Homework: Spring is in the air

By Alexander van der Vooren（Utrecht University） and Yuntao Long （IQTE）

1. Introduction

We constructed a simple agent-based model of people in different parts of a country, who begin to want a change in government. We divided people into five types of political states, pro-active, pro-inactive, neutral, against-inactive and against-active, and they interact with each other, and take different reactions to different rules. Different initial distributions reveal different results. We consider the three initial distributions, such as random, perfect separation, clustering, and we find that there is a really big disparity in the time it takes before the against-active people dominate the country. Given the initial distributions and the initial percentage of various agents, the model produces several results.

2. Research question

Problem statement:

People in different parts of a country, begin to want a change in government. The people who hold different political states have different attitude to their government, ant they interact with each other.

Question:

How does the initial location of the population of a country affect the speed of changing the government?

3. Model

In our model, we divide the population of a country into five types of people. People differs in political states, this is whether they are Pro or Against the current government and whether they are Active or Inactive supporters. Some people are Neutral. The specific classification of agents is as follows:

1)   Pro-active

2)   Pro-inactive

3)   Neutral

4)   Against-inactive

5)   against-active

People move around their country, represented by a grid in Netlogo. If people bump into each other while moving, they might change their political state. So, some agents cause other to change their political state. This is explained by the behavioral rules.

Behavioral rules

We adequately consider the interactions between the different types of agent in the model, people can be influenced by other people they meet with different political positions:

Pro-active

- If Pro-active meets Against-active, and if Against-active dominates local neighborhood, then become Pro-inactive.

Pro-inactive:

- If Pro-inactive meets Pro-active, then become also Pro-active,

- If Pro-inactive meets Against-active, then they become Neutral.

Neutral:

- If Neutral meets active agents, then they become inactive agents from the same type.

Against-inactive:

- Synchronic to Pro-inactive

Against-active:

- Synchronic to Pro-active

4. Results

We first test which kind of people will dominate the country under different initial presence of the different political states. People are randomly positioned on the grid. The Netlogo applet below enables you to replicate the different scenarios. 

Scenario 1. There are initially no active agents present:

Nothing happens, all Inactive agents stay inactive and all Neutral agents remain Neutral.

Scenario 2. Synchronal presence of political states and most people are inactive supporters of either Pro or Against. For example, Pro-active is 5% of population, 2) Pro-inactive is 40% of population, 3) Neutral is 10%, 4) Against-inactive is 40%, and 5) against-active is 5%.

Figure 1 illustrates a simulation run of scenario 1. Inactive agents become Active, and Neutrals disappear as well. Two strong fronts emerge of which one will eventually dominate the country. Since the set-up is synchronal, there is an equal probability for Pro and Against to dominate the market. We see that different clusters of Pro and Against agents emerge. As one of the types, Pro or Against, becomes a minority, then the clusters start to disappear and one will dominate the country.

Figure 1.

Scenario 3. Against-agents are initially slightly more present than Pro-active agents. For example, Pro-active is 4% of population, 2) Pro-inactive is 40% of population, 3) Neutral is 10%, 4) Against-inactive is 40%, and 5) against-active is 6%.

Figure 2 illustrates a simulation run of scenario 3. Eventually, either Pro-active or Against-active agents will eventually dominate the market. Since, Against-agents are initially slightly more present than Pro-active agents, there is a higher probability that Against-active agents will dominate the market.

Figure 2.

Simple test

Our research question is how the initial positioning of the different agents on the grid affects the speed of one type becoming dominant. Therefore we tested scenario 3, above, in three new scenarios. The first is similar to the above, the agents are randomly placed on the grid (left hand in Figure 3). In the second scenario the Pro and Against agents are perfectly separated from each other, and Neutral agents are randomly placed on the grid (center in Figure 3). In the third scenario there are four clusters. Two of which consist of Pro agents and two consist of Against agents.

Figure 3. Left is random, center is perfect separation, right is clustering

We ran for each scenario 100 simulations runs and calculated for each scenario the average number of time steps needed before one of the types becomes dominant (2/3 of the population). The simulation runs show that for random positions it took on average 2837 time-steps, for perfect separation 5229 time-steps, and for the clustering 2797 time-steps.

These results show that the number of interactions with agents of other types is an important factor in our model. When there is perfect separation, the change of meeting other people is initially much lower than for random and clustering. The almost similar results between random and clustering illustrate that also the clustering is an important factor. If Against-active people are clustered it might be easier to dominate the neighborhood such that Pro-Active people become inactive.

5. Further research

Given the limited time in graduate workshop available for homework, there are many insights and ideas we are thinking about. A striking question is what will the government do if the against-active population attempts to dominate the country and how the government can keep the country stable?

The model can be extended by testing different government policy, such as jailing Against-active people for a couple of years, which is actually the same as making them inactive and make it impossible for them to interact with other people. Another possibility, for a government that wants to control their system, is to prevent clustering of Against-active people. When the against-active people cluster a lot, the government could take some steps to scatter the same type of people. Finally, this model may also be extended to the election issues, international dispute and the spread of disease (going from on state to another, if bumping into other people) so on.