A Model to Support Risk Management Decision-Making

This paper considers the issue of designing a framework to efficiently manage the risk due to some adverse events an organization or a system may face. Risk comes from human being's incapacity to predict the consequences or outcomes of some external events and/or their own actions, or to express precisely their knowledge about things. Thus, risk is linked to uncertainties that are inherent to almost all activities of human being. Designing an effective risk management decision making framework necessitate to correctly address these uncertainties in terms of appropriate mathematical tools along with procedures to identify variables (risk factors, state of the system, consequences, objectives or stakes, possible actions, etc.) impacting decision process and relationships linking them and finally aggregating approaches to present high level managers with concise information. In this paper we will use a meta-matrix analysis to identify relationships between previously determined variables, Bayesian networks and influence diagrams, graphical tools that permit easy representation of probabilistic relationships (independence, causality, correlation, etc.) between variables to quantify these relationships, and Choquet integral as an aggregation tool.