Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents
Giorgio Piatti, Zhijing Jin, Max Kleiman-Weiner, Bernhard Sch\"olkopf,, Mrinmaya Sachan, Rada Mihalcea

TL;DR
This paper introduces GovSim, a simulation platform to study cooperative decision-making among LLM agents, revealing challenges in achieving sustainable cooperation and highlighting the importance of communication and moral reasoning.
Contribution
It presents a novel simulation environment for analyzing cooperation in LLM agents and demonstrates how moral reasoning improves sustainability outcomes.
Findings
Most LLM agents fail to sustain cooperation, with survival rates below 54%.
Effective multi-agent communication is crucial for cooperation.
Universalization-based reasoning significantly enhances sustainability.
Abstract
As AI systems pervade human life, ensuring that large language models (LLMs) make safe decisions remains a significant challenge. We introduce the Governance of the Commons Simulation (GovSim), a generative simulation platform designed to study strategic interactions and cooperative decision-making in LLMs. In GovSim, a society of AI agents must collectively balance exploiting a common resource with sustaining it for future use. This environment enables the study of how ethical considerations, strategic planning, and negotiation skills impact cooperative outcomes. We develop an LLM-based agent architecture and test it with the leading open and closed LLMs. We find that all but the most powerful LLM agents fail to achieve a sustainable equilibrium in GovSim, with the highest survival rate below 54%. Ablations reveal that successful multi-agent communication between agents is critical for…
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Taxonomy
TopicsOpen Source Software Innovations · Auction Theory and Applications
