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.