This article proposes a conceptual framework for ranking the relative gravity of diverse risks. T... more This article proposes a conceptual framework for ranking the relative gravity of diverse risks. This framework identifies the moral considerations that should inform the evaluation and comparison of diverse risks. A common definition of risk includes two dimensions: the probability of occurrence and the associated consequences of a set of hazardous scenarios. This article first expands this definition to include a third dimension: the source of a risk. The source of a risk refers to the agents involved in the creation or maintenance of a risk and captures a central moral concern about risks. Then, a scale of risk is proposed to categorize risks along a multidimensional ranking, based on a comparative evaluation of the consequences, probability, and source of a given risk. A risk is ranked higher on the scale the larger the consequences, the greater the probability, and the more morally culpable the source. The information from the proposed comparative evaluation of risks can inform the selection of priorities for risk mitigation.
and-conditions-of-access.pdf This article may be used for research, teaching and private study pu... more and-conditions-of-access.pdf This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.
Reliability Engineering & System Safety, Jan 22, 2020
Resilience indicators are a convenient tool to assess the resilience of engineering systems. They... more Resilience indicators are a convenient tool to assess the resilience of engineering systems. They are often used in preliminary designs or in the assessment of complex systems. This paper introduces a novel approach to assess the time-dependent resilience of engineering systems using resilience indicators. A Bayesian network (BN) approach is employed to handle the relationships among the indicators. BN is a Directed Acyclic Graph where the nodes represent variables of interest and the links between them indicate causal dependencies. It integrates deterministic, probabilistic, and subjective information within the same model. It also provides probabilistic relationships between the variables, which allows modeling the interdependencies between them. However, the use of BN is limited to static systems that are in a state of equilibrium. Being at equilibrium is often not the case because most engineering systems are dynamic in nature as their performance fluctuates with time, especially after disturbing events (e.g., natural disasters). Therefore, the temporal dimension is tackled in this work using the Dynamic Bayesian Network (DBN). DBN extends the classical BN by adding the time dimension. It permits the interaction among variables at different time steps. It can be used to track the evolution of a system’s performance given an evidence recorded at a previous time step. This allows predicting the resilience state of a system given its initial condition. A mathematical probabilistic framework based on the DBN is developed to model the resilience of dynamic engineering systems. Two case studies are presented in the paper to demonstrate the applicability of the introduced framework. One case study evaluates the resilience of Brazil. The other one evaluates the resilience of a transportation system.
This article proposes a rigorous mathematical approach, named a reliability-based capability appr... more This article proposes a rigorous mathematical approach, named a reliability-based capability approach (RCA), to quantify the societal impact of a hazard. The starting point of the RCA is a capability approach in which capabilities refer to the genuine opportunities open to individuals to achieve valuable doings and beings (such as being mobile and being sheltered) called functionings. Capabilities depend on what individuals have and what they can do with what they have. The article develops probabilistic predictive models that relate the value of each functioning to a set of easily predictable or measurable quantities (regressors) in the aftermath of a hazard. The predicted values of selected functionings for an individual collectively determine the impact of a hazard on his/her state of well-being. The proposed RCA integrates the predictive models of functionings into a system reliability problem to determine the probability that the state of well-being is acceptable, tolerable, or intolerable. Importance measures are defined to quantify the contribution of each functioning to the state of well-being. The information from the importance measures can inform decisions on optimal allocation of limited resources for risk mitigation and management.
This article proposes a conceptual framework for ranking the relative gravity of diverse risks. T... more This article proposes a conceptual framework for ranking the relative gravity of diverse risks. This framework identifies the moral considerations that should inform the evaluation and comparison of diverse risks. A common definition of risk includes two dimensions: the probability of occurrence and the associated consequences of a set of hazardous scenarios. This article first expands this definition to include a third dimension: the source of a risk. The source of a risk refers to the agents involved in the creation or maintenance of a risk and captures a central moral concern about risks. Then, a scale of risk is proposed to categorize risks along a multidimensional ranking, based on a comparative evaluation of the consequences, probability, and source of a given risk. A risk is ranked higher on the scale the larger the consequences, the greater the probability, and the more morally culpable the source. The information from the proposed comparative evaluation of risks can inform the selection of priorities for risk mitigation.
and-conditions-of-access.pdf This article may be used for research, teaching and private study pu... more and-conditions-of-access.pdf This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.
Reliability Engineering & System Safety, Jan 22, 2020
Resilience indicators are a convenient tool to assess the resilience of engineering systems. They... more Resilience indicators are a convenient tool to assess the resilience of engineering systems. They are often used in preliminary designs or in the assessment of complex systems. This paper introduces a novel approach to assess the time-dependent resilience of engineering systems using resilience indicators. A Bayesian network (BN) approach is employed to handle the relationships among the indicators. BN is a Directed Acyclic Graph where the nodes represent variables of interest and the links between them indicate causal dependencies. It integrates deterministic, probabilistic, and subjective information within the same model. It also provides probabilistic relationships between the variables, which allows modeling the interdependencies between them. However, the use of BN is limited to static systems that are in a state of equilibrium. Being at equilibrium is often not the case because most engineering systems are dynamic in nature as their performance fluctuates with time, especially after disturbing events (e.g., natural disasters). Therefore, the temporal dimension is tackled in this work using the Dynamic Bayesian Network (DBN). DBN extends the classical BN by adding the time dimension. It permits the interaction among variables at different time steps. It can be used to track the evolution of a system’s performance given an evidence recorded at a previous time step. This allows predicting the resilience state of a system given its initial condition. A mathematical probabilistic framework based on the DBN is developed to model the resilience of dynamic engineering systems. Two case studies are presented in the paper to demonstrate the applicability of the introduced framework. One case study evaluates the resilience of Brazil. The other one evaluates the resilience of a transportation system.
This article proposes a rigorous mathematical approach, named a reliability-based capability appr... more This article proposes a rigorous mathematical approach, named a reliability-based capability approach (RCA), to quantify the societal impact of a hazard. The starting point of the RCA is a capability approach in which capabilities refer to the genuine opportunities open to individuals to achieve valuable doings and beings (such as being mobile and being sheltered) called functionings. Capabilities depend on what individuals have and what they can do with what they have. The article develops probabilistic predictive models that relate the value of each functioning to a set of easily predictable or measurable quantities (regressors) in the aftermath of a hazard. The predicted values of selected functionings for an individual collectively determine the impact of a hazard on his/her state of well-being. The proposed RCA integrates the predictive models of functionings into a system reliability problem to determine the probability that the state of well-being is acceptable, tolerable, or intolerable. Importance measures are defined to quantify the contribution of each functioning to the state of well-being. The information from the importance measures can inform decisions on optimal allocation of limited resources for risk mitigation and management.
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Papers by Paolo Gardoni