We simulated if you can really reach anyone in 6 steps

- In 1999, the German newspaper "Die Zeit"

ran an experiment.

They asked a falafel salesman

and former theater director,

Salah ben Ghaly,

who in the world he would most like to be connected to.

He chose his favorite actor, Marlon Brando.

So the reporters then searched for a chain of friends,

family or acquaintances,

people who knew each other

on a first name basis,

who could connect ben Ghaly to Brando.

As it happens, ben Ghaly had a friend in California.

This friend worked alongside the boyfriend of a woman

who was the sorority sister

of the daughter of the producer

of the film "Don Juan DeMarco,"

starring Marlon Brando.

So in total, it took just six steps,

six degrees of separation.

And the idea is that this is not a unique example,

that you could connect any two people on the planet

in six steps or less.

But is it really true?

And if it is,

how does it affect our lives?

- How is this possible

in a world of now eight billion people

that we could be that close,

just six hops or less?

Does that affect how diseases spread,

how information travels?

Our math showed, the question is not why is the world small,

it's really how could it be otherwise?

But then I got a call from the FBI.

We are making the world smaller all the time.

Like it's supposed to be good,

and yet it does expose you to toxicity and malevolence

that you might've been shielded from.

- You look at the net effect of it,

and it's actually been pretty negative

by a lot of measures.

People have suffered.

- It's not only dangerous

in terms of disease propagation,

but anything malevolent

now has conduits that it didn't used to have.

- If we were all connected

to everyone else on the planet

completely at random,

then it would be almost a mathematical certainty

that any two of us would be connected

through fewer than six steps.

- Let's suppose I have my 100 friends

out of eight billion people.

Each of them knows 100 people.

So two steps away from me

is gonna encompass 100 times 100 people.

That's already 10-to-the-fourth people.

And so if you do 100 to the fifth power,

that's 10 to the 10th,

and that's more people than there are on Earth.

So notice that number is five,

I said, "To the fifth power."

That's the ballpark reason

why six degrees (laughing) of separation is true.

- But the shocking thing about this is,

the calculation you've just outlined

is about having 100 friends at random out of 10 billion

and they're all over the world,

but we know that in the real world,

that's nowhere near

what the distribution of friends are like.

- Yeah, absolutely true.

So this really crude calculation I did is absurd,

for the reason that you said.

The world is very far from random.

- The truth is people naturally cluster geographically.

Most of the people you know live close to you,

and they also have a higher probability

of knowing each other.

If you calculate the fraction of people you know

who also know each other,

that is a measure of the clustering in the network.

So let's try a model with a high degree of clustering.

Imagine all eight billion people on Earth

are arranged into a circle,

and say each person knows the 100 people closest to them.

So 50 to the left and 50 to the right.

Well, in this case,

the furthest person you can connect to

is just 50 people away.

So if you wanted to connect to someone

on the other side of the planet

through a chain of people who know each other,

well, it would take 80 million steps,

and to connect any two people

would take on average 40 million steps.

Even just getting 10% to the way there

would take eight million steps.

And six steps would get you,

well here.

(inquisitive music)

This is the paradox of six degrees of separation.

We know that we live in these local clusters

of friends and acquaintances,

but we also seem to be able

to connect anyone anywhere in just six steps.

10 years ago, I did my own experiment on this,

and I found that the average Veritasium viewer

was only 2.7 degrees of separation from me.

In social science,

this is known as the small-world problem,

named after the phenomenon

where you're say on holiday somewhere

and you bump into a stranger

who somehow knows your best friend,

and you say, "Wow, it's such a small world."

(light music)

In the mid-1990s, two mathematicians,

Duncan Watts and Steve Strogatz,

set out to solve this small-world problem.

- Duncan really sort of had very far-seeing imagination

at that point.

- We had computers that allowed us

to simulate environments

that were too complicated for for math to work.

- [Derek] Up until then, physicists had studied networks

that were ordered and regular,

like crystal lattices.

