Recently, I discussed the M.A.R.S simulation models developed by Iannaccone and Makowsky. Based on what I read, I decided to try to work out a similar simulation myself. I did so using R-Project and it resulted in the simulation shown below. For more details on the syntax I used, visit the `my functions’ part of my site, which has a page on the syntax for this specific simulation. Please read further for some interpretation of this animation.

In this lattice, we see white, red, and blue squares. White squares represent empty positions, the red and blue squares represent people with different characteristics. Their positions on the lattice are determined randomly. A typical Schelling simulation is performed in rounds. In this case: every round a random person is selected to move to another location, which is randomly selected as well. When this is performed randomly indeed, no patterns will emerge. However, I gave the persons in this simulation slight preferences: people living adjacent to people of the other color have a larger chance to move. When selected to move, people have a slight preference to move to a position which is not adjacent to many squares of another color than the own. When we run this simulation for 100 rounds, we see the emergence of enormous segregation.

When we focus on the histogram on the right, which shows the number of blue squares surrounding each red square, we see that especially the number of blue squares not surrounded by any of the red squares increases rapidly at one point, but does so in a non-linear manner.

Unique characteristics

Schelling-like simulations have at least two unique characteristics: They have become famous by showing that minor differences can lead to enormous aggregate outcomes. Also, even in this very basic simulation, it is possible that the aggregate outcomes vary over time and do so in a non-linear manner, even when the individual level processes are linear. In other words: from constant individual preferences, varying rates of segregation can emerge.

Additionally, the use of these types of simulations is attractive because several authors claim that they can bridge the methodological divide from micro- to macro-, thereby being applicable to this research proposal, since religious regionalism is an aggregate phenomenon. Finally, although in this research project I will address several shortcomings of a specific form of simulation models, I argue that the models can become (at least) as realistic as regression analyses. This can be a point for discussion.