This paper presents a methodology for assessing risk severity based on an Analytic Hierarchy Process (AHP) model perceived as a structure with multiple levels of granularity. The quantification of risk severity is performed using two types of synthesis: top-down and bottom-up. The former one determines risk severity levels experienced by the overall objective due to all risk factors and sub-factors, whereas the latter approach determines severity levels due to individual, selected risk factors and sub-factors. Importantly, this paper attempts to bridge the hierarchical problem solving process using AHP methodology with granular computing in a pragmatic manner. This has been illustrated with an example of constructing a quantitative model for identifying risk factors in the outbreak of cattle diseases.
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