Systems Resilience for Multihazard Environments: Definition, Metrics, and Valuation for Decision Making

Abstract

The United Nations Office for Disaster Risk Reduction reported that the 2011 natural disasters, including the earthquake and tsunami that struck Japan, resulted in $366 billion in direct damages and 29,782 fatalities worldwide. Storms and floods accounted for up to 70% of the 302 natural disasters worldwide in 2011, with earthquakes producing the greatest number of fatalities. Average annual losses in the United States amount to about $55 billion. Enhancing community and system resilience could lead to massive savings through risk reduction and expeditious recovery. The rational management of such reduction and recovery is facilitated by an appropriate definition of resilience and associated metrics. In this article, a resilience definition is provided that meets a set of requirements with clear relationships to the metrics of the relevant abstract notions of reliability and risk. Those metrics also meet logically consistent requirements drawn from measure theory, and provide a sound basis for the development of effective decision-making tools for multihazard environments. Improving the resiliency of a system to meet target levels requires the examination of system enhancement alternatives in economic terms, within a decision-making framework. Relevant decision analysis methods would typically require the examination of resilience based on its valuation by society at large. The article provides methods for valuation and benefit-cost analysis based on concepts from risk analysis and management.

A Measurable Definition of Resiliency Using Mission Risk" as a Metric"

“…This definition is very similar to the definition proposed by Ayyub [Ayyub, 2013], but it is more explicit in that it specifically calls out the fact that an estimate of resilience depends on the set of disturbances over which it is valid, as well as the fact that it exists within an operational timeframe.…”

“…This last argument is reinforced by the fact that the probabilistic nature of inputs and outputs warrants the consideration of a probabilistic measurement of mission risk [Haimes, 2009] [Holling, 1993]. Ayyub defined resilience in multi-hazard environments as "the persistence under uncertainty of a system's performance in the face of disturbances" [Ayyub, 2013]. Together, Holling and Ayyub indicate the importance of needing multiple contextual attributes to support the development of resilience metrics.…”

Quantification of resilience to water scarcity, a dynamic measure in time and space

“…Vugrin et al (2010) defined resilience as the ability of a system to efficiently reduce both the magnitude and duration of the deviation of system performance from the targeted system performance levels. Ayyub (2014) mentioned resilience as the ability of the system to return to a stable state after a perturbation. Based on the concepts of Bruneau et al (2003), various dynamic measures were reported for assessing the system's ability to bounce back from a failure state, as seen above.…”

Multi-Agent System Design Principles for Resilient Coordination & Control of Future Power Systems

“…First, the design principles described here are based upon static rather than dynamic measures of resilience. Although many authors have identified the need for such dynamic measures, the literature has yet to produce them [7,9,10,32,45,64,76,90,95]. Therefore, it is likely that the design principles described here will be expanded as the system resilience literature develops further.…”