This issuePrevious ArticleRisk in system of systems engineering and managementNext ArticleMultiple criteria intelligence tracking for detecting extremes from sequences of risk incidents
Interdependency analysis with multiple probabilistic sector inputs
The increasing degree of interdependencies among sectors of the
economy can likely make the impacts of natural and human-caused
disruptive events more pronounced and far-reaching than before. An
extended input-output model is implemented in this paper to analyze
risk scenarios to a particular sector and to estimate the resulting
ripple effects to other sectors. The proposed extension is capable
of combining likelihood and consequence estimates from multiple
experts, which incorporates traditional expected value and
extreme-event measures of risk. The probability densities of ripple
effects are generated via Monte Carlo simulation; hence, providing
estimates of the mean and extreme values of economic losses and
corresponding levels of sector disruptions. In investing for
additional airline security, for example, the breakeven level of
investment cost should be optimized with respect to both average and
worst-case consequences. Ultimately, the ranking of the sectors that
are most critically affected by a given disruptive event can provide
guidance in identifying resource allocation and other risk
management strategies to minimize the overall impact on the economy.
The methodology is demonstrated through an air transportation sector
case study.