Agent-Based Modeling Approach in Understanding Behavior During Disasters: Measuring Response and Rescue in eBayanihan Disaster Management Platform

Abstract

Development of a disaster management system is as complex as the environment it mimics. In 2015, the eBayanihan disaster management platform was launched in Metro Manila, Philippines. It is designed to be an integrated multidimensional and multi-platform system that can be used in managing the flow of information during disaster events. Since its development, usage of the system varies depending on the agent who uses the system and which area is affected by what type of disaster. As a complex problem, behavior of disaster agents, such as official responders, volunteers, regular citizens, is best understood if the system can capture, model, and visualize behavior over time. This study presents the development and implementation of an agent-based approach in understanding disaster response and rescue by automatically capturing agent behavior in the eBayanihan Disaster Management Platform. All user activities are logged and converted into behavior matrices that can be saved and imported into the Organizational Risk Analyzer (ORA) tool. ORA is used to generate the agent-based model which can be viewed in the eBayanihan platform. Actual behavior (ABehM) is compared against perceived (PBM) and expected behavior (EBM) during rescue and response. Results show that EBM networks are fully connected while PBM during rescue and response are granular and vast. Both however show centrality at the provincial and municipal level. ABehM on the other hand shows concentration only at the municipal level with more interactions with ordinary volunteers and citizens.