Prey - Predator Model

Students

• Jérôme Auguste
• Magali Morin

Description of the project

In the class of SMA, we had to develop a simple model consisting of three agent types: Wolf, Sheep, and GrassPatch. All agents evolve in a space represented by a grid.

Agents' possible actions:

• Sheep and Wolf: eat, reproduce, move
• GrassPatch: when eaten by a Sheep, takes some time to fully grow again, is eaten by the Sheeps

Run the code

Install requirements

    pip install -r requirements.txt

Run the server

    mesa runserver

Or, at the root of the project

    python ./run.py

Organisation of the project

• prey_predator/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.
• prey_predator/agents.py: Defines the Wolf, Sheep, and GrassPatch agent classes.
• prey_predator/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.
• prey_predator/model.py: Defines the Prey-Predator model itself
• prey_predator/server.py: Sets up the interactive visualization server
• run.py: Launches a model visualization server.

Implementation

• Display icons and current energy: on the grid Sheep and Wolves are both represented by icons with their current energy.

• Chasing mode: at first the movements of the Sheep and Wolves were set at random. To obtain better results, we implemented a real Wolf and Sheep movement behaviour (Wolves chasing sheep, Sheep running away from wolves).

• Energy gained: we decided to quantify the energy gained by a Wolf to the energy of the Sheep he eats.

• Tests: some unit tests can be run on the agents using pytest

Parameters

The parameters that can be modified are:

• Number of Wolves and Sheep on the grid before the start (10-200)
• Reproduction rate of Wolves and Sheep (0-1)
• Energy gained from eating Sheep (1-50)
• Energy gained from eating Grass (1-20)
• Energy at creation for Wolves and Sheep (1-100)
• Grass growing time (1-100)
• Chasing mode (True/False)

These values were the equilibrium we found for this model. Changing one value makes it unstable unless we find the right equilibrium for all the other parameters:

Parameters	Value
Number of Wolves at start	10
Number of Sheep at start	40
Reproduction rate of Wolves	0.09
Reproduction rate of Sheep	0.09
Energy gained from eating Sheep	10
Energy gained from eating Grass	10
Energy at creation for Wolves	4
Energy at creation for Sheep	10
Grass growing time	16
Chasing mode	False

Results

Analysis

At the initialization of the environment, grass patches are all fully grown so that sheep can eat them and live longer (increasing reproduction chance). However, as soon as the sheep population increases, it gives more opportunities for wolves to eat and live longer (and so to reproduce). Therefore, wolves population grows and sheep die faster (being eaten). Then at a certain point, wolves are not able to eat anymore and die from fatigue whereas free sheep start to reproduce again while eating more fully-grown grass. Then the model oscillates again and again.