strategicwm

Overview

The name strategicwm (or swm for short) stands for strategic world models.

swm uses LLMs to aids users in automating the construction of formal strategic world models, e.g., game trees, simply from natural language descriptions. Utilities are provided for visualizing and performing game-theoretic analysis of these multi-agent interactions.

a) swm currently offers two approaches to building strategic world models, the first of which takes a multi-agent approach to game tree construction itself. This is demonstrated in the colabs/cultivate notebook.

Extensive-form games are typically defined as a list or tuple of critical components:

• A set of players (including a chance / nature player),
• A mapping from world state to player identity (whose turn it is),
• A mapping from information state to the set of actions available to a player,
• A mapping for each player from states to information sets (what information is available to each player in a given state),
• A mapping from state to a binary outcome indicating a terminal node (leaf) has been reached,
• A mapping from terminal nodes (leaves) to payoffs for each player.

swm defines a specialized agent to execute each of these functionalities. Analogously to how the software development cycle has been decomposed into an assembly line of various role-specific agents executing bespoke tasks, swm decomposes the rigorous modeling of an extensive-form game into the coordination of the above set of specialized agents.

swm models the root node as a chance node which generates all the possible initial world states the scenario might describe. LLM generation is stochastic in general, so their responses are also modeled as chance nodes. The user can control the number of stochastic responses considered with the num_llm_seeds parameter.

Each expansion of a node in the game tree might require the coordination of multiple agents and hence require multiple LLM calls (~3). For this reason, swm also offers a second approach that potentially trades accuracy for faster speed and lower cost.

b) The second approach generates a full json tree from a single LLM call, which will likely be much cheaper in terms of token cost and also much faster to generate trees. The downside is sometimes a less comprehensive model of the interaction and fewer options to easily edit / re-generate subtrees. This is demonstrated in the colabs/transplant notebook.

What is strategicwm for?

The overarching goal of swm is to provide an API between the multi-agent interactions that exist in everyday life and the wealth of abstract, academic research on this topic in fields like computational game theory. With such a bridge, we hope learn how to make better decisions in real-world scenarios, and, as a byproduct, improve our abstractions and research in game theory.

• Game Construction: swm is designed to quickly prototype small game trees based on user descriptions of arbitrary scenarios.
• Game Solving: swm provides utils for loading user constructed game trees into OpenSpiel for game-theoretic analysis.
• Research: swm implements accelerated equilibrium solvers for n-player general-sum games, which can also serve as baselines for game-theory research in equilibrium solving and selection.

Installation

You can install strategicwm from PyPi:

pip install -U strategicwm

or from source, with no stability guarantees.

pip install git+git://github.com/google-deepmind/strategicwm.git

Quick Start

The colabs are the intended way to gain familiarity with the library. Unless you are looking to fork and build upon strategicwm, these will serve as your main way of interacting with the library. The colabs/ directory contains three example colabs. The cultivate and transplant colabs demonstrate the two approaches described in the overview: "node-by-node" and "one-shot" game tree generation. The eval colab demonstrates how to derive a policy from a strategic world model (a.k.a. game tree) and then evaluate it against either a policy derived from the true game tree using OpenSpiel or against an LLM with a simple game harness that generates moves using raw observations as input. This eval colab is set up specifically for Kuhn poker.

You should even be able to open the .ipynb notebooks directly from GitHub. Just add a cell at the top and run the following to get running:

!pip install strategicwm

Define Your Own Client Interface

This package is set up to use Google LLMs (a.k.a. Gemini models). If you would like to use alternative models, there is one file _src/client_lib.py that you must modify to depend on your desired LLM client. Specifically, the query_llm method must be updated. Make sure to adhere to the protocols defined at the top of the file. Simply clone this repo, modify the code accordingly, and build from source.

References

If you find this library useful, please consider citing these works:

@inproceedings{gemp2024steering,
  title={Steering language models with game-theoretic solvers},
  author={Gemp, Ian and Patel, Roma and Bachrach, Yoram and Lanctot, Marc and
  Dasagi, Vibhavari and Marris, Luke and Piliouras, Georgios and Liu, Siqi and
  Tuyls, Karl},
  booktitle={Agentic Markets Workshop at ICML 2024},
  year={2024}
}

@article{daskalakis2024charting,
  title={Charting the shapes of stories with game theory},
  author={Daskalakis, Constantinos and Gemp, Ian and Jiang, Yanchen and
  Leme, Renato Paes and Papadimitriou, Christos and Piliouras, Georgios},
  booktitle={Creative AI Track at NeurIPS 2024},
  year={2024}
}

This project also draws inspiration from these published works:

• Deng, Shilong, Yongzhao Wang, and Rahul Savani. "From Natural Language to Extensive-Form Game Representations." Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems. 2025.

Disclaimer

This is not an officially supported Google product.