Societal Risk and Resilience Analysis: Dynamic Bayesian Network Formulation of a Capability Approach

Tabandeh, Armin; Gardoni, Paolo; Murphy, Colleen; Myers, Natalie R. D.

doi:10.1061/ajrua6.0000996

Cited by 23 publications

(6 citation statements)

References 21 publications

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“…If costs were not implicitly considered, i.e., there was no tradeoff, there would be no reason to tolerate any disruption. Gardoni and Murphy (2010) and Tabandeh et al (2018Tabandeh et al ( , 2019) offer a capability-based approach to describing societal impacts of disasters. Indicators represent the capabilities, which capture distinct dimensions of an individual's well-being, including for example, meeting physiological needs, earning income, being mobile, and being socially connected (Tabandeh et al 2019).…”

Section: Societal Impact Literaturementioning

confidence: 99%

Household impacts of interruption to electric power and water services

et al. 2022

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Critical infrastructure systems derive their importance from the societal needs they help meet. Yet the relationship between infrastructure system functioning and societal functioning is not well-understood, nor are the impacts of infrastructure system disruptions on consumers. We develop two empirical measures of societal impacts-willingness to pay (WTP) to avoid service interruptions and a constructed scale of unhappiness, compare them to each other and others from the literature, and use them to examine household impacts of service interruptions. Focusing on household-level societal impacts of electric power and water service interruptions, we use survey-based data from Los Angeles County, USA to fit a random effects within-between model of WTP and an ordinal logit with mixed effects to predict unhappiness, both as a function of infrastructure type, outage duration, and household attributes. Results suggest household impact increases nonlinearly with outage duration, and the impact of electric power disruptions are greater than water supply disruptions. Unhappiness is better able to distinguish the effects of shorter-duration outages than WTP is. Some people experience at least some duration of outage without negative impact. Increased household impact was also associated with using electricity for medical devices or water for work or business, perceived likelihood of an emergency, worry about an emergency, past negative experiences with emergencies, lower level of preparation, less connection to the neighborhood, higher income, being married, being younger, having pets, and having someone with a medical condition in the house. Financial, time/effort, health, and stress concerns all substantially influence the stated level of unhappiness.

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Section: Societal Impact Literaturementioning

confidence: 99%

Household impacts of interruption to electric power and water services

et al. 2022

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“…These models typically characterize the relationship between different variables of the system and how they evolve over time. For example, Tabandeh et al (2019) and Kammouh et al (2020) proposed dynamical bayesian networks to predict the impact of negative events on engineering systems and evaluate their resilience and recovery times. The work in Zhao et al (2017) used hidden Markov models to quantify resilience in infrastructure systems considering adaptive, absorptive, and recovery capacity when the system in under disruptive scenarios.…”

Section: Probabilistic Dynamical Modelingmentioning

confidence: 99%

Computational models of community resilience

et al. 2021

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Protecting civil infrastructure from natural and man-made hazards is vital. Understanding the impact of these hazards helps allocate resources efficiently. Researchers have recently proposed static and dynamic computational models for community resilience analyses to evaluate a community's ability to recover after a disruptive event. Yet, these frameworks still need to adequately address community interdependencies and consider the impact of decision-making in modeling. This paper presents a state-of-the-art review of computational methods to model community resilience, focusing on the last ten years. It addresses critical terminology, community interdependencies, and current resilience guides within community resilience comprehension and discusses static and dynamic computational models, including probabilistic modeling in uncertain environments, rating models for community resilience assessment, 2 optimization-based modeling for resilient community design, game theory, agent-based, and probabilistic dynamical modeling. This paper presents key findings of promising research for future directions in the community resilience field.

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“…Rather than the static approaches, Tabandeh et al (2018) developed an indicator-based probabilistic formulation to model the societal impact and estimate the impact considering the immediate consequences and the recovery condition. The methodology uses DBN to integrate the predictive model of the indicators.…”

Section: Introductionmentioning

confidence: 99%