

24:30

could talk about some of the. Us.

24:33

Call them real world or even

24:36

economic aspects of. A. Flocking

24:38

because locus have a big. Impact

24:40

on the World Bigger than I had realized.

24:42

I mean, I'm looking at some statistics here

24:44

and my notes that. During. Plague

24:47

years. Locus invade more

24:49

than one cyst of the world's land

24:51

cover. yeah can you believe it And

24:53

a sec the livelihood of one in

24:55

ten people on the planet. So could

24:57

you talk to us a little about

24:59

that kind of research and how it

25:01

relates as questions of global food security.

25:04

Yeah you have see rights A nice

25:06

I phone discussed on as soon as

25:08

he to set the impact one in

25:10

ten people on a planet through fruits

25:12

autism, food security on the often do

25:14

so in in countries like Yemen and

25:16

Somalia that. Have major problems,

25:18

major conflicts and uncivil was and so

25:20

on but also due to climate since

25:23

the range of lucas is expanding across

25:25

much of it's rains and so I

25:27

mean at the moment the see Afghanistan

25:30

is facing a major crisis in this

25:32

food person. A couple of years ago

25:34

was Madagascar a year or two before

25:36

that it was Kenya having the largest

25:39

swarm and seventy years. So why in

25:41

a with all of the modern technologies

25:43

that we have for monitoring why the

25:45

Psalms getting more ferocious, a more severe.

25:48

You know, on one of the reasons

25:50

climate since is that what happens

25:52

with the swamps is so Lucas has.

25:55

It might be surprising foolishness to know

25:57

this, but locusts actually don't like being.

26:00

The other day I saw a

26:02

cryptic green dress up as.like to

26:04

be left alone so if they

26:06

have plenty of food. They.

26:08

Just isolated from each other they

26:10

avoid each other. It's only when

26:12

the forced to come together to

26:14

they transition. So the normally what

26:17

a cold solitary is because the

26:19

solitary life stunk. but if the

26:21

as forced to come together they've

26:23

evolved to transition. As of the

26:25

Jekyll and Hyde of insect wealth

26:27

they they've evolved to transition quite

26:30

suddenly within an hour behave really

26:32

into gov Gary as form where

26:34

the to start marching towards each

26:36

other, following each other, another. Thing

26:38

people may not know is that

26:40

look especially don't have wings for

26:42

the first. Several months

26:45

of their lives and so it's

26:47

when look suborn that flightless does

26:49

as a a slight and this

26:51

nymphs. it's only when the adults

26:53

to the have wings and so

26:55

what's happening here is that when

26:57

rains come into Africa for example

26:59

or into India when to other

27:01

areas then you can have lost

27:03

vegetation and the the small looks

27:05

population can proliferate as as the

27:08

so cryptic grasshoppers they can grow

27:10

in population size. Not as that

27:12

population grows they eat more and.

27:14

More more and else often the

27:16

Colonel speech rights coming. Now she

27:18

got a high population density and

27:20

then suddenly defeat disappears Than what

27:22

the locusts do is they've evolved

27:25

to transition to this cook areas

27:27

form with stop mazzini together to

27:29

stop all moving together. These songs

27:31

can be billions of individuals as

27:33

far as you can see. I

27:35

look so marching in unison as

27:37

if in a common purpose. And

27:39

once they grow wings they can

27:42

take slights and then it gets

27:44

even. worse because they can access

27:46

the trade winds are other know

27:48

environmental conditions where they can transfer

27:50

themselves as must swarms of a

27:52

hundreds even thousands of kilometers and

27:54

so this is one of the

27:56

biggest and most devastating collect to

27:58

behave as we have on our planet. I

28:02

can't say that I'm very familiar with the

28:04

idea of locust marching. We're used to thinking

28:06

of them as these clouds swarming

28:09

in the air. But tell

28:11

us a little more about the marching

28:13

because I do vaguely remember some astonishing

28:15

research of yours with the cannibalistic

28:17

aspect of locusts. Is that the right

28:19

word to use? Yeah, that was in

28:22

2008. But

28:24

you're right. Huge flocks of swarms

28:26

or clouds of locusts that transition

28:28

over great distances. We

28:30

don't know much about them because we did

28:33

not have the technology to study that. In

28:35

fact, we still don't have the technology to

28:37

study that. So it's not that it's not

28:39

important. It's incredibly important. But

28:41

we also know that what precedes these

28:43

flying swarms, I mean, the flying swarm

28:45

is a bit like a wildfire that's

28:47

already got out of control. Now

28:49

you're really going to have problems controlling it.

