Indicators to compare simulated crisis management strategies

Wolfgang Engelbach, Sandra Frings, Ritta Molarius, Christoph Aubrecht, Merik Meriste, Adriaan Perrels

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The impacts of natural and technological hazards are increasing, and their effects on health and wellbeing are amplified by population growth and economic activities. Moreover, media raise awareness about disasters, sometimes in biased manner, which altogether puts additional pressure on crisis management and requires retraceable justification of decisions during crisis response and for investments during the preparation phase. Planning and training during the phase of emergency preparedness can be made more efficient by the use of modelling and simulation tools. Simulation of crisis evolvement and corresponding response activities can be helpful to estimate, for example, the impact variation of an earthquake or a large accident. Running the same simulation but using different parameters (e.g. input data or decisions) will give insight into the consequences of a decision. By defining clear reference scenarios, a comparison with alternative scenarios is possible. Such a comparison can be visualised by maps or tables, if appropriate with such results also illustrating evolvement over time. Simulation in general is the imitation of the operation of a real-world process or system over time, specifically referring to potential future states (Aubrecht et al. 2008). The act of simulating something first requires that a model needs to be developed. This model represents the key characteristics or behaviours or functions of the selected physical or social system. The model represents the system itself, whereas the simulation represents the operation of the system over time. Key issues in simulation include acquisition of valid source information about the relevant selection of key characteristics and behaviours, the use of simplifying approximations and assumptions within the simulation, and fidelity and validity of the simulation outcomes (Banks 2001, Sokolowski 2009). A high quality simulation requires the appropriate level of abstraction, reliable models and good data quality, which may imply many and complex data for input, calculations and output. These need to be transferred to aggregated values for the user of a simulation and for the decision makers. For this purpose, indicators are relevant that highlight important aspects of complex datasets and allow high-level comparison of alternative scenarios. It needs to be mentioned that in crisis management absolute ranges of "good values" for indicators cannot be given, since that would normally be "zero cost" and "zero affected persons". Everything else is mainly political decisions about less disliked options, which is the case in particular in situations with limited resources to address the problems faced. However, for a given reference scenario, ranking criteria can be provided for a specific decision context, but the need commonly remains to consider several indicators in the context of one decision.
TitelProceedings of the International Disaster and Risk Conference IDRC Davos 2014
PublikationsstatusVeröffentlicht - 2014

Research Field

  • Ehemaliges Research Field - Energy


  • Indicator
  • Simulation
  • Crisis Management
  • Scenario
  • Decision Support


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