The Politician, the Liar, and the Obedient Worker: Emerging Behavior of LLM Agents in Hierarchical Games
On the map: LLM agents and tool use → Social simulation and multi-agent debate
Abstract (arXiv)
LLMs are rapidly embedding themselves into daily life: drafting our emails, managing our schedules, and making decisions on our behalf. As they move from individual tools to participants in multi-agent organizations, an important question arises: do they reproduce the governance failures like free-riding, corruption, and entrenched leadership that plague human institutions? We introduce the Hierarchical Game (HG), a public goods game extended with managerial authority, democratic elections, and private communication. Testing six frontier models across twelve experiments that add institutions one at a time (speech, peers, government, wages, oversight, elections), we find distinct behavioral profiles: Qwen promises and lies (13.3% broken promises); Grok refuses to cooperate on its own but becomes fully cooperative once a manager can punish it (16%→100%); Claude and GPT-4o cooperate reliably at baseline. But honesty proves fragile. When the manager role comes with a salary, all models except GPT-4o start cutting private deals to win or keep the position. When punishment is made anonymous, honest models begin to cheat. When all agents share the same model family, the first elected manager stays in power indefinitely. Leadership change only happens in groups that mix different families.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 12 of its 35 references that are in the map. First posted on arXiv 10 Aug 2026.
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12 of 35 references.
Closest ancestors · 3
Same direction, newest first
- 2025Understanding LLM Agent Behaviours via Game Theory: Strategy Recognition, Biases and Multi-Agent Dynamics8 Dec
- 2024Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents25 Apr
- 2023Generative Agents: Interactive Simulacra of Human Behavior7 Apr
Other references · 9
- 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
- 2022Out of One, Many: Using Language Models to Simulate Human Samplescited by 737
- 2023AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviorscited by 580
- 2022Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studiescited by 506
- 2023Large Language Models can Strategically Deceive their Users when Put Under Pressurecited by 132
- 2023Strategic Behavior of Large Language Models: Game Structure vs. Contextual Framingcited by 49
- 2023Hoodwinked: Deception and Cooperation in a Text-Based Game for Language Modelscited by 46
- 2024LLM economicus? Mapping the Behavioral Biases of LLMs via Utility Theorycited by 44
- 2025When Do Large Language Models Exhibit Unsolicited Deception?cited by 9
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- 2026Be Careful Who You Trust: Coordination Dynamics under Corrupted Communication in LLM Multi-Agent Games
- 2026When Agents Lie: Premeditation, Persistence, and Exploitation in Repeated Games
- 2025Corrupted by Reasoning: Reasoning Language Models Become Free-Riders in Public Goods Games
- 2026Is Lying an Emergent Behaviour in LLMs? Evidence from Gaslighting AI agents in a Sustainability Game
- 2026Voluntary Collusion with Secret Tools in Competing LLM Agents
- 2026Everyone Conforms, No One Believes: Pluralistic Ignorance in LLM Agent Populations
- 2026Trust, Lies, and Long Memories: Emergent Social Dynamics and Reputation in Multi-Round Avalon with LLM Agents
- 2025neuralFOMO: Can LLMs Handle Being Second Best? Measuring Envy-Like Preferences in Multi-Agent Settings