From Certain Doom to Survival: Agent-Driven Self-Governance in LLM Agent Societies
Abstract: 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.