Computational models of community resilience

Amanda Melendez, David Caballero-Russi, Mariantonieta Gutierrez Soto () and Luis Felipe Giraldo
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Amanda Melendez: University of Kentucky
David Caballero-Russi: The Pennsylvania State University
Mariantonieta Gutierrez Soto: The Pennsylvania State University
Luis Felipe Giraldo: University of los Andes

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 111, issue 2, No 2, 1152 pages

Abstract: Abstract 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 10 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, 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.

Keywords: Community resilience; Game theory; Agent-based modeling; Computational models; Interdependencies (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s11069-021-05118-5

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