Translator: Miguel Ángel Caballero Lijarcio
Reviewer: Júlia Vilafranca Molero Thanks to the great scientists
of the 17th and 18th centuries, we know that the universe and many other systems we know;
move and operate according to deterministic laws
we understand very well. For example, we know the
mechanics of a building or the Solar System’s movements. However, we also know that there are
other kind of systems. Particularly, we already had some clues
towards the end of the 19th century and throughout the 20th century,
when we started working on theories of non-linear systems,
chaotic systems, fractal geometries… And in late 20th
and early 21st centuries, we started using
something very useful: heavy machinery,
which helped us count with more precision and more thoroughness, so to speak. And it’s at that moment
when we discover these new and complex systems. Such systems exist in multiple sizes and interaction-time scales. These systems involve a wide variety
of nodes, non-trivial interactions, and links within these units
that act in a few seconds, minutes, or even milliseconds, etc. Not only do we find these systems
in nature, but also in human artifices. Any system having
a sufficient number of units and having those non-linear
and non-trivial interactions we were mentioning,
is considered a complex system whose properties emerge,
let’s say, unexpectedly. Non-deterministic. And now, we’ve come to the system
I wanted to talk about today: the social system, which is
the complex system par excellence, perhaps along with some others, right? The social system
has been studied for centuries. I mean it’s not a recent discovery. It’s a system we’re interested in.
It has multiple scales: family units, where people interact; the classroom’s dynamics in a school,
which can involve dozens of people; a big company, which can involve
hundreds or thousands of people… And nowadays, thanks to
the Communication Revolution, we’re hundreds of millions of people
when we use Twitter which counts with 300
or 400 million users. And we’re thousands
of millions in Facebook, wth around 1,200 million users. So imagine the many kinds of dynamics that can be created within these scales
and at different times. Related to what I’ve just said
about Twitter, Facebook, etc., it’s not only
the matter fascinating in itself. What is more, there are even
added advantages and interests. On the one hand, these new networks
or new tools give us different opportunities. They have changed
our way of communicating. Inmediacy and multiple ways: we receive
information on Facebook, we watch a video on YouTube
and then we share it on Twitter… As I was saying,
this is called inmediacy. And a second component,
which I am working on… I am an expert in that,
or at least I try to. This component I’m working on
is the data availablity. Not only are these social networks
interesting because they’re big, but also because we’re provided with
everything we want: we know who said what,
to whom and when. We have this object.
We can put it into computers. We can put it, let’s say,
on the table to try to dissect it and understand it even over time. And now that you have it on the table, where before we had speculation and we had to imagine
how things might work, now we have data instead. And now we can determine
whether this system resembles any other system we know and whether it works
according to some rules we can come to understand. And this is the exercise
we’ll carry out here today. Particularly,
among these other systems I’m also interested about there’s the mutualist ecosystems,
which I want to stress today. In these ecosystems,
as we can see in this image, the idea is very simple:
we have species, insects that visit flowers, and obviously
it’s called ‘mutualist’ because there’s some kind
of mutual benefit. The insect visits indeed the flower
to extract food and the flower also takes benefit
as it’s spread all over the area. The insect spreads the flower’s seed
across a specific area. Then, we, of course, must come to a halt
if we want to make comparisons, if we want to establish whether there is
some kind of parallelism. Let’s try to understand
these mutualist ecosystems. If I don’t have more information,
if nobody teaches me anything, if I can’t access any data,
what will be my most plausible hypothesis? How do these insects visit flowers?
Which models do they follow? How does an insect know
which flower it has to visit? I guess that if I haven’t got
more information, my answer will be at random, a random model.
For example, a butterfly flies, finds a flower and then
visits another flower. We’ve talked before about complex systems
and what they are. What matters is not only its nodes.
What really matters is the interaction, and here we have many clear interactions,
right? There are negative interactions
when there is competence within species. What two different insects want is to access
as many resources as possible. And that’s a
negative interaction. It’s the same with flowers. Flowers want to attract
as many insects as possible to be scattered, and so, flowers also compete
with each other. Finally, it’s in the mix
where a positive interaction occurs. It’s like that, isn’t it? As I said,
it’s all about mutualism: mutual benefits. The insect visits the flower,
takes food from it, and thus energy, and finally the flower is scattered.
And that’s the key. These negative and positive interactions
are responsible for us not observing random patterns.
It’s the other way round. We could say that these forces cause the system
to have some kind of pattern. And what is this pattern?
This is the one we observe in the real world, isn’t it?
When we go to the countryside and see these insects
and flowers, what we are really seeing
is the nesting pattern. It emerges naturally without
any direction. This kind of interaction exists
without any previous design. And what can be highlighted about
this form of interaction? Well, we can highlight many things, but
there are two of them I want to emphasise. One of them is the existence
of generalists. ‘Generalist’ in the sense of convenience
for the insect, which visits any flower.
