Reducing Systemic Risks in Humanitarian Aid Work with Guided Network Topology

Humanitarian aid works are critical for emergency response, damage mitigation, initial recovery and comprehensive rehabilitation following disasters. These efforts involve various aid actors with diverse mandates, capacities and expertise. Their interactions give rise to a network of aid actors. Effective coordination and communication among these actors are essential but challenging due to systemic risks such as random failures, extended communication paths, redundant connectivity, irrelevant connectivity and poor navigability. Reducing these systemic risks is crucial for the success of humanitarian aid workflows. This research aims to address these challenges by proposing an algorithm that proactively generates a resilient network of aid actors. The algorithm focuses on creating a network that is scale-free, exhibits small-world effect, is robust against random failures and maintains lower structural entropy. Using the SIDR-2007 humanitarian aid dataset, our experiments demonstrate that the proposed aid actors’ network enhances efficiency in navigability, coordination, communication and control. These findings suggest that understanding and optimizing network topology can significantly mitigate systemic risks and improve the performance of humanitarian aid efforts.