Model Zoo
ABMForge includes a growing collection of reference models that demonstrate common agent-based modeling patterns, scientific workflows, and best practices.
Purpose
The Model Zoo serves three purposes:
- Learning ABMForge
- Providing reproducible scientific examples
- Offering reusable starting points for research projects
Available Models
Schelling Segregation
Location:
model_zoo/schelling/
Demonstrates: - Grid environments - Agent relocation - Neighborhood analysis - Emergent segregation
SIR Epidemic
Location:
model_zoo/sir/
Demonstrates: - Disease transmission - State transitions - Population dynamics - Epidemiological simulation
Planned Models
Opinion Dynamics
- Consensus formation
- Polarization
- Social influence
Wealth Distribution
- Economic inequality
- Wealth exchange
- Redistribution
Market Simulation
- Financial markets
- Trading agents
- Market microstructure
Predator-Prey
- Ecological systems
- Population cycles
Flocking
- Collective motion
- Self-organization
Network Diffusion
- Information spreading
- Cascade dynamics
Common Structure
Each model follows:
model_name/ ├── README.md ├── model.py ├── agents.py ├── run.py └── config.py
Reproducibility
Every example should:
- Support deterministic seeds
- Export datasets
- Document parameters
- Include scientific references
Dataset Outputs
Examples may export:
- agent_state.csv
- model_state.csv
- event_log.csv
Educational Goals
The Model Zoo helps users:
- Learn ABM concepts
- Learn ABMForge APIs
- Build research-grade simulations
- Develop reproducible workflows
Roadmap
Near-term additions:
- Opinion Dynamics
- Wealth Distribution
- Market Simulation
Long-term additions:
- Reinforcement Learning Agents
- Multi-layer Networks
- Spatial Economics
- Large-scale Simulation Benchmarks