Analysis. We start with social network analysis just because well, society is actually a network of people and it's an extremely powerful tool enabled by the digital footprint and by computational techniques. So as I already said, the social, that's actually a network and we have networks all around us. Not only what we nowadays call a social network, like an online social network, there can also be offline social networks. We found, for example, at James Fowler and colleagues at The University of California, San Diego, found that even without digital networks, traditional networks, things like happiness are contagious. So even if out of the second and third degree, out of a friend of your friend of yours is happy, that can have an effect on your level of happiness as well. So these social networks are extremely important. Entire nations are basically, you can think about them as networks. There is an entire cluster of people that basically map out the networks of industry leaders and government leaders and in which premiums and which board of directors they sit and you won't be surprised if I tell you, you don't need to map many of these people because it's the same people sitting in industry, sitting in the government, sitting in the committees. Actually what a nation is and how a nation is run, you can very quickly map out by looking at the social network that constitutes to the society. I won't go deeper into that. We have two lectures on social network analysis. One of my mentors, Manuel Castells, wrote 1,000-page triology on The Rise of the Network Society on the network society. You can, well, the social is basically the network. You can explain social phenomenas in terms of these networks. Now the computational approach and the digital footprint has been very important in order to reveal these networks. As I said, these networks always existed and they have important effects on us. But the digital footprint allowed us to reveal these connections and to make a more formal science out of it, computational tools were also very important. Let me show you an example of how important that is if we do social sciences. Traditionally, if you were to have a social science problem, for example here, we have a bunch of people. We have eight people with different characteristics, and traditionally, what we would do is we would look at who they are. So who are these people. We have eight people, some of them have education. That's why they have the education head on. Some of them have a computer, so they do computational techniques, and two of them have red pants. So there are some characteristics and now we can make a theory out of that. Let's see if we can find some relationship and see who of these guys do computational techniques and who are and who do not. So traditionally, we would look at their characteristics. So with education, without education, with computer, without computer, and then we just count, put them in boxes. Now put people in boxes. So three of them have a computer, have education, one of them has a computer but doesn't have education, one of them has education but no computer and three of them don't have a computer and don't have education. Then we would do our favorite analysis technique, for example, least square, we run a correlation or regression and we can see, yeah, education has something to do with the use of computational tools. Fantastic, policy recommendation therefore would be well. Look at the people who have education. Well, we're not so fast. Maybe the people with the red pants also have to do something with it. So let's check that with the red pants, just to make sure we're not on the wrong track. We have one person, two persons with red pants, one with computer, one without and three people without red pants and computer and three people without red pants and without computers. So there's no relationship here between them, right? They are just as many people with red pants and with computer. So we proved it. Red pants have nothing to do with it. Don't worry about the red pants. What should red pants have to do with it, right? So we do our policy good. As I said, focus on the people with education to push computational methods. Now as an example, it turns out that the network structure is often very important because especially something like innovations. Computational methods here being innovation, they spread through social networks. So if we now would reveal in this hypothetical example, the social network among these people. These people are not all independent. They hang together and it's not like everybody's connected with everybody. We often, we do find something like this here. So we have done one cluster on the one side and one cluster on the other side and in this hypothetical example, the people with the red pants, they do matter. If I want the innovation of computers spread from one side to the other, I actually will have to build a bridge between the people with the red pants. But who are the people with the red pants? Well, they are innovators. They are agents of change. They are so innovative, they're wearing red pants. So actually, the best way to foster the spread of an innovation, if you look at it from this perspective that they are social networks, would not be to do something to help the people with education and focus on that. No, you can do a much cheaper intervention by focusing on this innovators. How do you detect them? But for example in this case, I revealed the social network behind it. Now this is just a hypothetical example but that's often what we find. What the digital footprint helped us is to get this additional piece of information. Who hangs together with whom? Before, it was like ephemeral, tacit. You knew but nobody else knew who you were actually hanging out with and now you leave that behind and we can analyze that. Society is much about who you are, is about with whom you are. It's like your mother told you, ''Be careful with whom you hang out because who you hang out with,'' and it's true. It's not only about who you are but tracking social networks, it's only became actually viable in a massive approach, thanks to the digital footprint and now we can analyze these social networks. Now, social networks have very intricate structures. So just look at this network here. You can see and analyzing these structures and we will do that throughout two lectures. In one lecture, we will look at the structure of the network and the other lecture, we will look at how networks evolve, how they actually change over time and we will understand more about that. Both of that is very relevant for doing social science because as I already said, you can get a lot of mileage out of it. For example, you can make policy recommendations to make the world a better place. You can get them much cheaper. Here is an example that use computational social science that control the spread of a disease in schools. So instead of spreading innovations, I now try to stop the spread of a disease. That also goes through social networks and we try to contain it. So they found out that it's not the red pants, it's children. Children play a very important role in the community in the spread of a disease. So the main policy goal in order to try to prevent the spread of a disease, is for example, well, where are children, they're in schools. So policymakers will just close down schools. That has a very high political and social cost. So if you think close down entire school, parents have to stay at home so they cannot work for the economy, that's a huge hit for the economy, if you close down a school. So closing one school is up to $100,000 but just from the money that the parents lose, the economy doesn't get boosted with that. So let's look for some smarter ways of how we can control the spread of a disease. What if you would know the network structure, we can much better see like well, how can we stop it, right? The computational social science solutions, for example mapped out here the network and that's what a network looks like in a school. There are different clusters in a network. But no surprise, these are the school classes and these as to classes and then different school classes have different grades and between the grades, for example, among all second grader, they also have a lot of contact, but then in different grades there's not so much contact. Basically, what they showed with a lot of computer simulations and we will do computer simulations on social networks and study that in detail. What they found actually, if you close down just one class, a class where there are two infected students. So two sick students, that is just as almost as effective as closing the down the school. Now you get 70 percent of the effect. If you close down the entire grade, let's say they are in one class, two second grader that are ill, you closed down all second grades, that's still much less than closing down the entire school. You get almost as good as it effect. That's what what this graph here shows. So how do we get all this social mileage, our social policy measure? We'll look at the network structure and analyze and simulate these networks, try to understand it and thanks to the digital footprint and to computer simulations, we have this modern insights and can help make the world a better place, basically.
