A little bit of a preference goes a long way
As the saying goes: “birds of a feather flock together”. People with similar backgrounds tend to end up in the same social group. This effect can be so extreme that it eventually leads to the kind of segregation in which the only people of the same background interact with each other. But how much of such a “preference for similarity” is needed to get a segregated society? In 1971, Thomas Schelling tried to answer this question using multi-agent simulations (Schelling, 1971). The script on this page (open script in separate tab) shows an implementation of Schelling’s model of segregation. The controls for this script are outlined at the bottom of the page.
The script on this page simulates how segregation occurs on the bleachers of AgentVille. The fictional town of AgentVille is known for its annual sports event, which draws many supporters of both Team Orange and Team Blue. When a supporter arrives at the stadium, he randomly decides where to sit and watch the event. But that does not mean that AgentVille supporters have no preferences on where they want to sit. Each supporter wants to sit close to someone that supports the same team. In fact, each supporter agent wants that among neighboring seats, there is some minimum percentage of people that supports the same team as he does. The way this works is demonstrated in Figure 1. As you can see by moving the agents around these nine spaces, each supporter agent has up to eight neighboring seats. An agent only shows a wide grin of happiness when at least 50% of the people in these neighboring seats supports his team.
|Figure 1: Agents want to have a minimum percentage (50% in this case) of people in neighboring seats to support the same team as they do. In this interactive figure, you can drag agents to a new position to see how this works. Only happy supporters show a wide grin.|
The pursuit of happiness
If a supporter is unhappy with his seat, he will eventually get up and move to one of the empty seats. It is not hard to see that if every agent demands that 100% of the people in neighboring seats supports the same team as he does, this process of switching seats will only stop when the supporters for both teams are completely separated. But does a lower similarity preference also cause segregation on the bleachers? Is the number of empty seats important? If each supporter wants 50% of his neighbours to support their team, will the average similarity be 50% when all supporters are happy, or would it be much higher?
The script on this page allows you to play around with different situations on the bleachers and see what will happen to the way supporters seat themselves. The graph keeps track of the number of happy supporters (green line) and the average percentage of people in neighboring seats that support the same team (red line). One thing you may want to keep an eye on, is the difference between the preferences of individual supporters (as shown by the desired similarity slider) and what the average similarity of neighboring supporters ends up being.
- Support agents on the bleachers: You can manually drag agents to empty seats. An agent that is happy with his current situation will show a broad grin. Agents without such a grin will eventually want to move to another seat.
- Happiness / similarity graph: After every change in the situation on the bleachers, the graph shows the current percentage of agents that is happy (green) and the average percentage of agents in neighboring seats that support the same team (red).
- Number of agents slider: This determines the number of agents trying to find a spot on the bleachers. There are 200 seats available, and the number of agents can vary between 1 and If the number of agents is even, there will be as many Blue Team supporters as Orange Team supporters. If the number of agents is odd, there will be one more supporter for Orange Team.
- Desired similarity slider: This slider determines for each agent what percentage of neighoring agents that support the same team will make him happy. All agents have exactly the same desired similarity.
- Simulation speed slider: This shows how quickly changes occur on the bleachers, varying from one change per second to up to 2500 changes per second.
- Start/stop simulation: The simulation will automatically stop when 100% of agents is happy. Starting the simulation does not reset the board. This means you can pause the simulation to make some manual changes if you want, and continue from the situation you have created.
- Reset field: Removes all agents from the board and lets each of them choose a new seat.