Skip to 0 minutes and 10 seconds Hello. In this lecture, we will focus on how we can model social self–organisation. We will later on look into how we can do this with cellular automata. So what does social self organisation mean? How do we define this concept? In the lectures on complexity in week one and on emergent behaviour in week two, you have learned that the spontaneous emergence of order is an important characteristic of complex social systems. You also have learned that this order is unplanned. It arises bottom up, from the interactions of many individuals who influence each other but who are not controlled by a central authority. This is also what we mean with the term social self organisation. Think of a bird swarm.
Skip to 1 minute and 4 seconds There is no leader bird, but the swarm creates its own structure when moving around without any bird intending to create this particular structure. In a similar way, there are many structures in human society that look as if they might be organised by a central planner. As if people follow such a plan to bring these structures about. But very often, people do not have such a plan and not such intentions.
Skip to 1 minute and 38 seconds Let us now look into a few examples of social self organisation in human society. One important example is how certain types of behaviour or opinions are clustered in geographical space.
Skip to 1 minute and 55 seconds Look here, at the map of how dialects are distributed across a geographical region. As you see, these dialects are not just scattered randomly all over the place. It’s not that any person at random picks his or her own dialect to speak. Instead, we see a pattern of clustering. There are regions in this area, and we see that within such regions people speak more or less the same dialect. But if you move across the border to another region, suddenly people speak another dialect. So what we see is homogeneity within the local region, but diversity at the overall level. We also call this clustering. A similar pattern of clustering arises when we look at the distribution of political opinions in geographical space.
Skip to 2 minutes and 54 seconds Here, you see an example of the distribution of the percentage of votes that the Social Democratic Party got in their most recent elections in Germany. This is broken down by voting districts. And as you see, in this pattern there are regions in which we have more or less the same colour across a larger space. So again, we see clustering. It’s not just random. In the north of Germany, for example, in most voting districts people vote relatively strongly social democratic, whereas in the south and southeast of Germany, we would see that there are relatively few votes for the Social Democratic Party. Again, it’s not a random pattern. It’s a pattern of clustering. Let’s now look at the third very important example.
Skip to 3 minutes and 50 seconds Consider the distribution of different ethnic groups across the neighbourhoods of a city. Here, we see a map of the city of Amsterdam. The colours in the map show you the percentage of non-Western immigrants living in a particular neighbourhood in Amsterdam. And, as you see, there are neighbourhoods with a relatively strong red colour that means a high percentage of non-Western immigrants, far above the overall average that you have in Amsterdam. And there are many neighbourhoods with a dark blue colour, indicating a high percentage of native Dutch people living in this neighbourhood. What you see much less frequently are neighbourhoods in between, mixed neighbourhoods, which have a composition that is more or less representative for the overall mix of the Amsterdam population.
Skip to 4 minutes and 44 seconds So again, we see a pattern off clustering of ethnic groups in certain neighbourhoods, and this pattern is called ethnic segregation. All three examples– the distribution of dialects, of political opinions, and ethnic residential segregation– show a pattern of order. That is, a non randomness in the distribution of behaviours or characteristics across space. But in none of these cases, the exact form of order that we see has been planned, or could be played by a central authority. The order is instead self organised. It results in an unplanned way from the interplay of the interactions of these many individuals. This makes it very difficult, if not impossible, to predict the outcome of social self organisation.
Skip to 5 minutes and 40 seconds For example, it is very difficult to predict, or even control, how the distribution of dialects, or political opinions, or ethnic patterns, in a particular society will develop in the future. But we can try to understand the conditions that shape the general characteristics of this order. In the lecture on agent based modelling that you have seen earlier, you have seen how computer models can be built that help us to understand effects of parameters of the system on the outcome. Here are some of the questions that we can try to answer with such models. How does the size of a group affect the number of different opinion clusters that can emerge in that group?
Skip to 6 minutes and 29 seconds Or, how does the number of neighbours that people have on average in a city affect the chances to find consensus on opinions? How do the preferences that people have for similar neighbours affect how much segregation arises in the city? And how does this, for example, depend on the size of the city, the number of different groups, or the size of neighbourhoods in the city? In the next lecture, you will learn about a particular type of computer models that we can use to address questions like these. These models are called cellular automata models.
What is social self-organisation?
This video introduces the first topic of this week: self-organisation. How do complex systems organise themselves? After explaining what this phenomenon is and what its key principles are the following steps will explain how you can model this. This is called cellular automata modelling.
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