The Dynamics of Governability: Decision Load, Feedback Latency, and the Limits of Continuous Control

This study presents a simulation-based exploration of governability under conditions of persistent, low-intensity disturbance. Rather than focusing on discrete crises, the model conceptualizes governance as a constrained decision-processing system operating under continuous inflow of demands, bounded capacity, and delayed feedback. The framework introduces decision load as a central state variable capturing backlog accumulation, escalation dynamics, and workload exceeding processing capacity. Governance performance is analyzed through a stylized system dynamics model comparing three architectural regimes: high-latency periodic review, unconditional fast intervention, and adaptive low-latency control guided by a governance-load metric (IEKV). Simulation results reveal several structural insights. First, governance degradation emerges endogenously through cumulative decision load rather than exogenous shocks. Second, governance-level congestion indicators precede observable deterioration in welfare outcomes, suggesting their potential role as early warning signals. Third, maximal responsiveness does not guarantee superior performance: unconditional high-frequency intervention generates rising coordination costs and control inefficiencies. In contrast, adaptive architectures that condition responsiveness on system load achieve more stable long-term outcomes by balancing responsiveness and control effort. The model is intentionally minimal and not designed for empirical forecasting. Its contribution is methodological: to demonstrate how feedback latency, decision queues, escalation mechanisms, and control costs jointly shape governability under sustained disturbance. The framework provides a tractable basis for further empirical operationalization using observable administrative indicators such as backlog size, response latency, escalation rates, and coordination complexity. The results suggest that the ability to manage decision load may constitute a fundamental dimension of systemic resilience in complex governance environments.