mlsanityData to 14 Oct 2026

Artificial Leviathan: Exploring Social Evolution of LLM Agents Through the Lens of Hobbesian Social Contract Theory

arXiv 2406.14373 ↗First posted 20 Jun 2024cs.AI cs.CL cs.CYcited by 16

On the map: LLM agents and tool use → Social simulation and multi-agent debate

Abstract (arXiv)

The emergence of Large Language Models (LLMs) and advancements in Artificial Intelligence (AI) offer an opportunity for computational social science research at scale. Building upon prior explorations of LLM agent design, our work introduces a simulated agent society where complex social relationships dynamically form and evolve over time. Agents are imbued with psychological drives and placed in a sandbox survival environment. We conduct an evaluation of the agent society through the lens of Thomas Hobbes's seminal Social Contract Theory (SCT). We analyze whether, as the theory postulates, agents seek to escape a brutish "state of nature" by surrendering rights to an absolute sovereign in exchange for order and security. Our experiments unveil an alignment: Initially, agents engage in unrestrained conflict, mirroring Hobbes's depiction of the state of nature. However, as the simulation progresses, social contracts emerge, leading to the authorization of an absolute sovereign and the establishment of a peaceful commonwealth founded on mutual cooperation. This congruence between our LLM agent society's evolutionary trajectory and Hobbes's theoretical account indicates LLMs' capability to model intricate social dynamics and potentially replicate forces that shape human societies. By enabling such insights into group behavior and emergent societal phenomena, LLM-driven multi-agent simulations, while unable to simulate all the nuances of human behavior, may hold potential for advancing our understanding of social structures, group dynamics, and complex human systems.

Key facts. Cited by 16 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 8 of its 52 references that are in the map. First posted on arXiv 20 Jun 2024.

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8 of 52 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents25 Apr
  2. 2023War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars28 Nov
  3. 2023Generative Agents: Interactive Simulacra of Human Behavior7 Apr

Other references · 5

  1. 2024Large Language Model based Multi-Agents: A Survey of Progress and Challengescited by 769
  2. 2022Using cognitive psychology to understand GPT-3cited by 398
  3. 2023Creating a Large Language Model of a Philosophercited by 21
  4. 2023Synergizing Human-AI Agency: A Guide of 23 Heuristics for Service Co-Creation with LLM-Based Agentscited by 5
  5. 2016Bots as Virtual Confederates: Design and Ethicscited by 2

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Citing papers per year: 2024: 2; 2025: 9; 2026: 5.

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  1. 2025Investigating and Extending Homans' Social Exchange Theory with Large Language Model based Agents
  2. 2024Shall We Team Up: Exploring Spontaneous Cooperation of Competing LLM Agents
  3. 2025Evolution of Cooperation in LLM-Agent Societies: A Preliminary Study Using Different Punishment Strategies
  4. 2025Will Systems of LLM Agents Cooperate: An Investigation into a Social Dilemma
  5. 2024Computational Experiments Meet Large Language Model Based Agents: A Survey and Perspective
  6. 2026Evolving Interpretable Constitutions for Multi-Agent Coordination
  7. 2026TerraLingua: Emergence and Analysis of Open-endedness in LLM Ecologies
  8. 2026AI Agents Alone Are Not (Yet) Sufficient for Social Simulation