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Hannah Fry: Is life really that complex?
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Can an algorithm forecast the site of the next riot? In this accessible talk, mathematician Hannah Fry shows how complex social behavior can be analyzed and perhaps predicted through analogies to natural phenomena, like the patterns of a leopard's spots or the distribution of predators and prey in the wild.
Hannah Fry - Complexity theorist
Hannah Fry researches the trends in our civilization and ways we can forecast its future. Full bio
Hannah Fry researches the trends in our civilization and ways we can forecast its future. Full bio
Double-click the English transcript below to play the video.
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Thanks very much.
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I am Hannah Fry, the badass.
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And today I'm asking the question:
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Is life really that complex?
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Now, I've only got nine minutes
to try and provide you with an answer,
to try and provide you with an answer,
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so what I've done
is split this neatly into two parts:
is split this neatly into two parts:
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part one: yes;
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and later on, part two: no.
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Or, to be more accurate: no?
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(Laughter)
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So first of all, let me try and define
what I mean by "complex."
what I mean by "complex."
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Now, I could give you
a host of formal definitions,
a host of formal definitions,
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but in the simplest terms,
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any problem in complexity is something
that Einstein and his peers can't do.
that Einstein and his peers can't do.
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So, let's imagine --
if the clicker works ... there we go.
if the clicker works ... there we go.
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Einstein is playing a game of snooker.
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He's a clever chap, so he knows
that when he hits the cue ball,
that when he hits the cue ball,
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he could write you an equation
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and tell you exactly where the red ball
is going to hit the sides,
is going to hit the sides,
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how fast it's going
and where it's going to end up.
and where it's going to end up.
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Now, if you scale these snooker balls
up to the size of the solar system,
up to the size of the solar system,
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Einstein can still help you.
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Sure, the physics changes,
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but if you wanted to know about
the path of the Earth around the Sun,
the path of the Earth around the Sun,
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Einstein could write you an equation
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telling you where both objects are
at any point in time.
at any point in time.
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Now, with a surprising
increase in difficulty,
increase in difficulty,
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Einstein could include
the Moon in his calculations.
the Moon in his calculations.
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But as you add more and more planets,
Mars and Jupiter, say,
Mars and Jupiter, say,
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the problem gets too tough for Einstein
to solve with a pen and paper.
to solve with a pen and paper.
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Now, strangely, if instead of having
a handful of planets,
a handful of planets,
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you had millions of objects
or even billions,
or even billions,
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the problem actually becomes much simpler,
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and Einstein is back in the game.
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Let me explain what I mean by this,
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by scaling these objects back down
to a molecular level.
to a molecular level.
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If you wanted to trace the erratic path
of an individual air molecule,
of an individual air molecule,
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you'd have absolutely no hope.
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But when you have millions
of air molecules all together,
of air molecules all together,
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they start to act in a way
which is quantifiable, predictable
which is quantifiable, predictable
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and well-behaved.
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And thank goodness air is well-behaved,
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because if it wasn't,
planes would fall out of the sky.
planes would fall out of the sky.
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Now, on an even bigger scale,
across the whole of the world,
across the whole of the world,
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the idea is exactly the same
with all of these air molecules.
with all of these air molecules.
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It's true that you can't take
an individual rain droplet
an individual rain droplet
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and say where it's come from
or where it's going to end up.
or where it's going to end up.
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But you can say with pretty good certainty
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whether it will be cloudy tomorrow.
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So that's it.
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In Einstein's time,
this is how far science had got.
this is how far science had got.
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We could do really small problems
with a few objects
with a few objects
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with simple interactions,
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or we could do huge problems
with millions of objects
with millions of objects
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and simple interactions.
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But what about everything in the middle?
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Well, just seven years
before Einstein's death,
before Einstein's death,
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an American scientist called
Warren Weaver made exactly this point.
Warren Weaver made exactly this point.
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He said that scientific methodology
has gone from one extreme to another,
has gone from one extreme to another,
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leaving out an untouched
great middle region.
great middle region.
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Now, this middle region
is where complexity science lies,
is where complexity science lies,
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and this is what I mean by complex.
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Now, unfortunately, almost
every single problem you can think of
every single problem you can think of
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to do with human behavior
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lies in this middle region.
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Einstein's got absolutely no idea
how to model the movement of a crowd.
how to model the movement of a crowd.
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There are too many people
to look at them all individually
to look at them all individually
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and too few to treat them as a gas.
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Similarly, people are prone
to annoying things like decisions
to annoying things like decisions
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and not wanting to walk into each other,
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which makes the problem
all the more complicated.
all the more complicated.
