Add continuous predator-prey model and refine documentation

[Ishwarpatra]

Mesa 4.0 ContinuousSpace Predator-Prey Benchmark

Summary

This project adds a modern ContinuousSpace predator-prey benchmark to the Mesa 4.0 ecosystem. It serves as a showcase for the new agent-centric API pattern, utilizing:

• self.agents.shuffle_do("step") for optimized scheduling.
• ContinuousSpaceAgent for high-fidelity spatial movement.
• A complete suite including a runnable model, Solara-based visualization, and a headless performance runner.

Motive

Currently, Mesa examples lack a standardized benchmark for continuous-space environments. This project aims to bridge that gap by providing:

• Spatial Index Scaling: A reproducible control-case to measure how spatial indices handle high agent densities.
• Performance Benchmarking: A CI-friendly check to ensure performance stability across Mesa updates.
• Reference Implementation: A clean, up-to-date code path for maintainers and researchers to follow when building spatial models.

Implementation Details

The prey_predator module is structured as follows:

• agents.py: Implementation of Prey and Predator logic.
• model.py: Definition of the continuous environment and scheduler.
• app.py: Interactive UI using Solara.
• run.py: Script for headless execution and benchmarking.

Standards & Compliance

• Mesa 4.0 Compatibility: Adheres to the latest API signatures and best practices.
• Code Quality: Verified with ruff check and formatted via ruff format.
• Documentation: Level-setting README including behavior, parameters, and expected Lotka–Volterra cycle results.

Usage Guide

Interactive Visualization

To launch the interactive dashboard:

solara run app.py

Headless Benchmarking

To run the model for performance data without a GUI:

python run.py

Dependency Installation

pip install -r requirements.txt
pip install solara

Future Roadmap

• Physics Integration: Adding high-speed collision detection and resolution.
• Chemistry Simulation: Implementing reactive transformation cycles.
• Scalability: Stress testing the spatial index with 10k+ agents.

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