And mathematicians, like Paul Erdos,

had done lots of work

on totally random networks,

but no one had studied what happens in between.

- There must be some enormous middle ground,

and that's what Duncan and I felt like

we're starting to explore.

- [Derek] To study this middle ground,

Watts and Strogatz imagined

a simple regular network of people,

or nodes,

dotted around a circle,

each connected to a few of their nearest neighbors.

- And so we had this idea,

"We're gonna start with the physicist end of regular,

and now we're gonna turn the randomness knob

to make it more and more random

through these random shortcuts."

- [Derek] We all have some experience with shortcuts.

- I belonged to this club called the Internet Chess Club.

I got to be very friendly with a guy in Holland.

That connection makes the world small,

because now, even though my friends don't realize it,

they're only one step away from a guy in Holland.

And so that kind of connection

where you sort of connect to someone

outside your normal circle

is what we came to call a shortcut.

- [Derek] So they went round the circle,

disconnecting some of the links

and reconnecting them at random

to a different node in the network.

And as they did that,

they watched what happened

to the average number of steps it took

to get from any one node in the network to another,

hopping between connected nodes.

In other words, the degree of separation.

- This is now the moment for the big reveal.

As Duncan turned the knob in his computer simulations,

as soon as he introduced a few shortcuts,

the world immediately gets as small as a random graph.

- [Derek] When they had rewired

just 1% of the links to shortcuts,

the average degree of separation

dropped from 50 in the original fully ordered network

to 10.

But Watts and Strogatz also tracked

how clustered the network was.

That's the fraction of a node's connections

that are also connected to each other,

or in other words, the fraction of my friends

who are also friends with each other.

What they found is that clustering

remained high for much longer.

- The world immediately gets as small as a random graph,

but it stays as clustered

as if it were still regular.

So you could simultaneously

have the clustering that we know is real

and the small world that we know is real.

- Now, in Watts and Strogatz's model,

they looked at 1,000 nodes,

but if you apply their model

to the eight billion people on Earth,

well, then you would only need three

out of every 10,000 friendships to be a shortcut,

and the average degrees of separation drops to six.

- Our math showed,

the question is not why is the world small,

it's really how could it be otherwise?

Duncan started saying to me,

"This is about discovering a whole new universe,

and its properties and laws."

I recognized that he was right.

- I just wanted to sort of reflect

on what you said about these sort of shortcuts.

I think I've had this phenomenon happen to me

sometimes in my life

where I'm sort of invited to an event

and it seems like a very random event.

Often I kind of feel like,

(sighing) "I don't really wanna go."

(Steven laughing) "You know, none of my friends

are going."

But then, maybe at the last minute,

I just say like, "Well, let's just roll the dice."

And I find that almost invariably

those are productive meetings.

I'm kind of wondering if there's a takeaway for people here,

which is that they should put themselves in situations

where the probability of forming these shortcut links,

would it sort of increase the luck in your life?

- You have just put your finger

on a very famous phenomenon in sociology

that is called the strength of weak ties.

'Cause you ask people how they got their job,

and people would say, "Oh yeah,

I heard about it from, you know, Randy."

And then he'd say, "Oh, is Randy a friend of yours?"

and people invariably would say,

"No, he's an acquaintance.

I wouldn't call him a friend,

he's an acquaintance."

That's a weak tie.

The strong tie is your best friend

or your circle of friends.

- [Derek] Excited about their breakthrough,

Watts and Strogatz wanted to test their small-world model

on some real-world data,

but this was 1996.

- [Steven] We had to think,

"Well, where are we gonna get data

on big networks where we could test this?"

And it was not so easy,

the internet was not mapped out,

Google didn't exist.

- [Derek] So they turned to an unusual source.

- [Steven] There was only one nervous system

that had been mapped at that time,

which was the worm, C. elegans.

Tiny worm, like a millimeter,

that you can find in the dirt.

A favorite of neurobiologists.

They knew every cell in the body of C. elegans

from the time it's a single cell

'til it becomes a whole organism.

So they had the total wiring diagram of that organism.

