Leire Labaka, Josune Hernantes, Ana Laugé, & Jose Mari Sarriegi. (2011). Three units of analysis for Crisis Management and Critical Infrastructure Protection. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Society's welfare is very dependent on the effective performance of Critical Infrastructure (CI). Nowadays, CI constitutes a network of interconnected and interdependent entities. This means that a serious event in one CI can originate cascading events in the rest, leading to a serious crisis. As a consequence, Crisis Management (CM) and Critical Infrastructure Protection (CIP) should converge and integrate their findings, providing a more unified approach. One relevant issue when developing integrated CM/CIP research is what type of unit of analysis should be used, as it conditions the research objectives and questions. This paper presents an analysis of three different units of analysis used in CM research, focusing on the research objectives and questions used in them. These three different units of analysis have been used in a European CIP research project where three simulation models have been developed based on these three units of analysis.
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Jennifer Mathieu, Mark Pfaff, Gary L. Klein, Jill L. Drury, Michael Geodecke, John James, et al. (2010). Tactical robust decision-making methodology: Effect of disease spread model fidelity on option awareness. In C. Zobel B. T. S. French (Ed.), ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings. Seattle, WA: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: We demonstrate a method of validating the utility of simpler, more agile models for supporting tactical robust decision making. The key is a focus on the decision space rather than the situation space in decision making under deep uncertainty. Whereas the situation space is characterized by facts about the operational environment, the decision space is characterized by a comparison of the options for action. To visualize the range of options available, we can use computer models to generate the distribution of plausible consequences for each decision option. If we can avoid needless detail in these models, we can save computational time and enable more tactical decision-making, which will in turn contribute to more efficient Information Technology systems. We show how simpler low fidelity, low precision models can be proved to be sufficient to support the decision maker. This is a pioneering application of exploratory modeling to address the human-computer integration requirements of tactical robust decision making.
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