Transcriber: Lia Apostol Reviewer: Diba Szamosi
Okay, thank you very much!
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 and define 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 going to hit the sides,
how fast it's going and where it's going to 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 to 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 going to end up
but you can say with pretty good certainty whether it's going to be cloudy tomorrow.
So, that's it. In Einstein's time this is how far science had 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 behavior 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 realize
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 measures 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 at 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 going to 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 that 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,
i.e. 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 it up in terms of our research.
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,
so 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
are 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)