(Waldgeräusche mit rhythmischen Beats) 
Thank you very much.  Perfect.  Okay, thank you very much. 
 I am Hannah Fry, a mathematician from London and the title of my talk is 'I predict a riot'.  
I think, I was probably being a tiny bit misleading when I chose the title, 
perhaps a more accurate title is "Can I predict a riot?"  
Actually, while we are thinking about it, 
I'm not the only one involved here, so we could refine that further by saying
 "Can anyone predict a riot?  
And you know what,  in for a penny, in for a pound, 
let's actually explore, can anyone actually predict anything at all to do with human behaviour. 
 I think this is an interesting question.  
Surely all of you are very smart intelligent independent people acting under your own free will.  
I come back to that, and I come back to riots in a little while,   
but first off, I thought I would give you the opportunity to win a  jar of sweets! 
So, this jar of sweets, all you have to do to win is 
I   want you to tell me how many sweets you think are in the jar.  
Any   guesses?  
16?  Very low, I would say.  On the low side. 
 I should say  because you are all independent and free willed people, 
acting under your own free will, I want you to make up your own minds, 
don't let anyone else influence you. Anyone else?  
How many sweets?  You think 83, okay, go ahead.  150, back there, 
60, okay, 160, okay.  
Okay.    
99 we have there.  92.  Okay.  
How many?  115, okay, the actual answer to how many sweets are in the jar is 117.
so you, Sir, you win yourself a jar of sweets.  
Come and get it. Round of applause, if we could.  
(applause) 
Now why did I do this?  Well, so, I also, that jar of sweets, 117, 
this is a picture of a jar of sweets that didn't make it to Berlin, I ate them before I got here. 
 I put this picture up on twitter, 
and I asked people on   twitter to do exactly the same thing as you have done here 
and  suggest how many sweet s in the jar.  Here are the answers they gave   me.  
There are the same number of sweets in the jar, 117 and you can see there is a range of answers, 
as we had here in the room,  everything from 50 plus, 
and I can guarantee no paperclip is in the  jar, all the way up to a stupid 350.  
But as I said, there were 117 sweet s in the jar.  Something really interesting happened,
 when you take the mean and average of all the guesses of people on twitter and it   comes up 117.9, 
which I think you all agree is astonishingly  accurate.  
Now if we had done that in the room and asked you to write  down your answers, 
we would have found a similar phenomenon, you  would have got very close 
to guessing the number of sweets in the jar.    
Now this idea was first discovered by a chap called Francis Gordon and he noticed, 
he was a at a fair grand watching people guess the weight of an ox, 
and he saw the number of people who overestimated balanced out the people 
who underestimate giving an average that is very accurate, the same idea with the jar of sweets.  
I want to include it because it is a simple example of how you can predict some types of human behavior. 
 I have had no chance at all of making  a prediction of anyone individual would make, 
what they would guess are the number of sweet s in the jar.  
If you look at it altogether, suddenly patterns emerge and it is easier to get a grip on.  
Okay, so   in the interest of working altogether, 
I notice, when we gave our lovely winner around of applause, you were were a bit raucous.  
You all clap  a bit different.  It didn't even occur to you to clap in sync  which is  a bit rude, 
so in the interests of acting together and seeing what   human behavior looks like as a group, 
I want to see a little experiment, if you can get it together to clap in sync for me.     
(applause)
Fantastic! I did actually make a prediction there of what  would happen 
and you are the first audience ever who has not done it.  
Thanks a lot for that! Because what audiences normally do and a  couple of you started it, 
what they normally do, but not you, is   
synchronize very quickly in under two seconds which you did manage 
but then they normally increase in tempo, 
so thanks for ruining that part   of the talk for me.  
There is another example of how human behavior on a larger scale has characteristics 
that are not immediately obvious.  
When you think about how is it possible that a room full of  people can fall into synchronicity 
in under two seconds, I think it is  an interesting question. 
 It can't possibly be that you are only  listening to your neighbor
 and changing your clap rate according to what your neighbor is doing, 
because if that was the case the claps would be more like a Mexican wave through at the room.  
Equally it can't be possible that each one of you is listening to 
what every  other single person is doing and generating your clap rate to   everyone in the room.  
There has to be something strange going on   that a  group of people can act in a particular way
 that is not obvious.  
Now, you might think that this is because you are humans 
and  therefore intelligent and also very good looking and so on, 
but actually, this appears in the natural world as well, 
so fireflies  for example, have been known to do this, to synchronize in groups.    
This is an example studied by a mathematician, whose work is amazing, 
a   picture of a wood in North America, slightly less attractive close   up, it has to be said, 
but fireflies have been known in Asia to synchronize their flashes.  
They flash to attract the mate, but this along a river bank, this is a river bank in Malaysia, 
this video, and  you can see that all of the fireflies are doing 
this exact same synchronized behavior. 
Now if you take the FireFlies and put them in   a dark room, 
they will flash much in the same way as people clap.    
They start flashing indiscriminately but quickly fall into synchronization with one another.  
Whatever is going on with humans is also going on with fireflies.  
You can imagine taking a  lovely stroll in the countryside.
Crickets chirping to attract a mate also quickly fall into synchronization with one another, 
and chirp simultaneously.  
