Abstract
Resilience is the ability of a system to respond, absorb, adapt, and recover from a disruptive event. Dozens of metrics to quantify resilience have been proposed in the literature. However, fewer studies have proposed models to predict these metrics or the time at which a system will be restored to its nominal performance level after experiencing degradation. This paper presents two alternative approaches to model and predict performance and resilience metrics with techniques from reliability engineering, including (i) bathtub-shaped hazard functions and (ii) mixture distributions . Given their ease of accessibility, historical data sets on job losses during recessions in the United States are used to assess the predictive accuracy of these approaches. Goodness of fit measures and confidence interval are computed to assess how well the models perform on the data sets considered. The results suggest that both approaches can produce accurate predictions for data sets exhibiting V and U shaped curves, but that L and W shaped curves that respectively experience a sudden drop in performance or deviate from the assumption of a single decrease and subsequent increase cannot be characterized well by either class of model proposed, necessitating additional modeling efforts that can capture these more general scenarios.