- [Derek] Watts and Strogatz tested their model

on the worm's neural network.

The worm has precisely 282 neurons,

and on average, they're connected to 14 others.

If you lay that all out in a line

along the worm's body,

the neurons at the ends

would be separated by around 40 steps,

and the average degree of separation

would be around 14.

But when Watts and Strogatz ran the calculations,

they found the average degrees of separation

between any two neurons was just 2.65.

To put that in context,

if they were connected totally at random,

it would be 2.25.

- [Steven] And yes, okay, so bingo,

that was a small world.

Then we were popping the champagne.

I mean, that was really exciting

that nature had done that.

So then we thought, "Well, okay,

but this should be true of lots of networks

because nature can't resist this mechanism."

- [Derek] So they looked at Hollywood actors

and power grids across the US.

Sure enough, they were both small-world networks.

For example, in the database

of over 200,000 Hollywood actors,

the average degree of separation was less than four.

Dangerfield was in "Caddy Shack"

with Bill Murray,

and Bill Murray was in "She's Having a Baby"

with Kevin Bacon.

- [Steven] Then the real payoff for us,

as people interested in dynamical systems

more than graph theory,

was, "Okay, so what?

You know, so what if the world is small?

Does that affect how things get in sync?

Does it affect how diseases spread?

Does it affect how information travels?

Whatever."

And so we did a number of experiments,

again, in the computer, like that.

- [Derek] Take disease.

I wanted to know how a few shortcuts

would affect how disease spreads through a network.

So I asked Casper and the team

to make a simulation.

- [Casper] And then the question to you is,

do you wanna start with a completely regular world

where it's completely clustered

or do you wanna start with completely random?

- [Derek] I would start with a regular world.

- [Casper] Okay.

There it goes.

- [Derek] There's the spread of infections.

- [Casper] Yeah.

- Wow. - So it takes over the world,

completely.

Well, if every step was a day,

it would take 73 days for the, you know, infection

to take over this entire world.

- [Derek] Well, let's introduce

a few shortcuts and see.

- [Casper] Okay, let's make it small world, like 10%.

Let's go.

- [Derek] Boom.

Wow, that's really dramatic. - (laughing) Whoa.

Right? - That's really dramatic

and very fast. - Yeah, so fast.

Yeah, after 26 days,

the whole world.

- [Derek] And that ramp up

does look exponential at the beginning.

- Right? - And then it kind of looks

linear there as well,

but it's almost like you can't go any faster.

- [Casper] Yeah.

Okay, so now let's make it a completely random network.

(inquisitive music)

- [Derek] Boom.

- Boom. - Crazy.

How many days now for a fully random network?

- [Casper] 25.

- [Derek] Basically identical.

- [Casper] Which is crazy because in the random case,

all your links are random.

You know, in the small-world case,

it's just 10%.

It's like if one out of your 10 friends are a shortcut,

which, you know, for some people might be a bit much,

but I reckon for you,

it's probably about right.

- [Derek] Yeah, I got lots of shortcuts.

(Casper laughing)

- [Casper] But the crazy thing is

that in this simulation,

we only use 100 nodes.

And if you use the same model

to the eight billion people on Earth,

then you would actually need less than 1%

of all your links to be shortcuts.

- [Derek] In 1998, Watts and Strogatz

published their findings

in a three-page article in "Nature,"

and the paper took off.

Within a few years,

the paper already had hundreds of citations.

By 2014, it was ranked the 63rd-most-cited paper

of all time.

And today, it's got around 58,000 citations.

That's higher than Peter Higgs' paper on the Higgs boson,

and almost three times as many

as Watson and Crick's Nobel Prize-winning paper on DNA.

- [Steven] So it's probably worth making that distinction

that citations are one measure of impact.

We're cited a lot more than Einstein,

and I think you know who's more important?

(Derek laughing)

It's not us,

but it does mean people thought it was worth citing.

We had many tens of thousands of citations

from people in far-flung fields,

from neuroscience, to sociology,

to graph theory, to computer science.