Maybe it is something to do, then, with the way that   living things brains work.  
Maybe they are capable of thing this  strange thing going on, 
the difference between the individual and the  group behavior 
but it may surprise you to know that even inanimate object have these properties.  
Here is an example, okay, so if you take a series of metronomes like this, 
and set them off, all at  different ticking rates, what they will do in a second, 
they are are all sitting on a board, he will pick up the board  and place them on two coke  cans.  
What happens very quickly is that just the physics of the set  up as it is, 
allows each metronome to influence every other one and  be influenced by every other one, 
and you can see very quickly that they become in sync.  
This one is being naughty but it happens in a second.    
I think this is astonishing, that even inanimate objects can display   group like properties 
which are different from how you would expect   an individual metronome to act.  
Before you get carried away with   thinking that inanimate objects are much more clever, 
there is a very similar example of this, exactly this behavior, 
that happened in  London and over the millennium.  
Okay.  So, in London there is a   bridge, the millennium bridge, 
built for the millennium, surprisingly,   and within a few days the bridge had to be closed 
because it was   wobbling so dramatically that people couldn't cross it safely.  
Now,   the thing is that there was nothing really wrong with the bridge,
 it had  been tested so and on and people could walk over it without it wobbling,   
but what happened on the bridge is very similar to the  metronomes 
in that the  behavior of humans causes it to wobble.  
If   you could Imagine a group of people walking in the same direction,   
there is some probability that two people will be planting their right   foot at exactly the right moment.  
Now because the shock absorbers  on the bridge weren't quite right, 
what that means is the bridge   moved ever so slightly, 
which would knock more people on to that same   path.  
And more people and more people, so eventually as you can see   here,
 the entire crowd of people are moving backwards and forwards   
perfectly in sync with one another and they are driving the bridge  and the wobble of the bridge.  
I wanted to show you these examples  about synchronization 
because they are really, I think, beautiful  examples  
of how the behavior of the group can differ from the behaviour of an individual. 
They are all quite simple, it’s all about the interaction between the individual and the whole. 
Now the study is what is known as complexity, the study of complexity. 
That is really my area of research. 
Complexity is something that has exploded in the past few years and I show you a little later 
how I applied complexity to look at riots. 
First I want to tell you a little more about what complexity is. 
Even though complexity is going crazy in the last decade, 
academics haven’t agreed on a definition of what it means for something to be complex. 
The one that catches it the best is by a mathematician called Mark Newman . 
He says “A complex system is a system composed of many interacting parts, 
which displays collective behavior that does not allow trivially from the behaviours of the individuals” 
That is the best we have got so far defining what it means to be complex. 
If you want it less wordy, you can use a definition one of my colleagues found. 
It says “Complexity is a bit like pornography: Hard to define but you know it when you see it”. 
A nice alternative. That definition aside, you can get close to understand what complexity means 
by thinking of it in comparison to other bits of science. 
Although, as I said, complexity has exloded in the  last few years,
 these ideas have been around for a while and there is   a paper by a chap called Warren Weaver,
 an American scientist, which he   wrote in 1948, 
and I think he absolutely nailed what complexity is   and what it is about.  
In this paper what he does is a kind of a   review of all science, 
every scientific discovery and application and   technique up until that point in history, 
then he puts them, puts   everything into three lead categories. 
 In the first category, which he   calls problems and simplicity,
 he gives the classic example of a  snooker ball or billiard ball on a table.  
You can imagine, a   physicist or mathematician observing that system, 
it would be very  easy to write down a series of very simple equations 
which can track   the movement of the ball on the table, how it rebounds from the side  and so on. 
 If you, slightly less simply, if you scale those balls up  to the size of planets, 
the problem is still the same, you can write simple equations which describe what is going on.  
So very few  objects interacting with each other in a simple way.  
This is a good  example, because as soon as you get more than two interacting with each other,
 if you have 3, or 10 or 15, suddenly the problem becomes  unmanageable 
and the traditional techniques no longer work.    
Strangely, if instead of 10 or 15 items or object that are   interactive,
you suddenly have millions or billions of them, the   problems strangely become simpler.  
You no longer care about trying to   track an individual object, 
you can start talking about the system as   a whole, a fluid dynamic is a good example.  
What you are doing is   tracking the properties of a system with billions of particles 
and   when they behave altogether, they behave in a way that is easily   quantifiable, 
easy to understand and that you can predictions for   
and this is the second group Warren Weaf was talking about, problems   of disorganized complexity.  
Disorganized because the particles, for   example, are moving around in a random way, 
an erratic way and all   disorganized, but at this end of the spectrum, 
at this point in   history, statistical has been discovered, dynamic s have been   discovered, 
and you can deal with problems of disorganized complexity   
where there are billions of object still interacting in a simple way.    
Warren Weaver made the point that at that point in history 
scientific   methodology had gone from one extreme to the other 
from were very few number of variables to an astronomical number and left untouched a great middle region.  
This middle region is where all the problems of   complexity go 
It is also where any problem that  you can think of  to do with human behaviour
 and understanding and predicting human behaviour is exactly 
where all the problems sit in what Weaveer calls  problems of disorganized complexity.  
