Simulating System Dynamics Models in Python
This project is a simple library for running System Dynamics models in python, with the purpose of improving integration of Big Data and Machine Learning into the SD workflow.
See the project documentation for information about:
For standard methods for data analysis with SD models, see the PySD Cookbook, containing (for example):
- Model Fitting
- Surrogating model components with machine learning regressions
- Multi-Scale geographic comparison of model predictions
If you use PySD in any published work, consider citing the PySD Introductory Paper:
Houghton, James; Siegel, Michael. "Advanced data analytics for system dynamics models using PySD." Proceedings of the 33rd International Conference of the System Dynamics Society. 2015.
There are a number of great SD programs out there (Vensim, iThink, AnyLogic, Insight Maker, and others). In order not to waste our effort, or fall victim to the Not-Invented-Here fallacy, we should have a very good reason for starting a new project.
That reason is this: There is a whole world of computational tools being developed in the larger data science community. System dynamicists should directly use the tools that other people are building, instead of replicating their functionality in SD specific software. The best way to do this is to bring specific SD functionality to the domain where those other tools are being developed.
This approach allows SD modelers to take advantage of the most recent developments in data science, and focus our efforts on improving the part of the stack that is unique to System Dynamics modeling.
If you'd like to work with this repository directly, you'll need to use a recursive git checkout in order to properly load the test suite (sorry..)
The command should be something like:
git clone --recursive https://github.com/JamesPHoughton/pysd.gitMany people have contributed to developing this project - by submitting code, bug reports, and advice.
Special thanks to the sdCloud.io development team, who have made great contributions to XMILE support, and for integrating PySD into their cloud-based model simulation environment.