SimLab

Overview

SimLab is a comprehensive simulation toolkit for modelling complex systems across multiple domains. Students run, configure, and analyse simulations through a unified interface — CLI, terminal UI, web, or Python API.

GitHub: michael-borck/sim-lab Install: pip install sim-lab

What It Simulates

SimLab includes 18+ simulation categories spanning business, science, and systems thinking:

Category	Examples
Business	Stock market, product popularity, resource fluctuations, supply chains
Statistical	Monte Carlo methods, Markov chains
Agent-based	Autonomous interacting agents with emergent behaviour
System dynamics	Stocks, flows, and feedback loops
Network	Topologies and processes on graphs
Ecological	Predator-prey (Lotka-Volterra), population dynamics
Epidemiological	SIR/SEIR disease spread models
Discrete event	Queueing theory, event-driven processes
Cellular automata	Grid-based rule systems

Multiple Interfaces

# CLI — run a simulation directly
simlab stock-market run --start-price 100 --days 365 --volatility 0.02

# TUI — interactive terminal interface
simlab-tui

# Web — browser-based dashboard
simlab-web

All interfaces share the same simulation engine. Students can start with the CLI, explore interactively in the TUI, and build dashboards with the web interface.

The Rehearsal

SimLab is rehearsal for systems thinking. Students don’t just learn about complex systems — they build models, adjust parameters, and observe what happens. A stock market simulation with different volatility settings teaches more about risk than a textbook definition. An SIR model with adjustable transmission rates makes epidemiology tangible.

The simulation registry system teaches software design patterns: dynamic discovery, consistent APIs across different model types, and parameter validation. Students writing their own simulations learn to extend a well-designed framework.

Disciplines

SimLab crosses traditional faculty boundaries:

• Information Systems — supply chain and queueing models
• Marketing — product popularity and market dynamics
• Management — resource allocation and scenario planning
• Science — ecological and epidemiological models
• Statistics — Monte Carlo and Markov chain methods

Part of LocoEnsayo

LocoEnsayo covers two types of rehearsal: professional skills (talking to AI characters) and AI development (learning to build and model systems). SimLab sits alongside SimpleBot and HandsOnAI in the development toolkit — a different kind of rehearsal, focused on understanding complex systems through simulation rather than conversation.