Foundation: An Economic Simulation Framework

This repo contains an implementation of Foundation, a framework for flexible, modular, and composable environments that model socio-economic behaviors and dynamics in a society with both agents and governments.

Foundation provides a Gym-style API:

• reset: resets the environment's state and returns the observation.
• step: advances the environment by one timestep, and returns the tuple (observation, reward, done, info).

This simulation can be used in conjunction with reinforcement learning to learn optimal economic policies, as detailed in the following papers:

The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies, Stephan Zheng, Alexander Trott, Sunil Srinivasa, Nikhil Naik, Melvin Gruesbeck, David C. Parkes, Richard Socher.

The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning Stephan Zheng, Alexander Trott, Sunil Srinivasa, David C. Parkes, Richard Socher.

Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist Alexander Trott, Sunil Srinivasa, Douwe van der Wal, Sebastien Haneuse, Stephan Zheng.

If you use this code in your research, please cite us using this BibTeX entry:

@misc{2004.13332,
 Author = {Stephan Zheng, Alexander Trott, Sunil Srinivasa, Nikhil Naik, Melvin Gruesbeck, David C. Parkes, Richard Socher},
 Title = {The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies},
 Year = {2020},
 Eprint = {arXiv:2004.13332},
}

For more information and context, check out:

• The AI Economist website
• Blog: The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies
• Blog: The AI Economist moonshot
• Blog: The AI Economist web demo of the COVID-19 case study
• Web demo: The AI Economist ethical review of AI policy design and COVID-19 case study

Simulation Cards: Ethics Review and Intended Use

Please see our Simulation Card for a review of the intended use and ethical review of our framework.

Please see our COVID-19 Simulation Card for a review of the ethical aspects of the pandemic simulation (and as fitted for COVID-19).

Join us on Slack

If you're interested in extending this framework, discussing machine learning for economics, and collaborating on research project:

• join our Slack channel aieconomist.slack.com using this invite link, or
• email us @ ai.economist@salesforce.com.

Installation Instructions

To get started, you'll need to have Python 3.7+ installed.

Using pip

Simply use the Python package manager:

pip install ai-economist

Installing from Source

1. Clone this repository to your local machine:

 git clone www.github.com/salesforce/ai-economist

2. Create a new conda environment (named "ai-economist" below - replace with anything else) and activate it

 conda create --name ai-economist python=3.7 --yes
 conda activate ai-economist

3. Either

   a) Edit the PYTHONPATH to include the ai-economist directory

 export PYTHONPATH=:$PYTHONPATH

OR

b) Install as an editable Python package

 cd ai-economist
 pip install -e .

Useful tip: for quick access, add the following to your ~/.bashrc or ~/.bash_profile:

alias aiecon="conda activate ai-economist; cd "

You can then simply run aiecon once to activate the conda environment.

Testing your Install

To test your installation, try running:

conda activate ai-economist
python -c "import ai_economist"

Getting Started

To familiarize yourself with Foundation, check out the tutorials in the tutorials folder. You can run these notebooks interactively in your browser on Google Colab.

Multi-Agent Simulations

• economic_simulation_basic (Try this on Colab!): Shows how to interact with and visualize the simulation.
• economic_simulation_advanced (Try this on Colab!): Explains how Foundation is built up using composable and flexible building blocks.
• optimal_taxation_theory_and_simulation (Try this on Colab!): Demonstrates how economic simulations can be used to study the problem of optimal taxation.
• covid19_and_economic_simulation (Try this on Colab!): Introduces a simulation on the COVID-19 pandemic and economy that can be used to study different health and economic policies .

Multi-Agent Training

• multi_agent_gpu_training_with_warp_drive (Try this on Colab!): Introduces our multi-agent reinforcement learning framework WarpDrive, which we then use to train the COVID-19 and economic simulation.
• multi_agent_training_with_rllib (Try this on Colab!): Shows how to perform distributed multi-agent reinforcement learning with RLlib.
• two_level_curriculum_training_with_rllib: Describes how to implement two-level curriculum training with RLlib.

To run these notebooks locally, you need Jupyter. See https://jupyter.readthedocs.io/en/latest/install.html for installation instructions and (https://jupyter-notebook.readthedocs.io/en/stable/ for examples of how to work with Jupyter.

Structure of the Code

• The simulation is located in the ai_economist/foundation folder.

The code repository is organized into the following components:

• The datasets (including the real-world data on COVID-19) are located in the ai_economist/datasets folder.

Releases and Contributing

• Please let us know if you encounter any bugs by filing a GitHub issue.
• We appreciate all your contributions. If you plan to contribute new Components, Scenarios Entities, or anything else, please see our contribution guidelines.

Changelog

For the complete release history, see CHANGELOG.md.

License

Foundation and the AI Economist are released under the BSD-3 License.