Emergent Governance in Self-Organizing LLM Civilizations

Overview • Key Innovations • Quick Start • Experiments • Citation

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Overview

Can LLM agent societies develop complex social structures—wealth inequality, dynasties, and governance—through emergent dynamics alone?

This project demonstrates that populations of LLM agents, when given economic incentives and governance mechanisms, spontaneously develop:

• Dynasties: Family lineages that persist across generations
• Inequality: Wealth concentration (Gini > 0.4)
• Governance: Agents propose and vote on their own rules

Generation 0              Generation 100              Generation 500
┌─────────────┐          ┌─────────────┐          ┌─────────────────┐
│ 100 identical│   ───→   │ Specialized │   ───→   │ Complex Society │
│   agents     │          │   dynasties │          │   with laws     │
└─────────────┘          └─────────────┘          └─────────────────┘
     Equal wealth           Wealth diverges          Emergent governance

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Key Innovations

1. Economic Dynamics

@dataclass
class CivilizationAgent:
    system_prompt: str      # Evolvable role
    wealth: float = 100.0   # Economic resource
    parent_id: str = None   # Lineage tracking

    def can_reproduce(self) -> bool:
        return self.wealth >= REPRODUCTION_COST  # e.g., 200

    def is_alive(self) -> bool:
        return self.wealth > 0  # Death when bankrupt

2. Dynasty Formation

Successful agents spawn offspring with inherited (mutated) prompts:

Founder (Gen 0)
└── Child 1 (Gen 1)
    ├── Grandchild 1 (Gen 2)
    └── Grandchild 2 (Gen 2)
        └── Great-grandchild (Gen 3)

3. Emergent Governance

Agents propose and vote on rules—we don't program specific rules:

Category	Example Emergent Rule
Taxation	"Agents with wealth > 500 pay 10% to common pool"
Meritocracy	"Only top performers can reproduce"
Welfare	"Minimum wealth guarantee of 20"
Oligarchy	"High-wealth agents get double votes"

async def propose_rule(agent, society_state):
    """Agent proposes a rule based on their position and values"""
    proposal = await llm.generate(f"""
    You are {agent.id} with wealth {agent.wealth}.
    Society Gini: {society_state.gini}

    Propose ONE rule that would benefit you or society.
    """)
    return parse_rule(proposal)

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Quick Start

Installation

git clone https://github.com/HowardLiYH/Emergent-Civilizations.git
cd Emergent-Civilizations
pip install -r requirements.txt
export OPENAI_API_KEY="your-key"

Run Dynasty Experiment

from src.genesis import CivilizationSimulation

sim = CivilizationSimulation(
    n_agents=100,
    n_generations=300,
    reproduction_enabled=True,
    governance_enabled=True,
    voting_system="equal"
)

results = await sim.run()

print(f"Final Gini: {results.gini:.3f}")
print(f"Active dynasties: {results.n_active_dynasties}")
print(f"Passed rules: {results.n_passed_rules}")

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Experiments

Experiment Suite

#	Experiment	Question	Scale
1	Dynasty Formation	Do lineages dominate?	50 agents, 200 gen
2	Inequality Emergence	Does wealth concentrate?	50 agents, 200 gen
3	Governance Emergence	What rules emerge?	100 agents, 300 gen
4	Voting Comparison	How does voting affect outcomes?	3 × 100 agents
5	Parallel Civilizations	Do initial conditions matter?	10 × 100 agents
6	Scale Test	What emerges at 1000 agents?	1000 agents, 500 gen

Parallel Civilizations Experiment

Run 10 societies with different initial conditions:

Society	Variation	Expected Outcome
A	Equal start	Baseline
B	Unequal start	Faster concentration
C	High reproduction cost	Fewer dynasties
D	Low reproduction cost	Population explosion
E	Frequent governance	More rules
F	No governance	Pure economy
G	8 task types	More specialists
H	2 task types	Less diversity
I	200 agents	Larger society
J	16 agents	Smaller society

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Project Structure

emergent_civilizations/
├── src/genesis/
│   ├── civilization_agent.py  # Extended agent with wealth, lineage
│   ├── reproduction.py        # Offspring creation with inheritance
│   ├── death.py               # Death/extinction mechanics
│   ├── dynasty.py             # Dynasty tracking and analysis
│   ├── governance.py          # Rule proposals, voting, enforcement
│   ├── society_metrics.py     # Gini, governance entropy, mobility
│   └── civilization.py        # Main orchestrator
├── experiments/
│   ├── exp_dynasty_formation.py
│   ├── exp_governance_emergence.py
│   └── exp_parallel_civilizations.py
├── results/
└── paper/

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Metrics

Society-Level Metrics

Metric	Formula	Interpretation
Gini	Wealth inequality	0 = equal, 1 = one has all
Governance Entropy	Rule diversity	High = diverse laws
Dynasty Concentration	Top 3 dynasties' share	Measures oligarchy
Social Mobility	Rank changes over time	Measures fluidity

Expected Results

Metric	Target
Gini at convergence	> 0.4
Top 3 dynasties control	> 50% of population
Distinct rule categories	> 5
Cross-society variance	Significant

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Theoretical Foundation

This work bridges multiple disciplines:

Field	Connection
Evolutionary Biology	Selection, inheritance, extinction
Economics	Wealth dynamics, inequality emergence
Political Science	Governance emergence, voting systems
Sociology	Social stratification, class formation
AI Safety	Emergent behavior in agent populations

Key Insight

Social structures emerge not from top-down design, but from the interaction of simple rules: compete, accumulate, reproduce, vote. The complexity is emergent.

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Related Projects

Project	Relationship
Emergent-Specialization	Paper 1: Rule-based agents (foundation)
Emergent-Prompt-Evolution	Paper 2: LLM prompt evolution (prerequisite)

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Citation

@article{emergent_civilizations_2027,
  title={Emergent Governance in Self-Organizing LLM Civilizations},
  author={Li, Yuhao and others},
  journal={Nature Machine Intelligence},
  year={2027}
}

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License

MIT License - See LICENSE for details.

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Part of the Emergent Specialization Research Series
Paper 3 of 3