Even, you know, English literature,

people would do things like draw networks between words.

- [Derek] Is there any irony in the fact that this paper

on global networks goes viral itself?

- [Steven] (laughing) Yes, I think so, maybe so.

(record scratching)

- [Derek] But then things got a little weird.

- [Steven] That's when I started getting

some strange phone calls.

I got a call from somebody at the FBI.

I was a little scared,

"What's the FBI calling me about?"

And so I called back,

and the person who picks up says,

"Hair and Fiber."

(laughing) I was calling the Hair and Fiber network

at the FBI,

the people who do you know criminology

based on what telltale hairs or fibers

are left on the victim's clothes

after they've been murdered.

There was a guy who said,

"What happens when the police have a suspect

and they say, "You have fibers on your sweater

that match the hair of the victim,"

and then the defense lawyer says,

"Well, you know, maybe the victim was on a bus

and left her fibers on the bus,

and then my client sat on the,

a secondary transfer," they would call it, "of these fibers,

that doesn't prove anything?"

So the FBI wanted to know,

what's the probability of secondary transfers

compared to primary transfers

from actually killing the person?

And like I said, "Well, I don't, what do I know now?"

(Steven and Derek laughing)

- [Derek] Now, that's a Steve Strogatz problem.

For most of us, a random caller

telling you they're an FBI agent

is probably a scam.

Chances are they got your number

from a data leak or a data broker.

Anytime you provide your name, phone number,

even your Social Security number,

that personal information can be scraped,

packaged and sold to anyone who will pay.

And if criminals get hold of it,

well, they can open credit card accounts in your name

or even use it to stalk or harass you.

Fortunately, today's video sponsor, Incogni, can help.

With your permission,

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So to keep your information safe,

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So I wanna thank Incogni

for sponsoring this video.

And now back to networks.

In 1998, Albert-Laszlo Barabasi

was studying the internet.

At that time, there were around 800 million webpage,

but despite the web's enormous size,

Barabasi found that on average

you could connect any two sites

with just 19 clicks.

Apparently the web was a small world too,

but the strange thing was,

it didn't look anything like the small-world network

in Watts and Strogatz's model.

- [Albert-Laszlo] We ended up mapping out

a region of World Wide Web,

and we had a very clear expectation

of how that network should look like.

- [Derek] Barabasi thought the distribution

of pages and links

would resemble a bell curve,

similar to what you'd get

for people's height across a population.

Most sites would have some average number of links

and there would be very few outliers either side.

But that is not what he saw.

(inquisitive music)

- [Albert-Laszlo] And so we measured the distribution,

and it didn't look anything like

what we expected. (laughing)

- [Derek] The curve started out steep.

Loads of websites had not many links.

Then there was this really long tail.

- [Albert-Laszlo] And here we saw webpages

that had not only a little more,

but sometimes 100 times more links than the average degree

or the average node on the website.

- [Derek] These were websites like Yahoo,

super-connectors that linked to thousands of other sites.

Barabasi called them hubs,

because when he mapped out the network,

they resembled the hub of a wheel,

with spokes going out to hundreds of other pages.

And it was these hubs

that made the web a small world,

not shortcuts.

So Barabasi wondered,

"How could this apply to other networks too?"

- [Albert-Laszlo] Most real networks,

or virtually all large networks,

follow two very fundamental principles.

First, any large network out there

never pops out as a large network,

but it grows, right?

You have a tiny World Wide Web in 1991,

and now we have trillions of nodes

on the World Wide Web.

How did we get one to a trillion?

One node at a time,

one website at a time.

All the networks out there,

no matter how old,

how fast they emerged,

they always emerged

with some kind of growth process.

So if you think about networks,

you must build in that growth process.

Number two, when a new node comes in,

you join Facebook,

whom you gonna connect to, right?

And it is somewhat unpredictable,

but it's biased.

Your connections are always biased

towards the more connected nodes,

simply because you are more likely to know

more connected node than less connected node.

We named this process preferential attachment.

