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Tina Comes, Michael Hiete, Niek Wijngaards, & Masja Kempen. (2009). Integrating scenario-based reasoning into multi-criteria decision analysis. In S. J. J. Landgren (Ed.), ISCRAM 2009 – 6th International Conference on Information Systems for Crisis Response and Management: Boundary Spanning Initiatives and New Perspectives. Gothenburg: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Multi-criteria decision analysis (MCDA) is a technique for decision support which aims at providing transparent and coherent support for complex decision situations taking into account subjective preferences of the decision makers. However, MCDA does not foresee an analysis of multiple plausible future developments of a given situation. In contrast, scenario-based reasoning (SBR) is frequently used to assess future developments on the longer term. The ability to discuss multiple plausible future developments provides a rationale for strategic plans and actions. Nevertheless, SBR lacks an in-depth performance evaluation of the considered actions. This paper explores the integration of both techniques that combines their respective strengths as well as their application in environmental crisis management. The proposed methodology is illustrated by an environmental incident example. Future work is to conduct validations on the basis of real-world scenarios by public Dutch and Danish chemical incident crisis management authorities.
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Michael Hiete, & Mirjam Merz. (2009). An indicator framework to assess the vulnerability of industrial sectors against indirect disaster losses. In S. J. J. Landgren (Ed.), ISCRAM 2009 – 6th International Conference on Information Systems for Crisis Response and Management: Boundary Spanning Initiatives and New Perspectives. Gothenburg: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Natural and man-made hazards may affect industrial production sites by both direct losses (due to physical damage to assets and buildings) and indirect losses (production losses). Indirect losses, e.g. from production downtimes, can exceed direct losses multiple times. Thus, the vulnerability of industrial sectors to indirect losses is an important component of risk and its determination is an important part within risk analysis. In this paper a conceptual indicator framework is presented which allows to assess the indirect vulnerability of industrial sectors to different types of disasters in a quantitative manner. The results are useful for information sharing and decision making in crisis management and emergency planning (mitigation measures, business continuity planning), since the developed indicator system helps to take the complex phenomenon of industrial vulnerability and the underlying interdependencies into account. Besides the identification and conceptual motivation of the indicators, methodical aspects such as standardization, weighting and aggregation are addressed.
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Tina Comes, Claudine Conrado, Michael Hiete, Michiel Kamermans, Gregor Pavlin, & Niek Wijngaards. (2010). An intelligent decision support system for decision making under uncertainty in distributed reasoning frameworks. 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: This paper presents an intelligent system facilitating better-informed decision making under severe uncertainty as found in emergency management. The construction of decision-relevant scenarios, being coherent and plausible descriptions of a situation and its future development, is used as a rationale for collecting, organizing, filtering and processing information for decision making. The development of scenarios is geared to assessing decision alternatives, thus avoiding time-consuming analysis and processing of irrelevant information. The scenarios are constructed in a distributed setting allowing for a flexible adaptation of reasoning (principles and processes) to the problem at hand and the information available. This approach ensures that each decision can be founded on a coherent set of scenarios, which was constructed using the best expertise available within a limited timeframe. Our theoretical framework is demonstrated in a distributed decision support system by orchestrating both automated systems and human experts into workflows tailored to each specific problem.
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