The first is an agent-based model of gentrification in part of Washington DC. Within the model, developers create new houses, residents can move in and out and as a result the demographics of the area changes over time. The movie below gives a sense of the model dynamics.
In another model, a student developed a simple agent-based model which asks the question if random police patrols vs. a concentrated police effort can reduce the number of burglaries in an area. The burglars have a simple routine and commit crimes when their energy reserves fall below a certain threshold. If the police agents see a crime being committed they arrest the burglar. Over time, crime hotspots emerge (detected using DBSCAN) which lead to an increased police presence in an area. The movie below gives a sense of the model dynamics.
In another model, a student explored how commuting behaviors of agents might lead to traffic jams and how different transportation options might reduce congestion. The movie below gives a sense of the model dynamics.
Overall it was a fun class with many interesting models programed in a variety of languages and toolkits (Python, Java, MASON, NetLogo).