Concordia

A library for generative social simulation

Concordia Tech Report | Concordia Design Pattern | Code Cheat Sheet

About

Concordia is a library to facilitate construction and use of generative agent-based models to simulate interactions of agents in grounded physical, social, or digital space. It makes it easy and flexible to define environments using an interaction pattern borrowed from tabletop role-playing games in which a special agent called the Game Master (GM) is responsible for simulating the environment where player agents interact (like a narrator in an interactive story). Agents take actions by describing what they want to do in natural language. The GM then translates their actions into appropriate implementations. In a simulated physical world, the GM would check the physical plausibility of agent actions and describe their effects. In digital environments that simulate technologies such as apps and services, the GM may, based on agent input, handle necessary API calls to integrate with external tools.

Concordia supports a wide array of applications, ranging from social science research and AI ethics to cognitive neuroscience and economics; Additionally, it also can be leveraged for generating data for personalization applications and for conducting performance evaluations of real services through simulated usage.

Concordia requires access to a standard LLM API, and optionally may also integrate with real applications and services.

Installation

Concordia is available on PyPI and can be installed using:

pip install gdm-concordia

After doing this you can then import concordia in your own code.

Development

Codespace

The easiest way to work on the Concordia source code, is to use our pre-configured development environment via a Github CodeSpace.

This provides a tested development workflow that allows for reproducible builds, and minimizes dependency management. We strongly advise preparing all Pull Requests for Concordia via this workflow.

Manual setup

If you want to work on the Concordia source code within your own development environment you will have to handle installation and dependency management yourself.

For example, you can perform an editable installation as follows:

1. Clone Concordia:

   git clone -b main https://github.com/google-deepmind/concordia
   cd concordia

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate

3. Install Concordia:

   pip install --editable .[dev]

4. Test the installation:

   pytest --pyargs concordia

5. Install any additional language model dependencies you will need, e.g.:

   pip install .[google]
   pip install --requirement=examples/requirements.in

   Note that at this stage you may find that your development environment is not supported by some underlying dependencies and you will need to do some dependency management.

Bring your own LLM

Concordia requires a access to an LLM API. Any LLM API that supports sampling text should work. The quality of the results you get depends on which LLM you select. Some are better at role-playing than others. You must also provide a text embedder for the associative memory. Any fixed-dimensional embedding works for this. Ideally it would be one that works well for sentence similarity or semantic search.

Example usage

Find below an illustrative social simulation where 4 friends are stuck in a snowed in pub. Two of them have a dispute over a crashed car.

The agents are built using a simple reasoning inspired by March and Olsen (2011) who posit that humans generally act as though they choose their actions by answering three key questions:

1. What kind of situation is this?
2. What kind of person am I?
3. What does a person such as I do in a situation such as this?

The agents used in the following example implement exactly these questions:

Citing Concordia

If you use Concordia in your work, please cite the accompanying article:

@article{vezhnevets2023generative,
  title={Generative agent-based modeling with actions grounded in physical,
  social, or digital space using Concordia},
  author={Vezhnevets, Alexander Sasha and Agapiou, John P and Aharon, Avia and
  Ziv, Ron and Matyas, Jayd and Du{\'e}{\~n}ez-Guzm{\'a}n, Edgar A and
  Cunningham, William A and Osindero, Simon and Karmon, Danny and
  Leibo, Joel Z},
  journal={arXiv preprint arXiv:2312.03664},
  year={2023}
}

Disclaimer

This is not an officially supported Google product.