That is I think in a shortened   version is the very best way that I can describe to you what   complexity is.  
In terms of looking at systems from the perspective,    
the disconnection between the individual and the whole, there are a   few examples 
where people have made a lot of headway and are able to   understand the system differently,
 and one of those is in traffic shock waves. 
 Imagine you are driving down a motorway, and sometimes  you find yourself in a traffic jam 
which lasts for a short while and   then disappears and there was never any obstruction, 
never any reason for the traffic to slow down.  
So to explore this idea, a  group of Japanese scientists 
got a group of cars to drive round in a  circle, 
just to see, just to try and explore the very idea.  
Now,   what they did is get all the cars, all the drivers,
 they told them to   drive exactly 30 miles an hour.  
But because there are people driving   these cars rather than robots,
some people would naturally drive at   29 miles an hour 
and others at 31 miles an hour, what that means is   
that the faster cars would catch up to the car s in front 
and have to put on their brakes, meaning the car behind will have to put on their brake 
and the car behind and the car behind.  What you end up   up with is the traffic shock waves 
that move backwards through the  field of traffic.  
In this experiment the shock wave you will see in   a second, 
they start off all equally the same, you can see this at   the beginning, 
now the shock wave moves backwards through the traffic at about 20 miles an hour, 
which is very similar speed to the shock  waves that you see on motorways, 
they have been observed, and it is an example, again, 
of how looking at  the bigger picture rather than   the individual car 
tells you something that you otherwise wouldn't be  able to see.  
This is something hat has since been applied in trying   to understand traffic 
and improving, well, improving traffic systems.    
Another really nice example is looking at how people move around, in   pedestrian dynamics.  
Okay if you imagine you have two, 
you have a   corridor with lots of people moving in opposite directions.  
Now, it   would be possible for each individual person to just act completely   as an individual, 
completely of their own independent and free will,   
and just barge straight through and not care about anyone else.  
Butt   it is much easier, as I'm sure you all have experience of,
 of picking   somebody who is traveling in the same direction as you, and then   just following them.  
If you do that, if everybody does that as we   all do, 
what you mind is that people naturally form these lanes in a   pedestrian traffic, 
and these lanes have been really well looked at,   
this experiment in the physics world, shows really clearly how these   lanes form.  
Now understanding how pedestrian moves and the sort of   macro level properties of pedestrians,
 if you look at evacuation   procedures, evacuation design, 
and have architects and engineers to   create buildings that are much more efficient 
by understanding how   people move and behave as pedestrians.  Rules that pedestrians are   using.  
You know this macro level behaviour and characteristics, 
the   thing about complexity is that you want to shrink that down 
and   understand what individual properties lead to that top level   behaviour.  
And it turns out that the way that people behave as   pedestrians
 has a lot of similarities to the way that birds behave   when they flock in the sky.  
So these, this is starling, flocking   birds making incredible moving and evolving patterns.  
You could be   forgiven, I think, when you first see this for thinking, 
for seeing   this macro level, for thinking there is a couple of birds in charge   
of the movement of these flocks and for the incredible shape that   they perform and make in the sky.  
But actually something much more   interesting is going on in the same way as the pedestrians, 
because   all these incredible shapes are created by a very simple set of rules   for each individual.  
Each individual bird that is.  The rules are   first off, basically don't fly into other birds, 
but also, to match  the speed and direction of your neighbour.  
It can be shown, in   computer simulations of this bird movement 
that come up with exactly   the say same type of characteristic patterns that you see in starlings  
 making movements by the really simple rules of an individual. 
 I   wanted to show you in the same way as the pedestrian and the traffic    
how these ideas can be exploited to make things better and the  example I have chosen
 is  Josep Guardiolas footballing style.  
To show you   looking at it from a complex system perspective, 
can explain why they   are so effective.  
Okay.   All those of you not football fans,
 let me   quickly and like a butcher distill all footballing tactics into one   sentence:  
Generally speaking, the traditional way of footballing   tactic s is 4  --  4  -  2 or thereabouts,
 in this kind of structure,   push forward wherever possible and try and score goals.  
That is essentially! 
(applause) 
Thank you!
So the general idea is each player has a really well defined role and position within the team.    
Now the football style or tactic style has some differences 
and it is much more about a macro level of the team, I have to say, 
this is   more relevant to the Barcelona, and I will show you how it links in  in a minute, 
but while at Barcelona who were widely regarded to be   the best team ever, 
he gave the players three simple rules, in the same way as the birds had simple rules as individuals, 
and then the macro level behavior came from that.  
He gave three simple rules to his  players: The first to make triangles across the pitch.  
If you make triangles rather than sticking to the straight lines, 
you give yourself always two passing opportunities, 
but you also ask the players to think ahead, so to think where, think of the future triangles 
and create the shapes all across the pitch.  Here is a video where you can see this happen.  
Barcelona in the darker colour, and you can see  the straight lines the other team are making 
while you can see how   the players moving around constantly create these evolving triangles.    
Barcelona in blue, look at the straight lines of the other team 
and   look how they are constantly moving around.  
They are not worrying about holding position, 
they are using simple rules to create a macro  level behavior of fluid football 
that is not the same as the traditional style.The second rule that Barcelona would use, is 'Pass   
and be patient'.  
