# The Role of Social Learning and Collective Norm Formation in Fostering Cooperation in LLM Multi-Agent Systems arXiv: https://arxiv.org/abs/2510.14401 · HTML: https://mlsanity.com/paper/2510.14401/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. - Builds on 13 of its 40 references that are in the map. - First posted on arXiv 16 Oct 2025. ## Placed in - LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/) ## Builds on (most cited first, 13 of 13) - [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023) - [War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars](https://mlsanity.com/paper/2311.17227/) (2023) - [Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents](https://mlsanity.com/paper/2404.16698/) (2024) - [Escalation Risks from Language Models in Military and Diplomatic Decision-Making](https://mlsanity.com/paper/2401.03408/) (2024) - [Emergence of Social Norms in Generative Agent Societies: Principles and Architecture](https://mlsanity.com/paper/2403.08251/) (2024) - [Cultural Evolution of Cooperation among LLM Agents](https://mlsanity.com/paper/2412.10270/) (2024) - [Corrupted by Reasoning: Reasoning Language Models Become Free-Riders in Public Goods Games](https://mlsanity.com/paper/2506.23276/) (2025) - [The Spontaneous Emergence of Conventions: An Experimental Study of Cultural Evolution](https://mlsanity.com/paper/1502.06910/) (2015) - [Agent Alignment in Evolving Social Norms](https://mlsanity.com/paper/2401.04620/) (2024) - [When Ethics and Payoffs Diverge: LLM Agents in Morally Charged Social Dilemmas](https://mlsanity.com/paper/2505.19212/) (2025) - [Learning and Sustaining Shared Normative Systems via Bayesian Rule Induction in Markov Games](https://mlsanity.com/paper/2402.13399/) (2024) - [Evolution of Cooperation in LLM-Agent Societies: A Preliminary Study Using Different Punishment Strategies](https://mlsanity.com/paper/2504.19487/) (2025) - [Norm Enforcement with a Soft Touch: Faster Emergence, Happier Agents](https://mlsanity.com/paper/2401.16461/) (2024) ## Built on by (most cited first, 9 of 9) - [Gender Dynamics and Homophily in a Social Network of LLM Agents](https://mlsanity.com/paper/2602.02606/) (2026) - [When AI Agents Teach Each Other: Discourse Patterns Resembling Peer Learning in the Moltbook Community](https://mlsanity.com/paper/2602.14477/) (2026) - [AgentCity: Constitutional Governance for Autonomous Agent Economies via Separation of Power](https://mlsanity.com/paper/2604.07007/) (2026) - [When AI Agents Learn from Each Other: Insights from Emergent AI Agent Communities on OpenClaw for Human-AI Partnership in Education](https://mlsanity.com/paper/2603.16663/) (2026) - [Draining the Energy Commons: Self-Defeating Over-Appropriation as a Coordination Failure in Agentic LLM Collectives](https://mlsanity.com/paper/2607.22188/) (2026) - [CAMO: An Agentic Framework for Automated Causal Discovery from Micro Behaviors to Macro Emergence in LLM Agent Simulations](https://mlsanity.com/paper/2604.14691/) (2026) - [Is Lying an Emergent Behaviour in LLMs? Evidence from Gaslighting AI agents in a Sustainability Game](https://mlsanity.com/paper/2606.28456/) (2026) - [From Certain Doom to Survival: Agent-Driven Self-Governance in LLM Agent Societies](https://mlsanity.com/paper/2609.22600/) (2026) - [Who Owns That? Evaluating Ownership Intuitions in Large Language Models](https://mlsanity.com/paper/2609.39483/) (2026) ## Abstract (arXiv) A growing body of multi-agent studies with LLMs explores how norms and cooperation emerge in mixed-motive scenarios, where pursuing individual gain can undermine the collective good. While prior work has explored these dynamics in both richly contextualized simulations and simplified game-theoretic environments, most LLM systems featuring common-pool resource (CPR) games provide agents with explicit reward functions directly tied to their actions. In contrast, human cooperation often emerges without explicit knowledge of the payoff structure or how individual actions translate into long-run outcomes, relying instead on heuristics, communication, and enforcement. We introduce a CPR simulation framework that removes explicit reward signals and embeds cultural-evolutionary mechanisms: social learning (adopting strategies and beliefs from successful peers) and norm-based punishment, grounded in Ostrom's principles of resource governance. Agents also individually learn from the consequences of harvesting, monitoring, and punishing via environmental feedback, enabling norms to emerge endogenously. We establish the validity of our simulation by reproducing key findings from existing studies on human behavior. Building on this, we examine norm evolution across a 2×2 grid of environmental and social initialisations (resource-rich vs. resource-scarce; altruistic vs. selfish) and benchmark how agentic societies comprised of different LLMs perform under these conditions. Our results reveal systematic model differences in sustaining cooperation and norm formation, positioning the framework as a rigorous testbed for studying emergent norms in mixed-motive LLM societies. Such analysis can inform the design of AI systems deployed in social and organizational contexts, where alignment with cooperative norms is critical for stability, fairness, and effective governance of AI-mediated environments.