Hospital and testing and ICU behavior This model simulates how a virus spreads and perpetuates in a human population. Green agents (healthy people) have a chance of becoming sick if they share the same space with a red agent (sick person). Agents that are grey are immune to the disease until their immunity wears out and they become green agents again. Each step in this simulation represents a week. Agents randomly move around until they die from sickness or from old age. The life span of each agent is 50 years (or 2600 weeks). Each step, red agents have a chance to recover. If a red agents remains sick for the entirety of the sickness duration then they die. http://www.netlogoweb.org/launch#http://www.netlogoweb.org/assets/modelslib/Sample%20Models/Biology/Virus.nlogo
Load Transit Network
Test loading of gtfs transit network data from exported json.
Separate LTC Stop Times
Based on GTFS data from London (Ontario, CA)'s Transit Commission.
Network Implementation 1
This simulation demonstrates how a network implementation with the current engine could function, with the assumption that stdlib functions would change how the user actually interacts with network features. In this implementation, edges are represented and used as agents. This means that we would expect and encourage users to add behaviors to edges. The edges get height values from their attached nodes, then use messages to cause them to change their height in a diffusion-like way.
Action Language Test Bed
Simulation for testing out the action language paradigm
Object Storing Test
This empty simulation has no behavior. It's an empty scaffold to build from.
[WIP] Network Behaviors
Network Implementation 3
This simulation demonstrates how a network implementation with the current engine could function, with the assumption that stdlib functions would change how the user actually interacts with network features. In this implementation, edges don't actually exist in the engine. Nodes simply have a network object which contains information about their network neighbors and all relevant properties (such as directed/undirected edge, edge length, etc...) Nodes search their neighbors for nodes they are connected to and modify their height in a diffusion-like manner.