28:52

But if you can control it before they

28:54

grow wings, when they're

28:56

forming these swarms in the desert

28:58

or these environments before that, then

29:00

there's great potential. And so for

29:03

practical reasons, we focused on these

29:05

wingless swarms. And in actual fact,

29:07

even though you're right, I started studying these in the mid

29:09

2000s, we're

29:11

now returning to locusts and I'm studying

29:13

them again. We've just created the world's

29:15

first proper swarm within

29:18

the laboratory environment ever

29:20

earlier this year where we tracked

29:22

10,000 locusts in a 15 by 15

29:24

by 8 meter imaging environment that

29:26

we built here specifically for the

29:29

purpose here in Constance. So

29:31

it's funny that you're mentioning it because my

29:33

research is now kind of looping back to

29:35

this same system. But yes,

29:37

as you said, what we discovered was these

29:40

insects, well, why are they marching together?

29:43

Why are they, you know, and we initially thought

29:45

it must be like fish schools and bird flocks.

29:47

It must be about information. It must be about

29:49

collective intelligence. Well, we

29:51

were wrong. And so this is

29:54

the great danger. If you see a

29:56

swarm of ants that's moving in a circle, moving in

29:58

a sort of mill. you

30:00

see a fish school for example turning in a

30:03

torus or a sort of donut like pattern or

30:05

you see a whirlwind, these are

30:07

all patterns that look the same but they

30:09

may be driven by very very different phenomena.

30:11

And I think I was misled in thinking

30:13

you know when you see collective motion

30:16

it must be similar processes that underlie

30:18

it. But in the case of locust

30:20

it was not this sort of information

30:23

transfer hypothesis. It

30:25

was actually the fact that in these

30:28

desert environments when the food suddenly becomes

30:30

short you're desperately short

30:32

of essential nutrients especially in the

30:35

desert protein salt and

30:37

water. And what

30:39

is better for you in this

30:41

sort of harsh environment than another

30:43

individual because they are perfectly balanced

30:45

nutritional composition. So

30:47

what the individuals do is they're attracted to each other

30:50

and they tend

30:52

to cannibalize each other. So

30:54

they've evolved to follow those that are

30:56

moving away to try and bite them at their

30:58

rear at the rear of the abdomen which is

31:01

very hard to defend against. The head is

31:03

heavily armored but the rear of the abdomen

31:05

is a weak point for

31:08

obvious reasons. There has to be an orifice

31:10

there and so they target that. But then

31:12

they also avoid being targeted by others and

31:15

the outcome of following those that are moving away

31:17

from you and moving away

31:19

from those moving towards you results

31:21

in the whole swarm beginning to

31:23

march together across this desert

31:25

environment. And they also benefit

31:28

by advecting by moving out of nutrient

31:30

poor areas together because you know if you put

31:33

a human in the desert a human

31:35

will tend to sort of get disoriented and tend

31:37

to move around in circles. Same with a locust.

31:40

But if you put them in a

31:42

swarm the collective alignment the synchrony among

31:44

the individuals, you know hundreds

31:47

of millions of individuals

31:49

aligning with each other. They can march

31:51

in a very directed fashion out of

31:54

these nutrient poor environments. And

31:56

they can also swamp predators. You know

31:58

predators just can't make much

32:00

of an indent here. It makes me wonder

32:02

actually as we talk about all these examples,

32:05

how did you become interested in all of

32:07

this back in the old days? You mentioned

32:09

this was back in 2008? That

32:12

was that paper in 2008. Yeah, you were

32:14

busy on this even before that, right? Yeah,

32:16

I did my PhD in the late 90s

32:18

on ants. I was fascinated by ant behavior.

32:21

And to be honest, it started with

32:24

a passion for nature and

32:26

an obsession with just natural history

32:28

and observing what was

32:30

around us. I thought as a child,

32:33

there must be an expert that understands

32:35

why swarms form, why

32:37

fish school, why birds flock. I thought

32:39

this must be something that everybody studies.

32:42

I was an artist as a child. I was

32:44

very interested in creative writing and poetry and art.

32:47

And so I was initially drawn in by

32:49

the pure beauty, the fascination by

32:51

the beauty of these. And

32:53

at high school, I was not a great student

32:56

in science. I was doing pottery

32:58

and I was doing painting. And

33:00

when I went to university, I remember my dad said

33:02

to me, you know, son, you should do what you're

33:05

good at. Do English or art. You're not a scientist,

33:07

you're a naturalist. And

33:10

he was right. He was absolutely right. And

33:12

it was then later when I did do a

33:14

biology degree. And I just knew in the very

33:16

first lecture of my biology lecture, I knew this

33:19

was the right thing for me. I just knew

33:21

it. And I discovered that there's this whole world

33:23

of statistical physics. These papers

33:25

came out in that time and they

33:27

blew my mind because there were authors

33:29

that were seeing deep mathematical

33:32

principles across systems. My

33:34

PhD advisor said, you know, to get a job, you

33:36

should become the world expert in one species of ants

33:39

and then you can be valuable. But

33:41

I was reading this work of scientists that

33:43

were doing the exact

33:45

opposite. They were studying everything from

33:47

physical systems to biological systems and

33:50

they were seeing these principles and

33:52

also the patterns and the structures

33:54

and the results that we're finding

33:57

were just naturally beautiful. And

33:59

so I thought, this has to be right. This has to

34:01

be the right way to do science.

