If you want to stop a huge crisis, sometimes the best move is for the people in charge to actually give up some of their power.
Simulations of migration systems and financial markets show that the most stable outcomes occur when institutional authority is 'non-increasing' as risk rises. Traditional heavy regulation and 'clamping down' during high-risk periods actually increased the probability of catastrophic swings and slowed down the system's ability to self-correct.
Chaos-Bound Autonomy: A Cross-Domain Simulation Study Of Bounded Governance, Metric Divergence, And Systemic Stability
SSRN · 6453058
Complex socio-technical systems exhibit recurring instability when autonomous agents optimize locally within institutional environments whose feedback loops fail to detect accumulating systemic risk. We introduce and empirically test the Chaos-Bound Autonomy (CBA) framework-a dynamical systems governance architecture that formalizes bounded autonomy as a tuple F = (Ω s , U s , ρ, A, E), wherein system authority is non-increasing in composite risk. Using agent-based simulations calibrated to empi