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When an agent moves, move-to is used to move the agent to the center of the patch it eventually finds. Sprout is used to create agents while ensuring no patch has more than one agent on it. Can you design a different visualization that emphasizes different aspects? NETLOGO FEATURES The SQUARE-X visualization shows whether an agent is happy or not. The two different visualizations emphasize different aspects of the model. When they don't, they use color as a proxy - i.e., they assume that agents of the same color make good neighbors. Suppose further that they don't always know if someone makes a good neighbor. One idea: suppose that the agents need some minimum threshold of 'good neighbors' to be happy with their location. For instance, have unhappy agents decide on a new location based on information about what a neighborhood is like from other agents in their network.Д¬hange the rules for agent happiness. Incorporate social networks into this model. Can you rewrite this procedure so the agents move directly to an appropriate new spot? The find-new-spot procedure has the agents move locally till they find a spot. How does the initial occupancy density affect the percentage of unhappy agents? How does it affect the time it takes for the model to finish?Д¬an you set sliders so that the model never finishes running, and agents keep looking for new locations? EXTENDING THE MODEL If each agent wants at least 40% same-color neighbors, what percentage (on average) do they end up with? How does the overall degree of segregation change? Try different values for %-SIMILAR-WANTED. So relatively small individual preferences can lead to significant overall segregation. In the case where each agent wants at least 30% same-color neighbors, the agents end up with (on average) 70% same-color neighbors. But the neighborhood becomes more segregated, with clusters of orange agents and clusters of blue agents. Over time, the number of unhappy agents decreases. But when the blue agents move to a new area, they might prompt orange agents to leave that area. If a few agents move into an area, the local blue agents might leave. But in the new locations, they might tip the balance of the local population, prompting other agents to leave. The unhappy agents move to new locations in the vicinity. But many agents are 'unhappy' since they don't have enough same-color neighbors. When you execute SETUP, the orange and blue agents are randomly distributed throughout the neighborhood. The SQUARE-X option visualizes the agents as squares. The OLD option uses the visualization that was used by the segregation model in the past. The VISUALIZATION chooser gives two options for visualizing the agents. The % SIMILAR and the NUM-UNHAPPY monitors are also plotted. The NUM-UNHAPPY monitor shows the number of unhappy agents, and the % UNHAPPY monitor shows the percent of agents that have fewer same-color neighbors than they want (and thus want to move). It starts at about 50%, since each agent starts (on average) with an equal number of orange and blue agents as neighbors. The % SIMILAR monitor shows the average percentage of same-color neighbors for each agent. For example, if the slider is set at 30, each blue agent wants at least 30% of its neighbors to be blue agents. (It takes effect the next time you click SETUP.) The %-SIMILAR-WANTED slider controls the percentage of same-color agents that each agent wants among its neighbors. The DENSITY slider controls the occupancy density of the neighborhood (and thus the total number of agents). (The topology is wrapping, so that patches on the bottom edge are neighbors with patches on the top and similar for left and right). If agents don't have enough same-color neighbors, they move to a nearby patch. The agents are set up so no patch has more than one agent. There are approximately equal numbers of orange and blue agents.

Netlogo modeling commons how to#

HOW TO USE ITД¬lick the SETUP button to set up the agents. This project was inspired by Thomas Schelling's writings about social systems (such as housing patterns in cities).

The simulation shows how these individual preferences ripple through the neighborhood, leading to large-scale patterns. But each agent wants to make sure that it lives near some of 'its own.' That is, each orange agent wants to live near at least some orange agents, and each blue agent wants to live near at least some blue agents. The orange agents and blue agents get along with one another. This project models the behavior of two types of agents in a neighborhood.

You can also Try running it in NetLogo Web

Netlogo modeling commons download#

If you download the NetLogo application, this model is included.