If you hold on to possession of the ball by keeping these triangles,
 if you constantly keep making the triangles and don't   let the opposition  to possession, 
you will move them around you and   you will force them to make mistakes.  
The second rule was to pass  and be patient. 
 Allow the other team to make a mistake and then exploit them when they make it.  
There is a nice example, Barcelona   in the dark, terrible quality video.  
Essentially a gap opened up up   here and you are going to see how  they react.  
He tops the gap up   there and runs straightforward into the gap.  
He is in the perfect   position to create a goal scoring opportunity, 
but they need the gap to open up again.  
So what the players do is carry on making these triangles, 
again look at the straight lines of the other team,   
continue passing the ball round and being patient 
until finally the gap opens up and they can exploit the opportunity and score a goal;     
I have to pause it there because he misses! I think it explains the idea very well.  
The third rule that Barcelona use, was the famous one of press for six seconds.  
This is the one people knew about.    
Essentially the idea is if you ever do lose possession of the ball,   
what you should do, is all run at the opposition players, the one with the ball, and press him, 
but the idea behind this is, that you narrow the field of play leaving him with little opportunity 
other than to  just boot the ball, you don't give him any passing opportunity.  
This   video, I love this video, which shows it nicely.  
Barcelona in dark,   they kick the ball lose possession and then all run towards him.    
The guy has no choice but to kick the ball out and they regain  possession.  
These are the rules that were employed, perhaps not from the mathematic point of view, 
but these very simple rules that mean   that the team, Barcelona, played as one moving beast, 
one fluid   object, rather than individuals.  It is these idea s of complexities.    
Now I want to say, because Josep Guardiola has now moved to Munich 
and he unfortunately lost the semi-final last week, which makes this extra  bit look a bit, 
but what I will say is that since he has been   at Munich, he has only been there for a season, 
they have been exploring lots of different styles, 
including some of the ideas he used at Barcelona, 
but others, but one thing they have been doing
 is  using Philip Lahm as an anchor in the triangle system, 
as you can see how the players are structured around him and he now holds the record   
for the most successful passes in a single game of 134, which is   astonishing.  
But the main point of sewing this, all these examples,   the main reason I want to include it, 
was to demonstrate how looking   at things from the macro level 
can give you the chance to exploit   weaknesses and stop patterns that 
otherwise wouldn't be immediately  obvious to you, 
and looking at things in a complexity point of view   gives you this opportunity.  
This is particularly pertinent when it   comes to do looking at something like riots 
and in particular, the   example I have studied of the London riots of 2011.  
So just to tell   you a little a bit about the riots. So, in 2011, 
between 6th and  11th  August in London, essentially the city exploded with rioting. 
 It  started initially after a very peaceful protest, 
after the tragic police  shooting of Mark Duggan turned violent in North London 
and because of   the sensitive nature of the protest, 
police initially tried to contain rioters rather than to move in heavy handedly and arrest   people. 
 In the course of the first evening, a bus got set on fire,   
and rioting escalated with arson coming into play, 
and they finally   moved into a local shopping centre and start looting.  
As things died   down that evening the next day copy cat riots sprang up all over London, 
which no longer had any direct connection to the original   protest, and strangely, 
I think, didn't really have a strong political   motivation.  
People during the riots of 2011 were  not particularly political motivated. 
Okay. That way.  
Where was I?  
But,   living in the city at the time, I think it was, well it was important   to say 
just how much the city was affected by these riots. 
 It was   really unexpected  and to have this widespread rioting across the city.     
On the third day is when events reached a peak with riots 
and you   will see this coming up in a second, with riots completely exploding across the city 
and across the rest of the UK.  Now,   immediately after the event,
 the police engaged to see how widespread   the riots were.  
Each one of these dots relates to an arrest made   in connection with an event.  
Each one is a person committing an   offense that they were then arrested for.  
Now, just to give you a few  facts about how dramatic these events were in London, 
there were over    4,000 are rests, 4,000 people arrested in the riot, 5 fatalities, 
5  people lost they their lives.  One thing  that make these riots   original in some ways, 
or unique in some ways, is that it was a   really strong emphasis on looting, 
much more than you see in previous  life, because there wasn't a strong political motivation.  
People  were really using this as an opportunity, 
especially in the copy cat things that happened in subsequent days 
people really were using it   as an  opportunity to raid shops essentially 
and there is a 250 million   pound estimated cost to the taxpayer.  
When these riots happened, I   was working in London, 
and at the time I was working on looking at   retail behaviour, how people shop, 
people's shopping habits from the   complexity point of view 
and there were two maps that were printed in  the Guardian,
 a very big national newspaper in the UK, these two maps   
were the thing that really sparked our interest and made us, 
or   suggested that perhaps we could look at the riots using complexity   and mathematics.  
This is the first of the maps.  Now, immediately   after the event, when people 
who were arrested appeared in court, the   Guardian newspaper sent reporters and recorded 
where they had   offended, but also where they lived.  
So we have two pieces of   information for every worker, and the riot locations are the white,   
dark dots in the middle and the red dots hard to see on the map, that   is where people lived.  
