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.

• Python 3
• altair
• altair-saver
• attrs
• cloudpickle
• dotmap
• iteround
• matplotlib
• NetworkX
• numpy
• psutil
• psycopg2
• pybind11 (for PyRQA)
• PyOpenCL (for PyRQA)
• PyRQA
• ray
• scipy
• sortedcontainers
• tqdm
• xxhash
• selenium (for saving altair graphs)
• Gecko Driver or Chrome Driver and a recent version of either of the respective Web browser (i.e., Firefox or Chrome; for saving altair graphs)

None

Backend:

• Flask
• Celery
• Redis
• PSUtil

Front-end:

• Materialize
• jQuery
• Fuse.js

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.

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

momacs app-pram setup

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 -)"

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

This project is licensed under the BSD License.