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In systems engineering, the risk matrix is a popular protocol for binning risks into a collection of probability and consequence cells. In its traditional form, the risk matrix produces a ranking of cells according to their position in an ordered list. From this, management can distinguish whether a set of risks collected in one risk matrix cell has a higher priority than a set of risks collected in another cell. However, if decisions require measuring the relative differences between pairs of cells across their rank positions, then it is necessary to map them from their ordinal scale to an interval scale. This paper introduces methods from representational measurement theory to transform a rank ordered list of risk matrix cells into an interval measurement scale. The transformation produces a scored risk matrix. This allows relative differences among cells to be meaningfully compared, which broadens its use in management decisions. A scored risk matrix provides greater insights into the urgency of risks grouped within cells than is possible in a traditional risk matrix, while remaining within its ease and popularity of use.
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