Abstract
This research utilizes an Object-Oriented Bayesian Network (OOBN) to model the relationships between the Sustainable Development Goal (SDGs) and resilience and sustainability at national, regional, and global levels. The ability of the OOBN to learn the parameters, i.e., the conditional probability distributions between the variables included in the network, was exploited to explore the impacts of progress of SDGs on the sustainability and resilience of nations. The resulting OOBN is used to examine different situations pertinent to policy analysis and design at the times of disasters, particularly in the wake of the COVID-19 pandemic. Three case studies are used to illustrate the step by step process of using the proposed OOBN as well as the expected results of its application in policy analysis and evaluation contexts. The proposed is able to provide insight regarding which SDGs will have more significant impacts on both resilience and sustainability as well as their constituent components. The results of this research indicate how data induced OOBNs can be utilised by policy makers to prioritize new policies and evaluate the impacts of existing policies on both the resilience and sustainability of societies.
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