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Wolf-Sheep Predation Model

Summary

A simple ecological model, consisting of three agent types: wolves, sheep, and grass. The wolves and the sheep wander around the grid at random. Wolves and sheep both expend energy moving around, and replenish it by eating. Sheep eat grass, and wolves eat sheep if they end up on the same grid cell.

If wolves and sheep have enough energy, they reproduce, creating a new wolf or sheep (in this simplified model, only one parent is needed for reproduction). The grass on each cell regrows at a constant rate. If any wolves and sheep run out of energy, they die.

The model is tests and demonstrates several Mesa concepts and features:

  • MultiGrid
  • Multiple agent types (wolves, sheep, grass)
  • Overlay arbitrary text (wolf's energy) on agent's shapes while drawing on CanvasGrid
  • Agents inheriting a behavior (random movement) from an abstract parent
  • Writing a model composed of multiple files.
  • Dynamically adding and removing agents from the schedule

Installation

To install the dependencies use pip and the requirements.txt in this directory. e.g.

    $ pip install -r requirements.txt

How to Run

To run the model interactively, run mesa runserver in this directory. e.g.

    $ mesa runserver

Then open your browser to http://127.0.0.1:8521/ and press Reset, then Run.

Files

  • wolf_sheep/random_walker.py: This defines the RandomWalker agent, which implements the behavior of moving randomly across a grid, one cell at a time. Both the Wolf and Sheep agents will inherit from it.
  • wolf_sheep/test_random_walk.py: Defines a simple model and a text-only visualization intended to make sure the RandomWalk class was working as expected. This doesn't actually model anything, but serves as an ad-hoc unit test. To run it, cd into the wolf_sheep directory and run python test_random_walk.py. You'll see a series of ASCII grids, one per model step, with each cell showing a count of the number of agents in it.
  • wolf_sheep/agents.py: Defines the Wolf, Sheep, and GrassPatch agent classes.
  • wolf_sheep/schedule.py: Defines a custom variant on the RandomActivation scheduler, where all agents of one class are activated (in random order) before the next class goes -- e.g. all the wolves go, then all the sheep, then all the grass.
  • wolf_sheep/model.py: Defines the Wolf-Sheep Predation model itself
  • wolf_sheep/server.py: Sets up the interactive visualization server
  • run.py: Launches a model visualization server.

Further Reading

This model is closely based on the NetLogo Wolf-Sheep Predation Model:

Wilensky, U. (1997). NetLogo Wolf Sheep Predation model. http://ccl.northwestern.edu/netlogo/models/WolfSheepPredation. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

See also the Lotka–Volterra equations for an example of a classic differential-equation model with similar dynamics.