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Opinion dynamics in society 2

In this lecture we continue our exploration of an opinion dynamics cellular automata model
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Let us now see what happens if we run the models according to the simple rules that we have just discussed.
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Let us now start the model from the initial configuration that you see here. As you can see, gradually and more and more clearly clustering in the distribution of opinions arises. We have blue regions and we have green regions. And sooner or later, the regions in which blue and green are mixed disappear and there are clear boundaries between blue and green. So this is a simple example of social self organisation. The structure has form that is not random. But this structure was also not planned by the agents or by anyone else. If you repeat the experiment, we see that next time, another structure comes about.
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So how does this happen? Recall that initially, opinions were just assigned at random. At some places, there’s a local green majority. Every one turns green into these regions, because here the majority rule makes everyone green in the end. At other places, there is a local blue majority. There everyone will turn blue sooner or later. But this happen simultaneously all over the place in different regions in our cellular world. So sooner or later, groups of blues and greens have formed in different places of the world and the configuration remains stable. To see what makes a group stable, consider this small cluster here of blues. So why does, for example, the blue agent in the upper left corner not switch to green?
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Let’s look into the 3x3 Moore neighbourhood of that agent. If we do this, we see this agent has four green neighbours and it has four blue neighbours. According to our state change rule, this means this neighbour, this agent will not change its colour. It will remain blue. If we inspect the green agent next to it, we see that this green agent has only three blue neighbours. So the green agent will remain green. And the same is true for every other cell in this cellular world in this final configuration. So it’s a stable clustered configuration that has arisen from our model. This example has shown how a very simple cellular automaton can produce social self organisation.
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We see an ordered structure emerge out of a random starting situation. But we cannot easily predict which structure exactly will arise, who will become green and who will become blue, or which colour will gain a majority. Such a model can now be used to explore further the conditions that shape general properties of the resulting distribution. If you go to the Net Logo programme, you can, for example, explore what happens if agents switch their opinion if there is a tie in their neighbourhood, if four neighbours are blue and four are green. Or what would happen if the world is made larger or smaller. We leave this to you to explore.
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In the remainder of this week, you will read articles that gives you insight into more elaborate cellular automaton models of important societal dynamics. The first article will be about opinion formation in society. And you will learn about a more elaborate model, Axelrod’s model of cultural dissemination. The other article will be about Schelling’s model of ethnic segregation. And while you have learned already about this model in the lecture on agent based modelling. But here in this article, we will also go a bit more into how realistic this model actually is.
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And you will learn that what we know about real ethnic preferences that people have, for example, in big cities in the US suggests that indeed the Schelling dynamic of ethnic segregation may be important explanation of why we see ethnic segregation in modern cities. These two articles and the corresponding assignments give you more insight into how more sophisticated cellular automaton models of social self organisations can be built and used. There’s much more to be learned about how we can use cellular automaton models to understand the dynamics of social self organisations. It’s a interesting world out there to be explored. With this, I want to conclude this lecture and thank you for your attention.
This video shows the simulation of opinion formation in a cellular automata model. Try it yourself in the Netlogo web voting model.
What happens and why does this happen? What can we learn from this? How realistic is the model compared to how opinions might form in real life?
Write your thoughts in the discussion section below. Do you agree with your fellow students?
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Decision Making in a Complex and Uncertain World

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