JoaoESmoreira

Complex Systems - Predator-Prey Modelling

In recent years, complex systems, characterized by nonlinear and dynamic interactions among multiple components, have provided fertile ground for exploring emergent phenomena and understanding the interplay between different entities. A remarkable example of such phenomena is the predator-prey dynamics.

With this work, we aim to explore the duality of possible outcomes, where populations may either coexist or face mutual extinction depending on interactions and environmental conditions. Through the proposed simulation, we seek to establish a simplified parallel to what occurs in nature, highlighting how small changes can disrupt ecosystems and lead to species extinction. We recognize that nature maintains a fragile balance, whose understanding is crucial for the conservation and preservation of biodiversity. By analyzing the obtained results, we aim to gain insights into the underlying mechanisms governing predator-prey dynamics in complex systems, thus contributing to a deeper understanding of emergent phenomena within these systems.

The full repository and documentation can be found here: Complex Systems Project Repository.

Tools and Technologies

Python3
meza predator prey

Proposed Model

This model presents three distinct types of agents: wolves (predators), sheep (prey), and grass (the sheep’s energy source), all located on a grid.

Characteristics of wolves and sheep:

Movement: Agents randomly choose a position in their Moore neighborhood and move to that position.
Energy: Agents consume energy to move, which can be replenished when they feed.
Feeding: Sheep feed on grass, while wolves feed on sheep when they occupy the same cell.
Reproduction: An agent can reproduce by dividing into two individuals, with energy equally split between them.
Death: Agents die if they run out of energy. Sheep may also die when eaten by wolves. Grass regrows in each cell at a constant rate. It is important to note that updates occur asynchronously, following the order in which agents were added to the environment.

In this model, the following parameters can be modified:

Number of individuals
Reproduction rate
Energy gained from food

Run Project

python3 -m venv env
. ./env/bin/activate
pip install -r requirements.txt

cd src
python3 run.py    # visualization mode
cd our_model
python3 model.py    # experiments

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