Now this London map, it locks a bit sort of   all over the place,
it really reflects the polycentric nature of the city, lots of residential areas all over the place. 
 If you compare   that to the map of Manchester, 
you can see a real contrast in this   signature pattern.  
So in Manchester, rather than everything being   all over the place,
 there is a violence centre,   
everything happened in the centre of the city and all of the suspects lived in a ring in the city. 
If I had drawn maps that showed you where people lived, where they shopped, 
you would end up with something really similar to this, these two maps, 
and the contrast between London and Manchester, two different cities, the signatures of those cities, 
this is the thing that really sparked our interest 
and made us think we could look at this, using similar techniques to the retail side of things. 
So, UCL, have some really good connections with the police. 
And as I mentioned, a moment ago, the police during, 
or after these riots they were really keen to understand how things got out of control so quickly. 
And if there was anything that they could have done to have minimized the damage 
or to bring about a quicker resolution to the unrest. 
And so, the questions, in particular, they were interested in asking, were why was it that the riots, 
I mean the riots spred, were widespread across the city, 
there were some areas that were really badly affected and other areas, right next door, 
which had hardly anything at all. And some of these areas, like Brixton in south London, 
has history of rioting, there's been rioting there before. 
There's other areas like Croydon and Eden surprised that it was so badly affected by the riots, 
the police wanted to know what made them so susceptible to rioting. 
They also wanted to know whether they had sufficient resources to deal with this, 
and whether they could have done anything better, whether there was anything they could have done better. 
The way the police managed to get a handle on the riots, in the end, 
by just recalling everyone from holiday, well a lot of people were away. 
Canceling all annual leave and bringing all police all over London to try to suppress things.
You can see here how the numbers changed across the few days. 
From three and half thousand on the first night all the way on Tuesday night 
when they really managed to get a handle on things, sixteen thousand police officers. 
All most more than four times as many police officers were on the streets,
 that's how they got a handle on it. This is really interesting, 
did they really need 16,000 police officers to manage to quash things 
or could they have done it with fewer police?  
So, the police gave us data on everybody who had been arrested in the connection with the riots. 
So this is, I guess you have to be aware of, 
I suppose, this doesn't mean that we have perfect information ton riots at all, 
we only know the people that got caught, or who were arrested,  rather suspects 
who were arrested. But we do still have over four thousand records and in particular, 
the important thing, again, is we have where they committed their offense, 
but also where they live. So we can track how far people moved across the city 
and begin to try to tease out some of the patterns and why they behaved in that way as a group. 
 I should say again, actually, because it's in my mind, 
the main thing here, as I try to emphasize with the earlier stuff, 
we're not trying, here, to make any predictions about the behavior of an individual person. 
And we can't talk, using these techniques, we can't talk about the motivations of an individual. 
This really is about looking at the big wide scale patterns of the city. Now, once we got the data, 
we did this visualization, now smaller circles are where people committed their offense, 
and the larger circles tell you how far they traveled, essentially, 
so can't plot suspect addresses on the map for obvious reason. You can see how far they traveled. 
Your eyes are naturally drawn to the really big circles. 
Your eye is naturally drawn to the bigger circumstances like that one there, 
but actually the vast majority of these events have very, very small circles 
where people traveled not very far at all. 
There are a few things that immediately become quite obvious from the patterns in the data. 
And the first one is the temporal signatures, the ebb and flow of things, 
so how riots built up to a peak in the late evening or early morning 
and then died down over night as police gain more control over the city.
 If you look at just that particular thing, if you look at how these events rose and fell and rose and fell,
 this is the graph you get, you can see quite clearly there how the Monday night 
was just a huge thing across the city. 
And, now, if I drew a similar graph to show you cases of seasonal flu in a country, 
you'd end up with something that looks very similar. 
So, lots of cases of seasonal flu in the winter and then it dies down over summer 
and then lots of cases and it dies down. 
This pattern, this signature of the ebb and flow of it, 
is really reminiscent then of the way that diseases spread. 
So, this, which we've subsequently done more work on, was the first thing that suggested
 that perhaps there was a contagious idea to riot that spread through the city. 
And it spread through the city in the same way that a case of the common cold might, for example. 
So that was the key observation, the first key observation. 
Now, the thing is that the way that ideas spread or the way that viruses spread is a really old 
and well studied problem. Not some complex sciences, it's been around for a long while. 
People really do understand how to look at the process of contagion. 
So it was in the data. 
So the second observation from the data was that it was where people came from. 
Now, in some ways, I suppose it's not a complete surprise to say 
that the people who were involved in the the riots, came from some of the most deprived areas of the city. 
So in the background here, the red, red and blue shows you the index of multiple deprivation. 
Essentially it takes into account the things like income, things like how many school qualifications, 
the number of unemployed people, the quality of the housing,
 all those different things are taken into account in this measure. 
So red is the most. You can see quite clearly a lot of the pins, 
which is where the suspects live, came from the most deprived areas of the city. 
What surprised me was how stark this relationship was. It really was a similar idea, 
deprivation along the side and the number of offenders at the top. 
It really is, basically a straight line. 
And the people who are involved really did come from some of the absolute worst areas of the city, 
places with the worst schools, highest crime rates all of these different kind of things. 
