Welfare Diplomacy: Benchmarking Language Model Cooperation
Gabriel Mukobi Hannah Erlebach Niklas Lauffer Lewis Hammond Alan Chan Jesse Clifton
The growing capabilities and increasingly widespread deployment of AI systems
necessitate robust benchmarks for measuring their cooperative capabilities.
Unfortunately, most multi-agent benchmarks are either zero-sum or purely
cooperative, providing limited opportunities for such measurements. We
introduce a general-sum variant of the zero-sum board game Diplomacy -- called
Welfare Diplomacy -- in which players must balance investing in military
conquest and domestic welfare. We argue that Welfare Diplomacy facilitates both
a clearer assessment of and stronger training incentives for cooperative
capabilities. Our contributions are: (1) proposing the Welfare Diplomacy rules
and implementing them via an open-source Diplomacy engine; (2) constructing
baseline agents using zero-shot prompted language models; and (3) conducting
experiments where we find that baselines using state-of-the-art models attain
high social welfare but are exploitable. Our work aims to promote societal
safety by aiding researchers in developing and assessing multi-agent AI
systems. Code to evaluate Welfare Diplomacy and reproduce our experiments is
available at https://github.com/mukobi/welfare-diplomacy.