Modeling Fragility in Rapidly Evolving Disaster Response Systems

Assessing the changing dynamic between the demand that is placed upon a community by cumulative exposure to hazards and the capacity of the community to mitigate or respond to that risk represents a central problem in estimating the community's resilience to disaster. This paper presents an initial effort to simulate the dynamic between increasing demand and decreasing capacity in an actual disaster response system to determine the point at which the system fails, or the fragility of the system. Public organizations with legal responsibilities for the protection of human life and property, as well as private organizations responsible for managing utilities, communications, and transportation systems in metropolitan regions, are unable to monitor the interdependent effects of these critical infrastructure systems in real time. Further, they are not able to share information effectively about an emerging threat, nor can they communicate easily among different response organizations at different jurisdictions in a regional event. Modeling the fragility of sociotechnical response systems is critical to enabling metropolitan regions to manage their exposure to risk more efficiently and effectively. To construct a theoretical model of this process, we observe the changing relationship between the demand for assistance and the capacity of the community to provide assistance. We include in our model measures of the magnitude of the disaster, the number of jurisdictions, and a simple type of cooperation to observe how these factors influence the efficiency of disaster operations. Information spreads quickly through inter-organizational or human networks. Stress in organizational performance arises when the amount of information surpasses human capacity to absorb and comprehend it, leading to failure in action. In complex disaster environments, failure in one component of an interdependent system triggers failure in other components, decreasing performance throughout the system and threatening potential collapse. Based on the assessment of disaster operations as a dynamic process among interdependent organizations, we sought to build a computational model of the relationship between demand and capacity in an evolving disaster response system. We developed a simulation platform using Cellular Automata (Epstein et al., 1996; Wolfram, 1994) to describe the pattern of interaction between demand and capacity. To formalize the interaction between organizations and information flow, we used evolving network theory which has been studied in the field of mathematics (Erdos et al., 1960), computer science, and physics (Barabasi et al., 1999; Newman, 2003). We show that different phases of disaster response require different types of information and management skills. The efficiency of disaster response is affected by the initial magnitude of the disaster, the type and amount of resources available, the number of jurisdictions engaged, and the type of response strategies used. The results from the simulation confirm that efficiency has a negative correlation to initial disaster magnitude and a positive correlation to initial capacity. The number of jurisdictions involved in response operations is an independent variable influencing efficiency in disaster response, but the strength and direction of this influence requires further study. Also, sharing resources without specific information to improve coordination appears not to enhance efficiency in disaster response. Finally, we focus not on the amount of information that is available to practicing managers, but on strategies for access to core information that enhance the efficiency of information flow throughout the network of responding organizations. Network theory is used to identify the core information