gbroques / modsimpy-notes

Notes on Modeling and Simulation in Python.

ModSimPy Notes

Notes on Modeling and Simulation in Python. A copy of the book is included in this repository as ModSimPy3.pdf, and was downloaded from Green Tea Press.

• Pre-Requisites
• Getting Started
• Chapter 1: Modeling
• Chapter 2: Bike Share
• Chapter 3: Iterative Modeling

Pre-Requisites

1. Install git (if not already installed)
2. Install Miniconda (for conda command)

Getting Started

1. Clone the repository (using commit c36a47)

   git clone https://github.com/AllenDowney/ModSimPy
   

2. Create conda environment

   conda env create -f environment.yml
   

3. Activate newly created conda environment

   conda activate ModSimPy
   

Chapter 1: Modeling

Starting from the lower-left corner of the above diagram, and navigating clock-wise:

• System - something in the real world we're interested in studying.

• Abstraction - often the system is complicated, so we have to remove details.

• Model - the result of abstraction. A description of the system with only the essential details.

• Analysis & Simulation - model can be represented in diagrams and equations, and implemented in a computer program.

• Prediction, Explanation, or Design - the result of analysis and simulation might be a prediction about what the system will do, an explanation of why it behaves the way it does, or a design intended to achieve a purpose.

• Validate - validate predictions and test designs by taking measurements from the real world and comparing the data we get with the results from analysis and simulation.

• Often simpler models are best

• Iterative Modeling

  1. Start with a simple model, even if it is likely to be too simple.
  2. Test whether it is good enough for its purpose.
  3. If not good enough for it's purpose, then gradually add features, starting with the ones you expect to be most essential.

• Internal Validation - Comparing results of successive models can catch conceptual, mathematical, and software errors. By adding and removing features, you can tell which ones have the biggest effect on the results, and which can be ignored.

• External Validation - Comparing results to data from the real world is generally the strongest test.

Chapter 2: Bike Share

See chap2.py.

python chap2.py

Chapter 3: Iterative Modeling

The process we use to make models less wrong.

Exercise: Make a list of ways this model is unrealistic.

Questions to Drive Exercise:

• What assumptions is the model based on?
• What are the differences between the model and the real world?

Iterative Modeling

1. Start with a simple model
2. Identify the most important problems
3. Make gradual improvements

Deterministic vs Stochastic Models: Deterministic - predictable; do the same thing every time they run Stochastic - un-deterministic, un-predictable, and random behavior

Metrics - Statistics that quantify how well the system works are called metrics.