"Studying censorship via social simulation" Antonio A. Casilli (Télécom ParisTech / EHESS)
Censorship is extremely difficult to study from a social science perspective.
It is an interruption of information flow.
So data on its impact and effectiveness are often inaccessible to researchers.
That is why we must rely on an innovative method: social simulation.
This method consists in building a computer simulation (in this case, a multi-agent system),
a piece of software modelling empirically observed phenomena.
Simulations push the logic of social facts to the extreme and help us reflect on their
determinants.
To illustrate this procedure, let's make a small step back in time.
We are the beginning of August 2011 – I’m enjoying a little holiday on a beach in Portugal.
But, when I come back to my hotel, this is the first image I see on TV: London is burning.
The British riots have begun.
I buy a ticket and fly to London.
There, the situation is extremely complicated: police brutality, rioters’ violence, citizen
grievance and – most of all – UK Prime Minister David Cameron announcing in the media
his intention to censor social media, which he describes as "vehicles of hate speech and
chaos.
" That came as a bit of a surprise.
A few months earlier, Cameron had been the first Western leader to visit Egypt after
the fall of Mubarak.
On that occasion, he had saluted the role played by Internet activists in the Arab Spring.
Oh sublime hypocrisy of European politicians!
The same technologies that, a few months before, were glorified for single-handedly bringing
down dictators in Africa, were now at the core of an unprecedented moral panic for their
alleged role in fuelling anarchy in the UK.
Anarchy, or rather sheer criminal behaviour: the British conservatives seemed to agree
that the riots had no political significance.
London mayor, Boris Johnson, declared in a public speech: “It's time we heard a little
bit less about the economic and sociological justifications for what is in my view wanton
criminality”.
No sociological explanations.
He just wanted repression.
The schematic explanation of social action adopted by the British government was very
clear – and very simplistic.
In UK politicians’ opinion, the main characters of these events fell into two categories.
On the one hand, the rioters: thieves, hooded criminals whose only rationale is a thirst
for plunder.
On the other side, we have police officers, in charge with social regulation.
But when sociologists started observing places affected by the riots, collecting documentation
(eg.
press and news sources), exploring social media, and of course speaking with people
affected by these events – a very different picture appeared.
A set of actors and rules of behaviour very different emerged.
The starting point of these social facts, of these riots, is a common *citizen*, living
a day-to-day life in a situation of quiet.
But when the level of resentment and discontent exceeds a certain threshold, the citizen can
become *active* and switch to riot mode.
Rioting goes on until *police officers* intervene to contain it, by deactivating all *arrested*
citizens.
Let’s see what’s the output in terms of our computer simulation.
Here we have a simple interface in which a number of variables (ratio of citizens and
police, government legitimacy, etc.) are configured.
In its initial state, this simulation displays a grid populated exclusively by quiet citizens
and police officers.
Once we run it, the simulation shows a situation of civil violence.
Rioters activate, they tend to group, to resist police attempts to arrest them and turn them
into inactive.
If we look at the curves on the right of the screen, we can follow the increase in the
number of rioters, in the number of arrested citizens and in the number of citizens who
do not take part in riots.
We can also choose to monitor the behaviour of a single citizen.
Let’s take this one, for example.
Let’s say his name is Ted.
Ted is surrounded by a circle.
It represents his "vision", i.e. his ability to acknowledge events taking place in his
environment.
He goes through several stages: he is initially in a state of quiet, then starts rioting,
and sometimes he is arrested by the police.
And what about social media?
Facebook, Twitter and BlackBerry’s BBM were singled out during the riots.
Yet, we cannot trust the simplistic assumption that they triggered violence.
At most, they ‘equipped’ the protests by providing them with a techno-informational
infrastructure.
How?
Via what we described as the "vision" of each social actor – that is, the circle surrounding
our random citizen, Ted, in the simulation.
Under normal circumstances, the circle has a diameter of 1, meaning that Ted can only
see his immediate surroundings.
But if he uses social media, the diameter of vision can become more sizeable.
Ted can be aware of what happens 1, 5 or 50 miles from his location.
He may decide to converge to another neighbourhood, for example, to attack a target or to help
others resisting police.
And what about censorship?
When censorship is enforced, Ted’s field of vision narrows down.
Censorship interrupts his flow of communication.
It decreases the ability of individuals to appreciate the characteristics of their environment.
In this sense, censorship blinds social actors to their own social context.
What are the consequences at the aggregate level?
How does censorship affect civil violence over time?
The results of our simulations are clear.
1) If government enforces total censorship, violence goes straight up, reaches a plateau
and stays up.
We may call that "the Mubarak scenario", because it is reminiscent of what happened in Egypt
when former dictator decided to use the kill-switch option to cut off Internet access for everyone.
2) In the second case, partial censorship, we expect an initial upsurge in violence,
which then becomes stable at a very high level and in a context of protracted political instability.
3) Finally, in the absence of censorship, the pattern of civil violence is very different:
it rises dramatically each time we have a riot, but then it goes back to zero.
Between one peak and the other, there are very long periods of social peace.
Moreover, the aggregate level of civil violence, represented by the white trend line, is significantly
lower than in the other two cases.
Therefore, the absence of censorship is preferable.
In this case, the social system converges to a period of peace after every riot peak.
This situation is called a "punctuated equilibrium".
Social harmony is not constant, but it’s cyclical.
Between one cycle and the other, the number of citizens in
a state of quiet (the white curve) rises.
And these citizens have the opportunity to freely express their disagreement towards
the government.
This can sometimes cause violent protests or conflict, but it is
a desirable situation of relative social harmony.
This study reminds us that a good rule to follow in times of social change is to avoid
trading democratic values and freedom of expression for an illusory sense of security.
We are witnessing increasing efforts to censor and
filter the Web.
Our efforts to fight censorship must increase, too.
A research approach based
social simulation can, I believe, show us
a way out of the political logic of censorship.