I-Corps: Dynamic Decision Support for Emergency Managers

University Of Pittsburgh, Pittsburgh PA

Investigators

Abstract

The decision support software developed through this project represents a new tool to aid decision makers in making informed, efficient decisions. The software tool builds on earlier research that explored interactions among technical systems, organizational processes, and physical and social conditions that affect information flow in managing risk and uncertainty. The design approach develops practical decision models using Bayesian networks and influence diagrams to assess uncertain conditions, based on systematic identification of interdependencies among the component technical, organizational, and knowledge systems that characterize urgent operating environments. It integrates technical skills in computer programming and simulation design, grasp of business dynamics and marketing, and understanding of context, policies, and constraints of emergency management. This decision support module identifies options available for action, given actual constraints and near-real time information from multiple sources, and calculates the probability of success of each option, based on the collective judgment of experienced emergency managers. This decision support tool addresses problems of scalability and simultaneity in information flow processes that have hindered inter-organizational decision making in large-scale, regional disasters. Modeling potential outcomes can systematically enable managers to compare a broader range of options. If successfully developed, this dynamic decision support tool may have a transformative effect on how communities manage risk. As the number, type, and severity of disasters increase in a global society that depends increasingly on large-scale systems in transportation, power generation, communication, and gas, water and wastewater distribution, the cost and consequences of failure in these socio-technical systems escalate exponentially. Managers may need improved tools to monitor these interdependent operating systems simultaneously, and adjust and adapt the balance between demand and resources available to manage sudden surges in demand from extreme events. This technology has the potential to benefit communities through helping local governments, nonprofit organizations, and small businesses increase their capacity to manage their continuing exposure to risk, but reduce losses by more informed, effective decision making.