Gardado en:
| Autor Principal: | |
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| Formato: | Recurso digital |
| Idioma: | inglés |
| Publicado: |
Zenodo
2026
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| Subjects: | |
| Acceso en liña: | https://doi.org/10.5281/zenodo.19081596 |
| Tags: |
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Table of Contents:
- <p>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.</p> <p>The framework introduces <em>decision load</em> 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).</p> <p>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.</p> <p>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.</p> <p>The results suggest that the ability to manage decision load may constitute a fundamental dimension of systemic resilience in complex governance environments.</p>