[MUSIC] Welcome to the last lecture of our course about simulation and modeling of material processes. I will speak today about agent based models. There are a large class of related simulation approach, and I will today try to introduce some of them. First using a few example that really motivates the need for this kind of simulation approach. First of all, natural observation about behavior of ants, where every ant has individual behavior. And every ant following a kind of, what seems to be a random goal, random [INAUDIBLE] will exhibit globally considering they are a large collection of ants. Global release structure and interesting behavior. For example, here are some experiment about ant forming piles of dead ants. If you put in a In a box some live ants and some dead ants from the same species. For example here Messor Sanctus. You will see that really soon the ants alive will start collecting the corpse of the dead ants. And try to gather them, cluster them in a pile. And at first it's really sparse points but if you wait you can see here about eight hours you can begin to find a really nicely structured pile. And the more you wait, the more the ants will bring all the ant corpse together in a single pile. And this is really interesting because biologists can ask lots of questions. For example, how does it work? Because an ant has really bad sight. Really almost no memory at all. And they communicate but locally through chemical signals. And the question is, how the different ants can agree in where to put the bodies and how to cluster all the bodies. So, is it a kind of swarming [INAUDIBLE] and how can we explain that process? So that's my first example at the end of this lecture I will give you a more detailed explanation. But let me jump to another two examples that we motivated. The about bacteria, so really, really small living organism. If ants are a few millimeters big bacteria are a micrometer wide, so really small. And nevertheless they exhibit interesting behavior. For example, If bacteria, most bacteria can swim, for example repeated here, and if they swim in a pond with an attraction solution like sugar, bacteria will swim toward the source. The same in a petri dish with a load and CT of gelos, the bacteria will swim until they reach the source. And that's a really interesting phenomena also because bacteria are so small that they cannot possibly feel a gradient. And they have really a course way of moving around. They have two propulsion modes, either they run straight. Or they tumble and they pick a direction at random. And despite the fact that they are not able to sense the gradient, the spatial gradient, and with this really coarse mode of movement. Most bacteria, if you wait a bit, they will find the source of the attractant solution and swim toward it. So again it's really interesting to understand the behavior of the bacteria, the individual behavior of the bacteria, can be adjusted, can be understood such as to explain this kind of phenomena. And another example, is what in a stock exchange for example. Where he has traders, and every trader seems to make an individual decision about bids asked. And most traders, they tend to follow individual strategy, but we could analyze the market evolution as a single entity, as a global entity. And the question here is how the global evolution of market would be influenced by the individual strategy, followed by the traders. Of course, the traders are not completely isolated from each other, because they have lots of ways to communicate, to exchange. And so it can be interesting to represent a stock exchange for example, by analyzing the individuality decision of each trader. How he reasons, how he makes his decision. And network of interaction between traders using stock exchange or other communication. I will not detail this example further but here is a nice introduction, very easy to read about what can we do. And so the common point about all these example that I showed you is that we want to model. We want to model basic entity, individual, small parts by modeling in them all the thing that we can see that we can understand. By studying the phenomena. But then observe the global emergent behavior. Other example can be provided like it or not pedestrian simulation for example to understand how buildings are evacuated when there's an alert. Or for example, an epidemy propagating that will propagate depending on the habits of people who was the network of interaction between peoples and so on. The ecological modeling is really interesting, for example, to detail how an invasive species will colonize a new ecosystem. And so there's a large class of model that could be solved using other method that we discussed the other week for example the parameter. But we present you here another way of modeling them. So that's the end of this first module and in the next one I'll explain exactly what are agents. [MUSIC]