The second important observation we made from the data. 
The third observation was the thing that brings us back to the idea of looking at shoppers, 
it was how far people traveled, rather. So this here is Brixton, 
see just up there, and around the outside is how far people traveled, where people came from. 
And you can kind of see there's a bit of a speckled effect, 
which is where the deprivation feature comes in. But there's also a bit of a radial thing, right. 
People generally didn't travel that far to go to the riots, 
certainly not people coming from the other side of the city. 
And if you distill this data down and look at a bar chart to see how far people traveled, 
you can see that most people, in fact, really didn't travel very far at all to go to the riots, 
they were rioting in their own neighborhoods, and infact 80 percent of people 
traveled less than three kilometers to get to the riots. 
Now the work that we've been doing on retail, looking at people's shopping behavior. 
This is the thing which really showed us there was something to do here. 
The process by which the rioters were behaving is very similar to the way in which shoppers behave. 
So, if you're going shopping you would prefer to shop local to where you live, 
you prefer not to travel very far to shop, but you're prepared to go a bit further 
for a really big retail center, right. 
Now, essentially, rioters were behaving in the same way. 
They were choosing riot locations that were close to where they lived, 
but they were prepared to travel further for a really big riot site. 
And this is very particular to the London riots because looting was so important. 
People were essentially choosing retail centers in the same mind set as they would 
if they were buying stuff, but they were going there and looting instead. 
And immediately after the event, a lot of the UK papers ran with the headline 'shopping with violence'. 
So it's a bit flippant but it does really capture what we found in our data. 
You can see here, this straight line is what you would expect
 if you were looking at the behavior of people going shopping, and the dotted line is 
how the rioters behaved, really similar, the characteristics. 
Okay, so what we wanted to do then is we wanted to try and come up 
with a mathematical representation of the event. 
And we wanted something that couldn't exactly predict when a riot was going to happen
 or when a riot was going to start, but could replicate the general patterns that you see in riots. 
There's no way we could actually predict exactly where the next riot site was going to be 
because we didn't have complete information. There's just no way possible. 
We only have the people who were arrested. So we wanted to create something 
that was capable of replicating these patters. 
And so using the things we found from the data and also from the complexity science, 
we came up with three-stage model. Now, in the first stage, 
as people decide whether or not to participate, 
and active residents decide to get involve in the the riots, 
this is very similar to the idea of infection that we were talking about.
 As soon as people had decided that they're going to riot they then chose 
where they're going to go based on the way people behave in a retail setting, 
which also a very old and well steadied subject. 
When they get to a riot site and they're there with police, they interact with police
 according to a model of civil violence, which again is a very old studied problem. 
Let me explain how it works with the diagram. You have a network of homes and shops,
 right. A rioter or a person or resident, someone that lives in the city
 decides whether they want to be involved in the the riots, when they decide to be involved, 
they chose where they're going to go as though they were going shopping, 
essentially. And when they get there, they interact with the police 
and there's a deterrent effect of the police presence, which is important.
 I'm not going to show you the equation properly, you can read the academic paper, 
if you want to. Just to say, it's got a sound theoretical basis, and it's highly special active as well. 
There's a lot of bits that feed into each other, that's where the real complexity comes in. 
So, I guess, how did the model do, how were we at generating these patterns, we didn't do too badly. 
So if you squash all five days worth of data into a single event, 
all five days of the arrest data into a single event and say it all happened at exactly the same time 
and then compare that to a model that starts everything off all together at the same time, 
you never expect them to be the same. You never expect them to be identical. 
But it does, because you know, there were different character risks across the days, 
another academic paper, you can read, if you like. But, still, we do all right, basically. 
In 26 of the 32 burros, the same or neighboring categories of events, 
but where the model is really powerful, I think, and really gives us something,
 is in the way that it tells you how different areas of the city were susceptible 
or whether or not different areas of the city were susceptible. 
Essentially, the four worst hit areas of the city were Brixton, Crodion, Clapham Junction and Ealing, 
these were the four areas with the biggest damage and problems, 
now, what we do in the model is, we pair off Brixton closest retail center,
 two neighboring retail centers, and we pair them off in the model. 
We start off with a very small riot in both of those areas and wait to see what p whats. 
Now, in some cases, the riot then explodes, draws in more people and explodes 
and in other cases it dies away into nothing. And we record what happens in that experiment, 
in that experimental model. And we repeat that experiment 
by pairing Brixton against Clapton and then against Candel and West-Norwood. 
We take the average of what happened in the Brixton, 
and compare it with what happened in the all the four neighboring retail centers, and in every single case, 
Brixton explodes while the others die away into nothing. 
Just as happened in the the current events themselves. 
We repeat that experiment, and in almost all of the cases, 
well, a slight different thing with western retail corps, 
but in almost all of the cases the ones that exploded are the ones
 that our model picked out as really susceptible area. 
This is important because you can use it to inform the police about 
where it's likely to be somewhere that is likely to be susceptible I should say,
 in theory you could have a surface of the city where you say to police, 
these are the areas that you have to focus on. And even areas like Croidon, 
which was surprising that our model predicted they were susceptible. 
