emergent-role-assignment

name: "emergent-role-assignment" description: "Category: Phase 3 Core - Self-Organization"

Emergent Role Assignment

Category: Phase 3 Core - Self-Organization Status: Skeleton Implementation Dependencies: sheaf-theoretic-coordination, chemical-organization-theory

Overview

Implements spontaneous role assignment in multi-agent systems through self-organization, dynamic hierarchy adaptation, and reward-based emergence without central coordination.

Capabilities

• Spontaneous Hierarchy: Agents self-organize into hierarchical structures
• Dynamic Role Adaptation: Roles change based on task demands
• Reward-Based Emergence: Roles emerge from collective optimization
• Stability Analysis: Verify organizational stability and convergence

Core Components

1. Role Dynamics (role_dynamics.jl)

   • Role state representation
   • Transition dynamics between roles
   • Stability attractors

2. Hierarchy Formation (hierarchy_formation.jl)

   • Emergent leadership via fitness
   • Span of control optimization
   • Dynamic reorganization triggers

3. Reward Shaping (reward_shaping.jl)

   • Collective reward functions
   • Credit assignment without centralization
   • Multi-agent learning objectives

4. Stability Verification (stability_verification.jl)

   • Lyapunov function construction
   • Convergence guarantees
   • Resilience to perturbations

Integration Points

• Input from: sheaf-theoretic-coordination (consensus on roles)
• Output to: chemical-organization-theory (roles as stable organizations)
• Coordinates with: feedforward-learning-local (local learning signals)

Usage

using EmergentRoleAssignment

# Define multi-agent system
agents = [Agent(id=i, capabilities=rand(5)) for i in 1:20]
environment = GridWorld(10, 10)

# Initialize role assignment system
role_system = RoleSystem(
    n_roles=4,
    transition_rates=0.1,
    reward_fn=collective_foraging_reward
)

# Simulate emergence
trajectory = simulate_emergence(role_system, agents, environment, steps=1000)

# Analyze stability
stability = analyze_role_stability(trajectory)
hierarchy = extract_hierarchy(trajectory.final_state)

References

• Bonabeau et al. "Self-Organization in Social Insects" (1997)
• Wolpert & Tumer "Optimal Payoff Functions for Members of Collectives" (1999)
• Tumer & Wolpert "A Survey of Collectives" (2004)

Implementation Status

• Basic role dynamics
• Simple hierarchy formation
• Full reward shaping framework
• Stability verification
• Benchmark on multi-agent tasks