- [Derek] Barabasi reasoned that these two principles

could explain how hubs naturally emerge

when a network grows.

So together with his colleague, Reka Albert,

he ran a simulation.

- [Casper] We've also got a simulation for this.

They started with a simple network

of just a few connected nodes.

Then they begin adding new nodes to the network

one at a time,

with just one condition,

they'd be more likely to connect the nodes

that already had more links.

- [Derek] That's so cool.

Looks very biological, very organic.

Also, I like how the nodes come out

and they don't just sort of stop in one spot,

they kind of like wiggle around

and like find their location.

I really like enjoy this.

Kind of like a space station.

- [Casper] That's what I was thinking.

- [Derek] Or like a space colony, right?

- [Casper] Yeah, yeah, right?

Like each little center one could be a planet,

then you've got all the sort of stations

going around it.

- [Derek] Yes.

- [Casper] So when Barabasi and Albert

let these networks evolve,

hubs emerged.

- [Albert-Laszlo] And we showed that growth

and preferential attachment together

naturally lead to the emergence of hubs.

- [Derek] With this simulation,

Barabasi and Albert showed how hubs

could emerge in virtually any complex network.

Take airports for example.

In 1955, Chicago O'Hare opened to commercial flights.

Unlike neighboring airport, Midway,

it had long runways

and plenty of space for new jet aircraft.

Airlines began shifting service there.

As more airlines connected flights to O'Hare,

passengers had more options to connect,

making it increasingly attractive.

After deregulation in the 1970s,

more airlines were free to add routes

and the feedback loop accelerated.

Each new route made the airport more useful to passengers

and more appealing to other airlines.

Today, O'Hare is the most connected airport

in the United States,

with direct flights to well over 200 destinations.

- [Casper] But we don't just see hubs in manmade networks.

In food webs,

you have a few keystone species, like Atlantic cods,

that connect hundreds of predators and prey.

And in the metabolic networks in our cells,

you have a few molecules, like ATP,

that govern hundreds of chemical reactions.

In the neural networks in our brain,

you have a few regions,

like the prefrontal cortex

that link hundreds of different functions.

Now, as each of these networks evolved

and grew over time,

you had new species,

new reactions and new circuits

that latched on to what was already well connected.

And so you get this sort of natural growth.

Now, preferential attachment

isn't the only mechanism that can create hubs.

There are plenty of other factors at play,

particularly in these more complex biological systems.

But what Barabasi's and Albert's simulation showed

is that all it takes is a tiny bias

when growing a network

and hubs end up being inevitable.

- [Albert-Laszlo] Once hubs are there,

they fundamentally change (laughing)

the way the system behaves

and the way we understand that system.

- [Derek] Hubs like O'Hare

mean you can get pretty much anywhere in the world

in just a few flights.

But that connectivity also has consequences.

In August, 2025,

thunderstorms shut down Chicago O'Hare,

and 280 flights were canceled

and 80 were diverted.

Overflow hit at least six other US airports.

While some planes stuck in Chicago

never left for Europe or Asia.

- [Albert-Laszlo] Bad weather in Chicago

totally changes not only the the travel pattern in Chicago,

but within 24 hours,

the whole country is being affected by that.

- [Derek] And we see the same phenomenon

in natural networks,

knocking out one keystone species,

like Atlantic cod,

can destabilize an entire ecosystem.

- [Albert-Laszlo] So this is what we call

the Achilles' heel of networks.

And this could be good news

or it could be bad news, right?

Good news if you wanna create drugs to kill bacteria,

then you're gonna go for the hub.

- [Derek] This idea has created

a whole new field of network medicine

where researchers develop drugs

to target crucial parts of a disease's metabolic network.

But understanding the role of hubs

doesn't just help develop cures for a disease,

it can help us control its spread.

(inquisitive music)

In 1990, Thailand was facing

one of the fastest-growing HIV epidemics in the world.

The government tried broad campaigns,

like posters, TV ads and school talks,

telling everyone to use condoms.

But the infection kept spreading.

