Abstract for: Ethical Attractors: Universal Patterns of Cooperation and Fractal Emergence in Complex Adaptive Systems

Complex socio-technical systems—from open-source projects to climate accords—must sustain cooperation even when local incentives reward defection. Traditional governance relies on surveillance and punitive rules that scale poorly and react slowly. We ask whether cooperation can emerge as a structural attractor that is both visible and self-reinforcing, eliminating the need for constant policing. We frame interactions as a potential game on a graph and add an entropy term to couple welfare and diversity, yielding Ψ = Φ + κH. Two universal ascent rules—soft-majority imitation and tabular Q-learning—climb Ψ. An observer network pools noisy local signals into coarse metrics (μ, H) and flags convergence. Analytical proofs cover asynchronous updates; simulations test synchronous regimes. Agent-based lattices, Watts–Strogatz, and Barabási–Albert networks all converge to the same cooperation plateau (μ∞ ≈ 0.82) with a smooth logistic rise (R² ≈ 0.92). Q-learning trajectories overlay the ABM curve (KS = 0.31, p = 0.002) and reproduce the predicted payoff ordering. The theoretical hitting-time bound E[τ] ≤ NΔ e^{βk} matches observed runtimes. Publishing μ/H dashboards turns ethical attractors into real-time “health signals,” enabling rapid, low-cost interventions. Tuning κ via subsidies widens the cooperative basin, aligning with Ostrom’s design principles. Future work will validate on empirical supply-chain data, stress-test with adversarial sensor noise, and scale detection to millions of asynchronous IoT nodes. Claude 3.5 Sonnet was used to help me generate diagrams and visualisations for the simulation