Updates in Cooperative AI

As AI increasingly operates in multi-agent environments, understanding how LLM agents handle social dilemmas is critical for safe deployment. In this talk, Zhijing Jin presents findings from her two studies exploring LLM cooperation in social simulations. First, GovSim work examines common-pool resource management, where agents must balance immediate extraction with long-term sustainability. Most LLMs fail to sustain cooperation, except for the most powerful models. Next, SanctSim work studies public goods games with institutional mechanisms, revealing four behavioural patterns: cooperators, oscillators, deteriorators, and rigid strategists. Counterintuitively, reasoning-optimised LLMs often underperform compared to traditional LLMs in sustaining cooperation. These findings show that enhanced reasoning doesn't guarantee cooperative behaviour – cooperation depends on communication strategies and moral reasoning. The insights have important implications for designing collaborative multi-agent AI systems.

We’re delighted to host this third seminar in our 'Updates in Cooperative AI' Series. We're running these seminars monthly, and you're welcome to subscribe to our Google Calendar to stay up-to-date on all upcoming events.