There's also something we can do about looking at,
because rioters interact with police in the model, you can also use this to set up, 
sort of an imaginary riot, and then use it to explore different policing strategies 
and see how important policing strategies are because our rioters react to police. 
And the two things, very simply that we explored, 
are how many police officers you'd need to quash things, 
and also, how important police response time is. 
You have to really be careful because a person in the model doesn't necessarily relate to an executive person. 
One person to one person is not exactly the same thing, so we have to be careful. 
But with a huge bucket load of sorts you can very loosely say 
that perhaps 16,000 police officers weren't necessary to quash things 
and perhaps there was something a bit smaller that could have been used 
rather than bringing everyone in before they got a handle on events . 
But the thing is, we mathematicians, it doesn't make sense to sit in an office 
to say I think the police should act this way or make a police strategy or conjure one up. 
What's more important is that if the police themselves can use these tools to try out different things, 
we can create an imaginary riot that has the same characteristics as a real riot, 
the police can use this to try out different things. 
Again, and at this stage it is a game, I should say, I'm quite silly, I'll be honest. 
But we created a game where it's a touch table in London 
where riots crop up and down across the city. 
And at the moment, a very silly version, you place Lego police cars on the table,
 that table can detect where you placed your police resources 
and the model in the background reacts. And, yeah, it gets you a score and so on. 
But the idea in general is to wrap up all of the maps, 
put it into an interface that is actually useful 
that police can use to explore different strategies and inform them 
using all of the ideas of complexity and 
inform them about what the macro level effects of their individual behaviors are. 
Yes, should I leave it there. Yes, I should leave it there. 
Thank you very much. 
(Applause).
Thank you very much. Are there any questions. 
We have a few minutes left. 
Yes.
Thank you very much.  I have a question, I'm sorry, I'm a bit late,
 I'm not sure I'm asking something that has already been covered. 
My question is, what  do you think or do others think 
about the ethical implications of this kind of research?  
What you talked about was that we look at some sort of scientific technology
 that enables us to look at rioting, but, what if we exchange rioting for demonstration.   
What about the way we already look at rioting?  
We also said that the rioters came from not very rich neighborhoods, but poor neighborhoods, 
and the rioting itself also started with a specific event. 
And, I'm also looking back towards the talk we had by Sasha Labo 
who said that technologies are not just technologies in a very neutral objective sense, 
but they also have proper scope and they have a social scope.  
I wonder, also looking back in history, if we think that this technology continues to advance 
and it gets more complex and better. If we look back, for example, to the French Revolution, 
where would we be today if we had back then technology to prevent the French Revolution?  
(Applause). 
Okay, so, actually, I had one more slide, it's in the interest of time I shortened. 
It short of addresses what you're trying to say. 
Essentially I think there are a couple of brilliant important points to make. 
The first here is that this work in particular is very specific to London.
 And the London riots in particular, and the reason for that is much looting there was. 
And the second thing is that because there was no political motivation for people, 
they were very easily deterred by police. 
The thing is that is really integral to the way this particular thing works 
because putting people off going and looting a pair of trainers
 is much easier than it is to stop somebody who's legitimately fighting for their freedom. 
Sorry I can't hear you.
All right, third point, I think in this particular instance there wasn't a united political aim. 
So I think what's kind of important. 
 I think deterring people from doing something like an opportunistic chance to steal a pair of trainers, 
which is what the evidence showed in the the long didn't, riots is very different from something like Syria.  
I don't think you can use these ideas in something 
where people are so much more motivated to show their opinion.  
I also think that it's also really important to just make the point 
that this is not about making predictions. 
This is not about saying in fifteen minutes time 
there'll be a riot on this street at this time. It's not about that at all. 
This is about just understanding and exploring the integral character risks in the way people behave. 
 I personally think, while data privacy and, you know, handing over too much control to technology
 is a really scary thing. 
And I completely agree with all the points that have been made several times over at re:publica. 
 I think that I still think there is something positive to be gained 
by looking at the macro level behavior of people in the way that we can design our society. 
(Applause). 
We looked at how to suppress the riots. 
And also look look at making the riots worse, such as manipulating the decision process of 
where people are rioting.
 I confess I didn't look at that. I'm starting to think if it would be possible.
Certainly not in this, certainly not with what we've done. 
It would be like saying how do you optimize where you go shopping? 
 I mean essentially that would be the analogy. 
Really it's about what you, as an individual are looking for. 
 I think maybe that's the point then that because we're looking at this from a macro level, 
those kind of individual choices don't really come into it that much. 
Hello. That was a very interesting speech. 
Though, still as far as I understood, in the long run, it came to the result 
that the most riots are the strongest riots came from under privileged areas, 
which also have a tradition of those riots, so a bit provoking  question:
Haven't you not only proved medically what common sense or experience 
what most people or policemen say already? 
Yeah. This was something that I was really interested in. 
The mathematics that we were doing was rally about looking at the macro level patterns.  
I personally was really interested in looking at our story, 
people coming from really deprived neighborhoods, so I also made a short film, 
which you can watch on-line where I went and interviewed a lot of different rioters, 
people involved in the events and tried to understand from their perspective 
why they had got anyone involved. And I think that is a rally important point. 