So in 1991, the government tried something different.

They started targeting hubs.

They told brothels around the country

that every client must use a condom

or else they'd be shut down.

And the impact was huge.

For example, HIV infections among young men

joining the military

dropped by more than 50%.

And by 2013, Thailand's Ministry of Public Health

estimated the policy had prevented

over five million infections.

All because they realized the importance of hubs.

(upbeat music)

- [Casper] Hubs and shortcuts make any complex network

more connected than it seems.

That means things spread quickly,

whether that's airport delays, information or disease.

But could that impact run even deeper?

I mean, could the structure of our social network

influence our very behavior and beliefs

without us even being aware of it?

- [Derek] Back in 1997,

Watts and Strogatz investigated just that,

using a game called the prisoner's dilemma.

It's probably the most famous problem in game theory,

and it's used to represent a ton of different conflicts

we see in the real world.

We've actually done a full video on it before,

but here's a quick recap.

The premise is simple.

A banker with a chest full of gold

invites you and another player to play.

You each get two choices,

you can cooperate or defect.

If you both cooperate,

you each get three coins.

But if you defect while your opponent cooperates,

you get five coins and they get nothing.

And if you both defect,

then you each get one coin.

So what would you do?

Suppose your opponent cooperates,

then you could also cooperate

and get three coins,

or you could defect and get five coins instead.

So you're better off defecting.

But what if your opponent defects?

Well, you could cooperate and get no coins

or you could defect and at least get one.

So no matter what your opponent does,

your best option is always to defect.

Now, if your opponent is also rational,

they'll reach the same conclusion

and therefore they'll also defect.

And as a result, when you both act rationally,

you both end up in the suboptimal situation

of getting one coin each

when you could have gotten three.

(bright music)

But in 1980, Professor Robert Axelrod

found that if you play your opponent hundreds of times,

well, then cooperation wins out.

He ran a tournament among the world's leading game theorists

and all the most successful strategies were nice.

The winning strategy was called tit for tat,

because its default position was to cooperate

and it would only defect in retaliation.

He also showed that a small cluster of cooperators

can work together to overcome a world of defectors.

- [Casper] So that's kind of the scene we're setting, right?

You get to this realistic place

where you get tit-for-tat life strategies

to sort of dominate the world,

because in Axelrod's tournament,

every strategy played against every other strategy,

or they only interacted

sort of with their near neighborhood,

which is, you know, the small cluster.

And now you could wonder,

"Well, what if we start changing the way this works?

What if we put them on a network?"

- [Derek] Well, Watts and Strogatz

simulated their own version

of the prisoner's dilemma that did just that.

They set up a regular network

where each player was connected

to a few players on either side.

Then they would simultaneously play

against all of their connections.

The rules were simple.

If most of a player's connections cooperated,

then that player would also cooperate.

But if most of their connections defected,

then they would defect in retaliation.

They started with a small cluster of cooperators

surrounded by defectors,

and they watched the network evolve.

Over time, what they saw was cooperation spread,

just like what Axelrod had found.

But then they reran the simulation,

this time with a few links rewired to shortcuts.

And all of a sudden,

the cooperators were crushed

and they ended up with a world of defectors.

(defectors whirring)

And when they started from a totally regular network

and gradually increased the fraction of shortcuts,

they found there was this critical fraction

beyond which the percentage of cooperators

at the end of the game

drops to zero.

- I think the thing that's really crazy

is that you've taken the exact same strategies

with all the same properties,

same character traits and personalities, if you will,

and you're not changing any of that.

All you're changing is the way they're connected,

and you go from a world where everyone's completely nice

and working together

to one where it's filled with nastiness

and people betraying each other,

only by changing how they're connected.

- [Derek] It's like if the bulk of your interactions

are sort of negative,

then you start being negative too

and you just contribute to the overall negativity.

Whereas if like a few people are nice,

and you can imagine, "Oh, that makes me feel good

and so I'm gonna be nicer to that person."

- [Steven] The intuition for that is that cooperation

is fostered by having little clumps.