It doesn’t care, right? The generalist flower is that
which dresses up, so to speak, to be visited by any insect. Here you can see in this graph
how the insect stands over the top, as you see,
interacts with all of the flowers, included the left-hand-side flower. And we have other protagonists:
‘specialists’. Specialists prefer to link themselves
to generalist elements, they only want to visit
generalist elements. That would be the last insect we see,
as well as the right-hand-side flower. That’s a peculiar pattern.
That’s nicer than the random one. The random pattern had no logic,
whereas this one is a kind of staggering. But that is not a stylistic problem.
Why has the system evolved towards this pattern?
Well, what reports and also investigations say is that
this particular structure is ideal for all the system someway,
as it minimises competence and maximises survival. In other words, there still exists
competence among insects, but this way of sharing out
visits results in less competence. They don’t bother each other that much.
The same applies to flowers. When we talk about maximizing survival,
we aren’t meaning the insect surviving, but the system surviving. That’s a good thing
for the system as a whole. Okay, interesting and fascinating,
isn’t it? But I’ve come here,
to talk about social systems, particularly,
about online social systems and about how we communicate
one another online. The question is the same: There’s a social system. We no longer have
butterflies, but users. We no longer have flowers,
but hashtags and key words. And the question is: can we observe the same thing? What is the communicative dynamics?
How does the way users interact with hashtags determine
the way we communicate? Obviously, to answer this question
we need to know more, or at least
we need to have a hypothesis, don’t we? So now we’re going to focus on Richard Dawkins’ famous book,
‘The Selfish Gene’. Basically,
this book deals with genetics, and at the end of the book, there’s a bold and attractive hypothesis
which has been called ‘memetic theory’. It’s all about memes.
A meme is an idea, a concept. And what Dawkins tells us is that,
just like genes, memes have an associated fitness, And by ‘fitness’, I mean that the higher your fitness is, the better you
are adapted. If memes, or ideas, had a high fitness,
that would make it easier to spread those ideas.
From brain to brain. That is what we can see
when it comes to viral phenomena. You can see here in this graph
those widespread ideas. Of course, we also have
unattractive memes. They lack specific characteristics
and their fitness is low, so they die sooner. These less attractive memes
do communicate with each other, but they can’t go from brain to brain. That means something very important:
not every symbol, not every meme is identical. Fitness classifies them by
usfulness, beauty, etc. And this is our question: Do online communication systems look like mutualist ecosystems? Now that we have this hint,
we can work on the hypothesis that every single meme,
every single key word has an associated fitness. There are the same ways of
interaction we’d seen before in the ecosystems. In other words, users compete with
each other on social networks because we want to be more visible,
so to speak. We want more people
to follow us and to read us. But symbols and hashtags also compete
with each other because they want to be as widespread as possible.
It’s just like the flowers’ case. It seems that we already have the perfect
ingredients, right? When I change the slide, we should be able to see
the same patterns both in natural, mutualist ecosystems
and online systems. But results don’t show that. But don’t boo at me yet.
Okay, this hasn’t worked. These things happen when you start
studying a social phenomenon that has been going on for a long time.
That’s to say, we can study a social network from its inception,
from the moment a matter was born, right? We can study a matter from the beginning, when it’s not very popular yet,
to the point when it hatches and becomes a massive phenomenon, right? These are the results we got.
If we take the early stages of a discussion, we can see that there is
few people interested in that discussion. As an example, remember 15M Movement.
(an anti-austerity movement in Spain). There was a big discussion about it
in Twitter, but there have been
many more. Now we find the opposite. We don’t live in a nested structure, but in a modular structure. This kind of structure is
practically the opposite. They’re boxes relatively isolated which don’t communicate
with each other too much. We’re talking about
subgroups of Twitter users who discuss their issues there. They barely share key words with
other groups that discuss about different things. And that’s what we
didn’t expect to see, did we? Therefore, here we have these
communicative barriers. There must be somthing we haven’t
towards the end of that online discussion, we do find what we expected: the nested pattern
we’d been talking about, that pattern we wanted to see, right? At this stage, how do we interpret
this transition from a system that didn’t resemble the mutualist ecosystem
to another system that does resemble it? Well, it’s the same discussion growth that somehow forces users to try it. We have severe cognitive constraints. We can’t permanently follow a discussion
or use 25, 40, 50, 100 key words to focus the discussion, right? What we can see here
is that most people tended to go towards a
more global structure allowing a more efficient communication but also respecting its cognitive
constraints at the same time. If you use athe hashtag with more fitness,
you make sure you see and are seen, you can follow the discussion and they
can also follow you in that discussion. Therefore, this evolution leaves us with a way of interpreting the evolution
of the discussion and achieving consensus on
online networks. We’ll always see a first stage:
segregation. We have different groups of people
talking about their own issues in a disorganized way. Then we move on to an intermediate situation
with partial consensus. These specialised discussions
start internally organising by themselves and evolve towards
that kind of efficient communication that minimizes cognitive costs. Finally, we move on
to a general-consensus stage, where we all take part in
the same discussion. There are no longer barriers,
units or communities. Thus we reach a general consensus. . And that’s it!
I’d like to thank you for your attention. (Aplause)