mlsanityData to 14 Oct 2026

Bosses, Kings, and the Commons: Cooperation Under Power Asymmetry in LLM Societies

arXiv 2605.29062 ↗First posted 27 May 2026cs.CLcited by 1

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Abstract (arXiv)

Communities can sustainably manage shared resources (commons) through self-governance and cooperative norms, a central finding of Ostrom's theory of self-governance. However, real-world commons (e.g., fisheries, forests, and irrigation systems) are often governed under asymmetric power structures, where certain individuals or institutions possess disproportionate control over resource extraction and collective outcomes. As Large Language Models (LLMs) are increasingly explored as agents in synthetic governance simulations, understanding how LLM societies behave under asymmetric power structures is becoming increasingly important, yet existing evaluations largely ignore such asymmetries. We introduce Sovereignty over the Commons Simulation (SovSim), a generative multi-agent simulation framework that incorporates an agent with asymmetric power (boss or king) into a society of symmetric agents (workers or peasants), where all agents extract from a shared resource, collectively determining its sustainability over time. Across eleven state-of-the-art models, we find that introducing asymmetric power leads to severe breakdowns in cooperation and sustainability, with up to an 87.3% degradation in survival rate relative to symmetric settings.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 10 of its 34 references that are in the map. First posted on arXiv 27 May 2026.

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10 of 34 references.

References · 10

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2022Out of One, Many: Using Language Models to Simulate Human Samplescited by 752
  3. 2023Generative agent-based modeling with actions grounded in physical, social, or digital space using Concordiacited by 126
  4. 2024Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agentscited by 125
  5. 2024GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluationscited by 120
  6. 2024Secret Collusion among AI Agents: Multi-Agent Deception via Steganographycited by 113
  7. 2023Generative Language Models Exhibit Social Identity Biasescited by 111
  8. 2023Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiationcited by 96
  9. 2025SimBench: Benchmarking the Ability of Large Language Models to Simulate Human Behaviorscited by 52
  10. 2025Will Systems of LLM Agents Cooperate: An Investigation into a Social Dilemmacited by 25

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