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Probabilistic Relational Agent-Based Models (PRAMs)

Probabilistic Relational Agent-based Models (PRAMs) is a modeling and simulation framework that puts agent-based models (ABMs) on a sound probabilistic foundation. When compared to equivalent ABMs, PRAMs are:

  • More space- and time-efficient (due to the way it encodes agent population)
  • More sound (due to being probabilistic)
  • More expressive (due to being relational)

For more information see documentation.

This software is in the pre-release stage.

Dependencies - Core Library (src/pram)

Dependencies - Simulation Library (src/sim)

None

Dependencies - The Web App (src/web)

Backend:

Front-end:

Setup

You can install PyPRAM like so:

pip install git+https://github.com/momacs/pram.git

To install all extra dependencies instead, do:

pip install git+https://github.com/momacs/pram.git#egg=pram[all]

Remember to activate your venv of choice unless you want to go system-level.

The momacs Utility

To create a new venv and install PyPRAM inside of it, use the momacs command-line utility like so:

momacs app-pram setup

Ubuntu

The setup-ubuntu.sh script is the preferred method of deploying the package and all its dependencies (including the system-level ones) onto a fresh installation of the Ubuntu Server/Desktop (tested with 18.04 LTS). This is ideal for initializing virtual machine images. The do_env variable controls whether the package and its dependencies are installed inside a Python venv (yes by default). The setup script can be downloaded and executed like so:

sh -c "$(wget https://raw.githubusercontent.com/momacs/pram/master/script/setup-ubuntu.sh -O -)"

References

Documentation

Cohen, P.R. & Loboda, T.D. (2019) Probabilistic Relational Agent-Based Models. International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (BRiMS), Washington, DC, USA. PDF

Loboda, T.D. (2019) Milestone 3 Report.

Loboda, T.D. & Cohen, P.R. (2019) Probabilistic Relational Agent-Based Models. Poster presented at the International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (BRiMS), Washington, DC, USA. PDF

License

This project is licensed under the BSD License.