How to Guide and Assess Risk Reduction using Risk Characterization Indicators

Abstract

Problem statement: The risk level predictions in risk assessment often suffer from uncertainties and may, thus, overlook some adverse effects. This problem can be reduced by using risk reduction strategies that continuously guide activities toward lowest possible risk. Approach: This study suggested a method to guide and assess such risk reduction strategies using multi-indicator risk characterization. It was a challenge for the method to secure robustness against unavoidable high uncertainty and to secure flexibility that embraced multiple indicators for different aspects governing the risk level. This methodology was to protect real existing targets, denoted Protection Units (PU), against adverse effects and applied knowledge about all PUs, or a representative fraction of those. A set of risk indicators described different aspects of the risk level for each PU. A scenario in this context contained the set of PUs, each having their risk level described by the set of different risk indicator values. Results: The result was a multi-criterion solution that was analyzed using partial order ranking, where ambiguities between single criteria prediction of risk level as either higher or lower were analyzed and mapped. Conclusion/Recommendations: Risk level hotspots, in which several criteria simultaneously predicted higher risk level for specific PUs, was used as key-elements to provide guidance and assessment of the need for risk reduction and the method was, therefore, called Hotspot ruled ranking (HotsRank).