[Re] Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents

TMLR Paper4304 Authors

Abstract:

Large Language Models (LLMs) are increasingly used in strategic decision-making environ-
ments, including game-theoretic scenarios where multiple agents interact under predefined
rules. One such setting is the common pool resource environment. In this study, we build
upon Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM
Agents (Piatti et al., 2024), a framework designed to test cooperation strategies among LLM
agents. We begin by replicating their results to a large degree to validate the framework.
Then, we extend their analysis by identifying a notable trend: specialized models trained
on research papers and mathematical reasoning tasks outperform general-purpose models of
similar scale in this environment. Additionally, we evaluate the recently released DeepSeek-
R1-Distill models, which show improvements over their baseline counterparts but come at
a higher computational cost. Finally, we investigate the impact of different prompting
strategies, including the veil of ignorance mechanism and other prompting strategies based
on universalization principles with varying levels of abstraction. Our results suggest that
older models benefit significantly from explicit boundary conditions, whereas newer models
demonstrate greater robustness to implicit constraints.

Submission Length: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: Quanquan Gu

Submission Number: 4304