Mathematical modeling for coping with uncertainty and risk

Coping with uncertainty in decision-making, especially for integrated management of risk, requires the analysis of various measures of outcomes resulting from applying alternative policy options. Policy options include various ex ante measures (such as mitigation, different arrangements for risk spreading) and ex post measures aimed at reducing and sharing losses. The outcomes of implementing a given set of policy measures are typically measured by various indicators such as ex ante and ex post costs, benefits from mitigation measures, welfare, quality of the environment, and indicators of risk exposure (value at risk, insolvency). The amount of data and the complexity of their relationships for any risk management problem are far too great to be analyzed based solely on experience and/or intuition. Therefore, mathematical models have become a key element of decision-making support in various policy processes, especially those aimed at integrated management of disaster risk.

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