# From Certain Doom to Survival: Agent-Driven Self-Governance in LLM Agent Societies arXiv: https://arxiv.org/abs/2609.22600 ยท HTML: https://mlsanity.com/paper/2609.22600/ ## Key facts - No citing papers in the map yet (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 34 of its 41 references that are in the map. - First posted on arXiv 18 Sep 2026. ## 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, 30 of 34) - [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023) - [Discovering Language Model Behaviors with Model-Written Evaluations](https://mlsanity.com/paper/2212.09251/) (2022) - [Executable Code Actions Elicit Better LLM Agents](https://mlsanity.com/paper/2402.01030/) (2024) - [Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies](https://mlsanity.com/paper/2208.10264/) (2022) - [Multi-agent Reinforcement Learning in Sequential Social Dilemmas](https://mlsanity.com/paper/1702.03037/) (2017) - [Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark](https://mlsanity.com/paper/2304.03279/) (2023) - [Multi-Agent Risks from Advanced AI](https://mlsanity.com/paper/2502.14143/) (2025) - [Generative agent-based modeling with actions grounded in physical, social, or digital space using Concordia](https://mlsanity.com/paper/2312.03664/) (2023) - [Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents](https://mlsanity.com/paper/2404.16698/) (2024) - [Inequity aversion improves cooperation in intertemporal social dilemmas](https://mlsanity.com/paper/1803.08884/) (2018) - [Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback](https://mlsanity.com/paper/2404.10271/) (2024) - [A multi-agent reinforcement learning model of common-pool resource appropriation](https://mlsanity.com/paper/1707.06600/) (2017) - [Corrupted by Reasoning: Reasoning Language Models Become Free-Riders in Public Goods Games](https://mlsanity.com/paper/2506.23276/) (2025) - [CoopEval: Benchmarking Cooperation-Sustaining Mechanisms and LLM Agents in Social Dilemmas](https://mlsanity.com/paper/2604.15267/) (2026) - [When Ethics and Payoffs Diverge: LLM Agents in Morally Charged Social Dilemmas](https://mlsanity.com/paper/2505.19212/) (2025) - [An Electoral Approach to Diversify LLM-based Multi-Agent Collective Decision-Making](https://mlsanity.com/paper/2410.15168/) (2024) - [Generative Value Conflicts Reveal LLM Priorities](https://mlsanity.com/paper/2509.25369/) (2025) - [Societal and technological progress as sewing an ever-growing, ever-changing, patchy, and polychrome quilt](https://mlsanity.com/paper/2505.05197/) (2025) - [The Role of Social Learning and Collective Norm Formation in Fostering Cooperation in LLM Multi-Agent Systems](https://mlsanity.com/paper/2510.14401/) (2025) - [Democratic AI is Possible. The Democracy Levels Framework Shows How It Might Work](https://mlsanity.com/paper/2411.09222/) (2024) - [Societal Alignment Frameworks Can Improve LLM Alignment](https://mlsanity.com/paper/2503.00069/) (2025) - [Reputation as a Solution to Cooperation Collapse in LLM-based MASs](https://mlsanity.com/paper/2505.05029/) (2025) - [Evaluating Collective Behaviour of Hundreds of LLM Agents](https://mlsanity.com/paper/2602.16662/) (2026) - [More Capable, Less Cooperative? When LLMs Fail At Zero-Cost Collaboration](https://mlsanity.com/paper/2604.07821/) (2026) - [Investigating Prosocial Behavior Theory in LLM Agents under Policy-Induced Inequities](https://mlsanity.com/paper/2505.15857/) (2025) - [Full-Stack Alignment: Co-Aligning AI and Institutions with Thick Models of Value](https://mlsanity.com/paper/2512.03399/) (2025) - [Strategies of cooperation and defection in five large language models](https://mlsanity.com/paper/2601.09849/) (2026) - [A Theory of Appropriateness That Accounts for Norms of Rationality](https://mlsanity.com/paper/2603.14050/) (2026) - [Human Values Matter: Investigating How Misalignment Shapes Collective Behaviors in LLM Agent Communities](https://mlsanity.com/paper/2604.05339/) (2026) - [The Subtle Art of Defection: Understanding Uncooperative Behaviors in LLM based Multi-Agent Systems](https://mlsanity.com/paper/2511.15862/) (2025) ## Abstract (arXiv) Multi-agent LLM systems are increasingly evaluated in social dilemmas, but most work treats governance as imposed by the experimenter, expressed rhetorically, or restricted to a fixed menu of mechanisms. We introduce GovSim-SelfGovern, an extension of the GovSim common-pool resource environment in which agents author executable Python governance rules, receive sandbox validation feedback, vote on proposed laws, and live under the rules they enact across rounds. To evaluate agent-driven self-governance, we examine three scenarios ranging from stable abundance to a fatal resource wall where five agents cannot all survive through harvest alone. To solve this, agents must write and debug useful laws in time before their institutions degrade sharply under resource pressure. Finally, we study a central alignment question: when agents hesitate to propose exile, are they rejecting it for normative reasons, or does it never enter their candidate set? Our results show that executable governance improves the space of possible interventions for agents, but survival depends on whether agents discover the right institutional mechanisms in time. Fiscal capacity enables redistribution, while deeper reasoning and removal of democratic veto make exile more feasible. GovSim-SelfGovern therefore adapts executable code actions to a common-pool governance setting and shows how scarcity turns institutional authorship into a political and ethical problem.