Real-world MAS datasets
Statistical hypothesis tests
Each phase gates the next — formal proofs constrain simulation space; simulations generate validation hypotheses.
FINDING 1
Convergence Theorem
N = agent count, α = interaction rate, t = elapsed time
EMPIRICAL EVIDENCE
N=100
N=500
N=1000
Convergence probability approaches 1 exponentially as N×t grows.
FINDING 2
Phase Transition Boundary
Critical density threshold ρ_c below which collective intelligence fails to emerge. Above ρ_c, local interaction rules spontaneously produce global coordination.
THEORETICAL vs. EXPERIMENTAL VALUES
N=200
Theory
Exp.
N=500
Theory
N=1000
Theory
Theoretical
Experimental
Comparative Analysis
Method Convergence Scalability Robustness
Centralized MAS (baseline) O(N²) comms Poor High
Gossip protocols O(N log N) Medium Medium
Our framework (ECI-DMA) O(N log N) Excellent High ✓
Swarm baseline (ACO) O(N²) Medium Low
Our framework achieves superior scalability while maintaining convergence guarantees
Contributions
1
Formal convergence framework for decentralized multi-agent systems
2
Phase transition analysis of emergent behavior
3
Open-source simulation toolkit (AgentForge)
PUBLISHED
Decentralized systems can achieve collective intelligence through simple local interaction rules