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Einstein also couldn't tell you
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when the next stock market crash
is going to be.
is going to be.
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Einstein couldn't tell you
how to improve unemployment.
how to improve unemployment.
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Einstein can't even tell you
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whether the next iPhone
is going to be a hit or a flop.
is going to be a hit or a flop.
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So to conclude part one:
we're completely screwed.
we're completely screwed.
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We've got no tools to deal with this,
and life is way too complex.
and life is way too complex.
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But maybe there's hope,
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because in the last few years,
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we've begun to see the beginnings
of a new area of science
of a new area of science
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using mathematics
to model our social systems.
to model our social systems.
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And I'm not just talking here
about statistics and computer simulations.
about statistics and computer simulations.
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I'm talking about writing down
equations about our society
equations about our society
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that will help us understand
what's going on
what's going on
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in the same way as with the snooker balls
or the weather prediction.
or the weather prediction.
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And this has come about
because people have begun to realize
because people have begun to realize
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that we can use and exploit analogies
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between our human systems
and those of the physical world around us.
and those of the physical world around us.
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Now, to give you an example:
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the incredibly complex problem
of migration across Europe.
of migration across Europe.
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Actually, as it turns out, when you view
all of the people together,
all of the people together,
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collectively, they behave as though
they're following the laws of gravity.
they're following the laws of gravity.
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But instead of planets
being attracted to one another,
being attracted to one another,
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it's people who are attracted
to areas with better job opportunities,
to areas with better job opportunities,
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higher pay, better quality of life
and lower unemployment.
and lower unemployment.
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And in the same way as people
are more likely to go for opportunities
are more likely to go for opportunities
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close to where they live already --
London to Kent, for example,
London to Kent, for example,
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as opposed to London to Melbourne --
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the gravitational effect of planets
far away is felt much less.
far away is felt much less.
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So, to give you another example:
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in 2008, a group in UCLA
were looking into the patterns
were looking into the patterns
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of burglary hot spots in the city.
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Now, one thing about burglaries
is this idea of repeat victimization.
is this idea of repeat victimization.
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So if you have a group of burglars
who manage to successfully rob an area,
who manage to successfully rob an area,
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they'll tend to return to that area
and carry on burgling it.
and carry on burgling it.
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So they learn the layout of the houses,
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the escape routes
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and the local security measures
that are in place.
that are in place.
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And this will continue to happen
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until local residents and police
ramp up the security,
ramp up the security,
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at which point, the burglars
will move off elsewhere.
will move off elsewhere.
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And it's that balance
between burglars and security
between burglars and security
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which creates these dynamic
hot spots of the city.
hot spots of the city.
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As it turns out,
this is exactly the same process
this is exactly the same process
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as how a leopard gets its spots,
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except in the leopard example,
it's not burglars and security,
it's not burglars and security,
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it's the chemical process
that creates these patterns
that creates these patterns
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and something called "morphogenesis."
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We actually know an awful lot
about the morphogenesis of leopard spots.
about the morphogenesis of leopard spots.
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Maybe we can use this to try and spot
some of the warning signs with burglaries
some of the warning signs with burglaries
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and perhaps, also to create
better crime strategies to prevent crime.
better crime strategies to prevent crime.
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There's a group here at UCL
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who are working with
the West Midlands police right now
the West Midlands police right now
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on this very question.
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I could give you
plenty of examples like this,
plenty of examples like this,
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but I wanted to leave you
with one from my own research
with one from my own research
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on the London riots.
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Now, you probably
don't need me to tell you
don't need me to tell you
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about the events of last summer,
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where London and the UK saw
the worst sustained period
the worst sustained period
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of violent looting and arson
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for over twenty years.
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It's understandable that, as a society,
we want to try and understand
we want to try and understand
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exactly what caused these riots,
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but also, perhaps, to equip our police
with better strategies
with better strategies
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to lead to a swifter
resolution in the future.
resolution in the future.
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Now, I don't want to upset
the sociologists here,
the sociologists here,
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so I absolutely cannot talk about
the individual motivations for a rioter,
the individual motivations for a rioter,
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but when you look at
the rioters all together,
the rioters all together,
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mathematically, you can separate it
into a three-stage process
into a three-stage process
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and draw analogies accordingly.
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So, step one: let's say
you've got a group of friends.
you've got a group of friends.
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None of them are involved in the riots,
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but one of them walks past
a Foot Locker which is being raided,
a Foot Locker which is being raided,
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and goes in and bags himself
a new pair of trainers.
a new pair of trainers.
