I`m Hannah Fry, the badass,

and today I`m asking the question:

Is life really that complex?

Now, I`ve only got 9 minutes to provide you with an answer.

So, what I`ve done is split this neatly into two parts:

Part one - Yes,

and later on, part two - No, or, to be more accurate:

No? Okay, so first of all let me try to find what I mean by complex.

Now I could give you a host of formal definitions,

but, in the simplest terms, any problem and complexity

is something that Einstein and his peers can`t do.

So, let`s imagine, if the clicker works, there we go.

Einstein is playing the game of snooker.

He`s a clever chap, so he knows that when he hits the cue ball

he could write you an equation

and tell you exactly where the red ball is gonna hit the sides,

how fast it`s going and where it`s gonna end up.

Now, if you scale these snooker balls up to the size of the solar system,

Einstein can still help you, sure, the physics changes,

but, if you wanted to know about the path of the Earth

around the Sun, Einstein could write you an equation

telling you exactly where both objects are at any point in time.

Now, with a surprising increase in difficulty

Einstein could include the Moon in his calculations,

but, as you add more and more planets, Mars and Jupiter, say,

the problem gets too tough for Einstein to solve with a pen and paper.

Now, strangely if instead of having a handful of planets

you had millions of objects,

even billions, the problem actually becomes much simpler

and Einstein is back in the game.

Let me explain what I mean by this,

by scaling these objects back down to a molecular level.

If you wanted to trace the erratic path of an individual air molecule

you`d have absolutely no hope,

but when you have millions of air molecules all together

they start act in a way which is quantifiable

predictable and well behaved, and, thank Goodness, air is well behaved

because if it wasn`t planes would fall out of the sky.

Now, on an even bigger scale, across the whole of the world, the idea

is exactly the same with all of these air molecules.

It`s true that you can`t take an individual rain droplet

and say where it`s come from, where it`s gonna end up

but you can say with pretty good certainty whether it`s gonna be cloudy tomorrow.

So, that`s it. In Einstein`s time this is how far science has got.

We could do really small problems with a few objects,

with simple interactions, or you could do huge problems

with milions of objects and simple interactions.

But what about everything in the middle?

Well, just seven years before Einstein`s death

an American scientist called Warren Weaver made exactly

this point. He said that scientific methodology

has gone from one extreme to another

leaving out an untouched great middle region.

Now, this middle region is where complexity science lies

and this is what I mean by complex.

Now, unfortunately, almost every single problem you can think of

to do with human behaviour lies in this middle region.

Einstein`s got absolutely no idea how to model the movement of a crowd,

there are too many people to look at them all individually

and too few to treat them as a gas.

Similarly, people are prone to annoying things like

decisions of not wanting to walk into each other

which makes the problem all the more complicated.

Einstein also couldn`t tell you when the next stock market crash is going to be,

Einstein couldn`t tell you how to improve unemployment,

Einstein can`t even tell you

whether the next iPhone is going to be a hit or a flop.

So, to conclude part one, we`re completely screwed,

we`ve got no tools to deal with this and life is way too complex.

But, maybe there`s hope, because in the last few years

we`ve begun to see the beginnings of a new area of science

using mathematics to model our social systems

and I`m not just talking here about statistics and computer simulations,

I`m talking about writing down equations about our society

that will help us understand what is going on

in the same ways with the snooker balls or the weather prediction.

And this has come about because people have begun to realise

that we can use and exploit analogies

between our human systems

and those of the physical world around us.

Now, to give you an example of the incredibly complex problem

of migration across Europe.

Actually, as it turns out when you view all of the people together

collectively they behave as though

they`re following the laws of gravity.

But instead of planets being attracted to one another,

it`s people who are attracted to areas with better job opportunities,

higher pay, better quality of life and lower unemployment.

And in the same way as people are more likely to go for opportunities close

to where they live already, London to Kent, for example,

as opposed to London to Melbourne,

the gravitational effect of planets

faraway is felt much less.

So, to give you another example, in 2008 a group in UCLA

were looking into the patterns of burglary hot spots in the city.

Now, one thing about burglaries is this idea of repeat victimization.

