Stochastic design of an early warning system
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abstract
Early warning systems (EWS) are monitoring devices designed to avoid or to mitigate the impact posed by a threat. Since EWS are time-sensitive or stochastic, it is necessary to develop a design methodology that defines the integration of the participating monitoring information sources, the identification of potential warning thresholds, and the assessment of the associated risk within an explicit causal analysis. This paper proposes a framework for a stochastic design of an early warning system, introducing a risk measure as the reference variable that encapsulates the different effects retrieved by the monitoring instruments. Within a decision-making framework the risk measure serves as the index for defining the system warning thresholds. A Bayesian approach is proposed as a suitable tool for integrating and updating the joint states of information and the warning levels as the information flows through the warning system.
