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Chapter 4: System Dynamics

Part II — Simulation Paradigms

Implemented by the dissmodel-sysdyn package.

Live demo: try the models in this chapter directly in the browser — dissmodel-sysdyn-demo on Hugging Face Spaces.

Learning objectives

  • Install and explore the system dynamics model library
  • Run models via CLI, Streamlit, and notebooks
  • Recognize the categories of available models

4.1 Installation

dissmodel-sysdyn's own README shows pip install dissmodel-sysdyn, but the package has no PyPI release (no PyPI badge, no publish workflow) — the main dissmodel repository's own ecosystem table documents the actual install path as a direct GitHub install, same as every other extension package (Chapter 3):

pip install "git+https://github.com/DisSModel/dissmodel-sysdyn.git"

4.2 Included models by category

Category Models
Epidemiology SIR
Ecology & Biology Predator-Prey (Lotka-Volterra), Yeast Growth, Daisyworld, Population Growth, Limited Growth, Chaotic Growth
Physics & Thermodynamics Coffee Cooling, Room Temperature, Tub (Stock/Flow)
Complex Systems Lorenz Attractor, Homeostasis
Environment Mono Lake Water Balance
Stochasticity Random Walk

4.3 Usage

python examples/cli/sysdyn_sir.py
streamlit run examples/streamlit/sysdyn_all.py
jupyter notebook examples/notebooks/

There are 14 educational notebooks, each functioning as a self-contained tutorial with scientific context, mathematical formulation, and guided experiments (e.g. sysdyn_daisyworld.ipynb on the Gaia hypothesis, sysdyn_lorenz.ipynb on deterministic chaos).

Exercises

  1. Open src/dissmodel_sysdyn/models/sir.py. Unlike every model in Chapter 5, SIR extends dissmodel.core.Model directly, not SpatialModel. Explain why a system-dynamics model has no need for a gdf/backend or a neighborhood.
  2. SIR is decorated with three stacked @track_plot("Susceptible", "green") / @track_plot("Infected", "red") / @track_plot("Recovered", "blue") calls. Run python examples/cli/sysdyn_sir.py and identify, from the live chart, which compartment peaks first and why (hint: compare the duration and contacts default parameters documented in the class docstring).
  3. Compare sir.py (a compartmental model with discrete stocks) to lorenz.py (a continuous three-variable chaotic system). Both override execute() on a plain Model — what does each model's execute() compute per tick, and why does neither need pre_execute or post_execute?
  4. Read the sysdyn_daisyworld.ipynb notebook's introduction and describe, in your own words, what the Gaia hypothesis claims and how the model's stocks (black daisies, white daisies, bare ground) operationalize it.

Summary

dissmodel-sysdyn shows that DisSModel's core lifecycle (setup → execute, driven by Environment.run()) is not tied to spatial data at all: every model here subclasses dissmodel.core.Model directly, with no gdf, no backend, and no neighborhood — the state is just a handful of numeric stocks (susceptible/infected/recovered, predator/prey populations, x/y/z in the Lorenz system) advanced by ODE-like update equations each tick. The @track_plot decorator is the piece that turns any tracked attribute into an automatically-charted time series with no extra plumbing, which is why every model in the package — from SIR to Daisyworld to the Lorenz attractor — can be dropped into the same Streamlit explorer (sysdyn_all.py) used for CA models in Chapter 5. The 14 notebooks are this package's main teaching surface: each pairs a scientific framing (epidemiology, ecology, thermodynamics, deterministic chaos) with the exact stock/flow equations implemented in src/dissmodel_sysdyn/models/.