It's essential to add a complimented story. I'm talking a little long. It was going to be contained -- 
but I think really we should use these ideas of looking at riots as much to try to understand why, 
for example, our young people in London, are in a situation 
where that is what they were doing as much as trying to help the police to stop the city from, 
you know, turning into a riot zone.  I think you have to have that coupled open. NEW SPEAKER: 
If I understood correctly, your data set, your analysis is based on the police arrests? >> 
We used patterns that we found in the data set to inform a data free model. >> 
But aren't police arrests, a sociological research show that police arrests are anything but neutral, 
can that really represent who the rioters were?
people, particularly, especially underprivileged groups of society tend to be arrested more. 
Your model is based on a skewed data set, 
by telling the police that they should go into these areas even more?
Well, okay, I think there are a couple of important things you said there 
you have to be aware of the flaw in the data.  
I think that's really important. And I think that,
 yeah, understanding that you don't know that was involved in the riots. 
 I think I'd add to that that the data set that we have 
is so large that we're picking out macro level patterns
 that completely represent or appear within that data and using that to inform assumptions 
about the way that people behave. 
So, while I agree you have to be careful about understanding the limitations of your data, 
I think you have to be careful of the limitations of this kind of mathematical modeling in total, 
you have to understand what it can offer you and what it cannot offer you. 
 I forgot the second part of your question, I'm so sorry.
Hi.
Hello.
That was a really interesting of -- came up with and I hope one day you are able to predict 
with more precision than you are. 
But, ohing to the fact that the government already has some really crazy surveillance methods
 for you guys living there, don't you think there's just too much power in the hands of the government 
against people that want to express themselves, maybe not in terms 
that would be beneficial for government, but more for the people?
 So, it's a similar point, I completely agree with you.  
I think, I also really, really want to stress here, I think, of with this math and this science, 
it's incredibly important that people, the laymen, politicians, 
everybody really understands what it can offer and what it can't offer. 
And what this can offer is telling you that you need to immediately go to this place 
and do this and so on. This doesn't give you control. 
What it does give you is an understanding for why people are doing thing in the way they can, 
and highlights areas that you can improve, like, you know,
 focusing on -- or defined areas or youth workers,
 this was really big thing that came up in the interviews, focusing on those kind of ideas,
 this is not a control thing.  I think people are generally and understandably
 wary of science and technology controlling their lives.  
I think there have been example in the past where science and mathematical models like this 
have been completely misused and been promised, 
you've been promised the world to do things they just can't do. 
The global economic crisis, is in some part at least, 
of people irresponsibly using mathematical models. 
 I think it's incredibly important that these type of things do exist, 
because I really believe in them.  I think it's important 
that people make the effort to understand what the limitations of mathematical models are, 
I understand the limitations of the data, 
but I think it's really important that people, you know, communicate where the limitations are, 
I guess. Yeah. NEW SPEAKER: 
Hi, first of all, I'd like to thank you, I guess on behalf of the whole audience, 
for a very insightful and interesting talk.
Thank you. 
(applause)
So, I happen to be in London when the riots broke out and being a sociologist 
and also being a little bit riot experienced as an observer.  
I went from -- junction to high point to Brixton, just to verify what was happening.  
I made some observations that I found quite interesting, I guess they are linked now. 
First of all I saw that contrary to what the media showed, like gave us of a picture, 
like of the people and that districts, like two thirds of the people,
the inhabitants of the neighborhoods seemed to be like against 
what was happening down on their streets and their shops and so on. 
So they totally opposed looting and stealing and everything, 
but, interestingly enough they were like lining up on the street observing it 
and they were booing and applauds the police when they finally came in, 
but they didn't stop the looters. 
Like the clear majority, whereas in east London, 
Turkish shop owners they just bounded up, 
and pushed the looters back within no time, like you don't mess with us. 
No Turkish shop was being looted.  
I found interesting because if you have ever watched the first of May riots for example in Istanbul, 
you clearly see that they have a whole different tradition in rioting. 
It's way more, like, they got this riot pattern. 
Everybody knows what to do. It's not organized chaos kind of thing. 
Everybody knows where to go, where the police are and so on. 
So, I found that in democratic societies where you give up like all the responsibility 
for what's happening in your neighborhood and also like the of (Speaking German) 
you give it up to the states, so if something happens call the police that's 
what we learn from childhood, and they will sort it out. In other countries you have to do for yourself. 
 I found maybe there are some problematic indications for us. >>
 I don't know if it's the same here, but in England the papers really make a big deal out 
of any story where somebody tried to intervene and then they got, 
you know, backlash. And there was an example a little while ago 
where a farmer saw some teenagers doing something in the street, 
I can't remember what it was, went out to tell them off and was stabbed. 
And papers make a big deal when someone intervenes and they're injured.  
I think there's a sense of fear across the UK, 
if you get involved and try to stop somebody from doing something, 
it will end upcoming back on you.  I guess, I guess I should study it a bit, 
just as somebody who lives in London and somebody who lives in the UK,
 that would certainly be the reason why I might hold back and wait for the police to get there. 
You're right, it does seem strange when people are so clearly out numbered 
why they're able to take control. 
So, thank you very much, Hannah. 
(applause)
 I think there are a lot of other questions, perhaps you can do this