34:03

And so at that time, I just got drawn

34:05

into the world of physics. Did

34:08

you ever have the pleasure of talking

34:10

to your dad afterward about your

34:12

change in direction? I never

34:15

ever thought my dad remembered this. And

34:17

then when I got promoted from assistant

34:19

professor to full professor at Princeton University,

34:22

I got a phone call from the chair

34:24

of the department that said, congratulations, Professor Köhnzen.

34:26

And I was just completely blown away. So

34:28

of course, I called my mom and dad,

34:30

and my dad answered the phone. And

34:33

then he said, I think I called you

34:35

a naturalist. That's the only time. That's a

34:37

decade later. I never knew he even remembered

34:39

this discussion. Well, it's

34:42

a good story. It's a really good

34:44

story. We like to talk about big

34:46

unanswered questions on this show. And so

34:48

what do you see as some of

34:51

the biggest unanswered questions about flocks and

34:53

schools and collective behavior generally? Well,

34:55

absolutely, I do. And this is getting me

34:57

onto the topic that I'm so excited about

35:00

now. So again,

35:02

earlier in my career, I thought, you

35:04

know, the brain, of course, is a

35:06

wonderful collective computation entity, one of the

35:08

most beautiful examples, you know, how does

35:10

the brain make decisions? And

35:13

it's a collection of neurons. And of

35:15

course, we have ant swarms or locus

35:17

swarms or bird flocks or fish skulls,

35:19

all of these different components interacting together.

35:21

So is there something deeply connecting

35:24

these different systems or not?

35:27

And what I'm fascinated at the

35:29

moment about is collective

35:31

decision making, and especially collective

35:33

decision making in space. So

35:36

how does the brain represent space time?

35:39

And how does that matter in terms of decisions?

35:41

And what on earth does that have to do

35:43

with collective behavior of animals? What

35:45

I realized about five years ago, is that

35:48

I think there's a deep mathematical

35:50

similarity. And I think there are

35:52

deep geometric principles about how the

35:54

brain represents space, and also

35:56

time. And one of the

35:58

most exciting things here is use of

36:00

mathematics again. I dropped a

36:05

sabbatical at Isaac Newton Institute for Mathematical

36:07

Sciences. It came to university as a

36:09

distinguished fellow yet I can't solve an

36:11

equation. But I love

36:16

the fact that I can work with

36:18

amazing mathematicians and by

36:20

working with physicists and mathematicians

36:22

and biologists and by conducting

36:24

experiments on animals in virtual

36:26

reality we've built a

36:28

suite of technologies here so we

36:30

can put a headset like a metiquest 3

36:33

on a fish that's less than a centimeter long but

36:36

we can create virtual immersive holographic

36:38

environments so we can completely control

36:41

the input we can completely control

36:43

the causal relationships if you

36:45

know I'm influencing you and you're influencing me

36:48

and then there's a third individual are they

36:50

influencing me directly or via you or both

36:53

or a fourth individual or fifth in

36:55

a now virtual reality environments we can put

36:57

these individuals into what we call the matrix

37:00

like in the movie where each individual

37:02

is in his own holographic world

37:04

and interacting in real time with

37:06

holograms of other individuals but

37:09

in this world we can play around

37:11

with the rules of physics we can play

37:13

around with the rules of space and time

37:15

to understand better how does the brain integrate

37:17

these and so this is really

37:20

blowing my mind because we can show that

37:22

the brain does not represent

37:24

space in a Euclidean way

37:27

it represents space in a non-Euclidean

37:29

coordinate system and we can

37:31

then show mathematically why this is so

37:34

important which is that when you

37:36

start dealing with three or more

37:38

options than actually walking space time

37:40

making space on Euclidean can dramatically

37:43

reduce the complexity of the world into

37:45

a series of bifurcations and

37:48

close to each bifurcation amplifies differences

37:50

between the remaining options so

37:52

this this beautiful internal structure

37:55

and so we think we have this universal

37:57

theory of how the brain makes special decisions

38:00

that we couldn't have ever got at without

38:02

looking at a range of organisms

38:04

like fish and locusts and flies

38:07

within these types of virtual reality environments. And

38:09

so that's what I'm super excited about. Well,

38:14

I can't wait to hear about all of

38:16

this as you work it out. I could

38:18

go on with you all day, but I

38:20

think it is time to say thank you.

38:22

We've been speaking with evolutionary ecologist Ian Cousin

38:25

about hawking, swarming, schooling, and all

38:27

sorts of collective behavior. Ian, it's

38:29

been such a pleasure learning about

38:31

what you're up to and the

38:33

wonders of nature that you've helped

38:35

unravel for us all. Thanks very

38:37

much. It's been a pleasure. Thanks,

38:39

Steve. You

39:00

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