If I have a little clump of people

that are kind of my buds,

we get to have a lot of encounters,

and cooperation tends to emerge from familiarity,

the same way that iteration helps,

that if I know that I'm gonna see you again,

I'm gonna encounter you again,

it ends up being to my advantage to cooperate.

Whereas like the world of the internet,

where anyone can get on Twitter

and badmouth anyone else,

that tends to discourage.

We don't have pockets,

you don't have communities.

- [Derek] It kind of explains

this keyboard warrior phenomenon,

and that, yeah, people say things on the internet

they wouldn't say to-- - They wouldn't.

Most people are nice in real life.

- [Derek] (laughing) Yeah.

- [Steven] It's funny that the small world,

I know, you'd think the small world's,

well, 'cause it's a Disney song, right?

♪ It's a small world after all ♪

Like it's supposed to be good,

and yet it does expose you to toxicity and malevolence

that you might've been shielded from

in the small town.

Social media has kind of been

toxic.

The initial idea being

'hey we connect up a bunch of people and people who have been

separated geographically

we connect you with your old friends.'

You look of the net effect of it and it's actually been

pretty negative by a lot of measures.

Intrigued by the findings,

Watts started wondering if the results

applied to the real world too.

For years after I did this work

I had wanted to test the hypothesis

with actual human subjects

So he got some volunteers to play a similar game

called the public goods game across different network structures

He was expecting that, like they found previously,

more shortcuts in a network,

would make cooperation less likely to emerge.

But what he found was, the structure of the network

had no effect.

Cooperation was just as likely to emerge in a totally clustered network

as it was in a totally random one.

- [Duncan] We were very puzzled by this result.

And then we kind of did some more work.

- [Derek] When Watts dug deeper,

he realized that the network structure did matter.

In the more clustered networks, people were more likely

to copy each other.

So if by chance someone started out cooperating,

then everyone would cooperate.

But it was equally likely that someone would start out by defecting,

in which case everyone else would defect.

And over all the games they played, these two effects canceled each other out,

which is why it seemed like

the network structure didn't matter.

- [Duncan] It's sort of on a knife edge, right?

Where like one person does something selfish

and everything goes south.

In another world, everybody kind of holds it together

and everything goes well.

It's crazy that the world

could be like on a knife edge like that, you know,

could tip one way or the other,

kind of just depends on how someone gets out of bed that day.

But then Watts realized something.

See, in real life,

you can choose who you hang out with.

So he reran the experiment

allowing players to change who they were playing with.

And this time he used the prisoner's dilemma

so that players could easily identify the defectors.

- [Derek] And the finding was clear, the more you allowed players

to choose who they were playing with,

the more likely they were to cooperate.

- [Casper] You can make this a lot better for yourself by just

acting and being decisive

and being proactive about things.

- [Derek] Yeah, yeah, it's a thing I try

to teach my kids too, like if someone's annoying you, just ignore them.

Like there's nothing to be gained by continuing to interact with people

who are bringing negativity into your life.

- [Casper] In fact, making a choice can be powerful

in more than one way.

There's something about the world

that makes it prone to those upheavals.

Meaning it's always kind of poised on an edge of instability.

And that gives each of us more power than you'd think we would have.

It is actually possible for individual people

to start movements that grow and take off.

And ultimately, if you look at history,

that is what happens.

It's always one person who is stubborn and does something

that leads to 10 people, 1,000 people,

and things change because of it.

It always starts with one person somehow.

It's the Steve Jobs quote, right?

The people who are crazy enough to think they can change the world

are the ones who do.

And the wonderful thing is, it all starts with you

believing you have that power. - Yeah

Learning all about network science has taught me many things,

but perhaps the most important

is that our networks shape us,

but our actions shape the networks.

So choose both wisely.

- [Casper] Hey, if you made it this far,

all the simulations we ran through with Derek,

we will actually make them available

on a website that you can go to so you can play around with them yourself.

So thank you so much for watching,

we really appreciate it.

And yeah, see you for the next one.