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He texts one of his friends and says,
"Come on down to the riots."
"Come on down to the riots."
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So his friend joins him,
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and then the two of them text
more of their friends, who join them,
more of their friends, who join them,
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and text more of their friends
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and more and more, and so it continues.
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This process is identical to the way
that a virus spreads through a population.
that a virus spreads through a population.
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If you think about the bird flu epidemic
of a couple of years ago,
of a couple of years ago,
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the more people that were infected,
the more people that got infected,
the more people that got infected,
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and the faster the virus spread
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before the authorities managed
to get a handle on events.
to get a handle on events.
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And it's exactly the same process here.
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So let's say you've got a rioter,
he's decided he's going to riot.
he's decided he's going to riot.
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The next thing he has to do
is pick a riot site.
is pick a riot site.
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Now, what you should know
about rioters is that, um ...
about rioters is that, um ...
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Oops, clicker's gone. There we go.
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What you should know about rioters is,
they're not prepared to travel
they're not prepared to travel
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that far from where they live,
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unless it's a really juicy riot site.
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(Laughter)
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So you can see that here from this graph,
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with an awful lot of rioters
having traveled less than a kilometer
having traveled less than a kilometer
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to the site that they went to.
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Now, this pattern is seen
in consumer models of retail spending,
in consumer models of retail spending,
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i.e., where we choose to go shopping.
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So, of course, people like
to go to local shops,
to go to local shops,
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but you'd be prepared
to go a little bit further
to go a little bit further
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if it was a really good retail site.
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And this analogy, actually, was already
picked up by some of the papers,
picked up by some of the papers,
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with some tabloid press calling the events
"Shopping with violence,"
"Shopping with violence,"
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which probably sums it up
in terms of our research.
in terms of our research.
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Oh! -- we're going backwards.
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OK, step three.
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Finally, the rioter is at his site,
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and he wants to avoid
getting caught by the police.
getting caught by the police.
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The rioters will avoid
the police at all times,
the police at all times,
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but there is some safety in numbers.
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And on the flip side, the police,
with their limited resources,
with their limited resources,
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are trying to protect
as much of the city as possible,
as much of the city as possible,
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arrest rioters wherever possible
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and to create a deterrent effect.
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And actually, as it turns out,
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this mechanism between the two species,
so to speak, of rioters and police,
so to speak, of rioters and police,
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is identical to predators
and prey in the wild.
and prey in the wild.
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So if you can imagine rabbits and foxes,
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rabbits are trying to avoid
foxes at all costs,
foxes at all costs,
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while foxes are patrolling the space,
trying to look for rabbits.
trying to look for rabbits.
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We actually know an awful lot
about the dynamics of predators and prey.
about the dynamics of predators and prey.
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We also know a lot about
consumer spending flows.
consumer spending flows.
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And we know a lot about
how viruses spread through a population.
how viruses spread through a population.
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So if you take these three analogies
together and exploit them,
together and exploit them,
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you can come up with a mathematical
model of what actually happened,
model of what actually happened,
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that's capable of replicating
the general patterns
the general patterns
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of the riots themselves.
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Now, once we've got this,
we can almost use this as a petri dish
we can almost use this as a petri dish
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and start having conversations
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about which areas of the city
were more susceptible than others
were more susceptible than others
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and what police tactics could be used
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if this were ever to happen
again in the future.
again in the future.
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Even twenty years ago, modeling
of this sort was completely unheard of.
of this sort was completely unheard of.
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But I think that these analogies
are an incredibly important tool
are an incredibly important tool
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in tackling problems with our society,
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and perhaps, ultimately improving
our society overall.
our society overall.
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So, to conclude: life is complex,
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but perhaps understanding it need not
necessarily be that complicated.
necessarily be that complicated.
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Thank you.
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(Applause)
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ABOUT THE SPEAKER
Hannah Fry - Complexity theoristHannah Fry researches the trends in our civilization and ways we can forecast its future.
Why you should listen
Hannah Fry completed her PhD in fluid dynamics in early 2011 with an emphasis on how liquid droplets move. Then, after working as an aerodynamicist in the motorsport industry, she began work on an interdisciplinary project in complexity sciences at University College London. Hannah’s current research focusses on discovering new connections between mathematically described systems and human interaction at the largest scale.
Fry is the author of the TED Book, The Mathematics of Love: Patterns, Proofs, and the Search for the Ultimate Equation.
Hannah Fry | Speaker | TED.com