So, if you have a group of burglars who manage to successfully rob an area,

what they`ll do is they`ll tend to return to that area and carry on burgling it,

so they learn the layout of the houses, the escape routes

and the local security mesures that are in place,

and this will continue to happen

until local residents and police ramp up the security

at which point the burglars will move off elsewhere.

And it`s that balance between burglars and security

which create these dynamic hot spots of the city.

As it turns out, this is exactly the same process

as how a leopard gets its spots,

except in the leopard example it`s not burglars and security,

it`s the chemical process that

creates these patterns and something called morphogenesis.

We actually know an awful lot about the morphogenesis of leopard spots.

Maybe we can use this to try and spot some of the warning signs with burglaries

and perhaps, also to create better crime strategies to prevent crime

and there's a group here UCL who are working

with the West Midlands police right now on this very question.

I could give you plenty of examples like this but

I wanted to leave you with one from my own research

on the London riots.

Now, you probably don`t need me to tell you about the events of last summer

where London and UK saw the worst sustained period of violent

looting and arson for over twenty years.

It`s understandable that as a society we want to

try and understand exactly what caused

these riots but, also, perhaps to equip our police

with better strategies to lead to a swifter resolution in the future.

Now, I don`t want to upset the sociologists here,

so I absolutely cannot talk about

the individual motivations for a rioter

but when you look at the rioters all together,

mathematically you can separate it into a three stage process

and draw analogies accordingly.

So, step one. Let`s say you`ve got a group of friends,

none of them are involved in the riots.

But one of them walks past a Foot Locker which is being raided

and goes in and bags himself a new pair of trainers.

Now, he texts one of his friends and says, you know,

"Come on down to the riots." So his friend joins him,

and then the two of them text more of their friends

who join them and text more of their friends and more

and more and so it continues.

This process is identical to the way that a virus spreads through a population.

If you think about the bird flu epidemic a couple of

years ago, the more people that were infected,

the more people that got infected and the faster the virus spread

before the authorities managed to get a handle on events.

And it`s exactly the same process here.

So, okay, let`s say you`ve got a rioter,

he`s decided he`s gonna riot,

the next thing he has to do is pick a riot site.

What you should know about rioters

is that they`re not really prepared to travel out far

from where they live unless is a really juicy riot site.

(Laughter)

So you can see that here, from this graph

that an awful lot of rioters having traveled less than a kilometer

to the site that they went to.

Now, this pattern is seen in consumer models of retail spending,

where we choose to go shopping.

So, of course, people like to go to local shops

but you`d be prepared to go a little bit further

if it was a really good retail site.

And this anology actually was already picked up by some of the papers,

with some tabloid press

calling the events "Shopping with violence"

which probably sums up in terms of our research.

Uhm, oh, I`m going backwards.

Okay, step three. Finally, the rioter is at his site

and now he wants to avoid getting caught by the police.

The rioters will avoid the police at all times

but there is some safety in numbers, and on the flip side

the police with their limited resources,

are trying to protect as much of the city as possible,

arrest rioters wherever possible and to create a deterrent effect.

Actually, as it turns out, this mechanism between the two species,

to speak of of rioters and the police

is identical to predators and prey in the wild;

so if you can imagine rabbits and foxes,

rabbits are trying to avoid foxes at all costs,

while foxes are patrolling the space

trying to look for rabbits.

We actually know an awful lot about the dynamics of predators and prey,

we also know a lot about the consumer

spending flows and we know a lot about

how viruses spread through a population.

So, if you take these three analogies together and exploit them

you can come up with a mathematical model

of what actually happened,

that`s capable of replicating the general patterns

of the riots themselves.

Once we`ve got this we can almost use this as a petri dish

to start having conversations about which areas

of the city were more susceptible than others

and what police tactics could be used

if this were ever to happen again in future.

Even twenty years ago modelling of this sort

was completely unheard of, but I think that

these analogies is an incredibly important tool in tackling

problems with our society and perhaps,

ultimately improving our society overall.

So, to conclude: life is complex, but perhaps

understanding it need not necessarily be that complicated.

Thank you!

(Applause)