It's All in How You Keep the Score

The use of computer-aiding software in public sector decision making has been m-creasmg. There are many multi-criteria decision making (MCDM) models available, like multi-attribute utility technology (MAUT), analytical hierarchy process (AHP), and P/G% Analysis. These programs work quite similarly, and if the same scores and weights are entered, they will yield virtually identical outcomes. In the public sector, key variables are often difficult to measure, so analysts are encouraged to estimate relationships for missing data on a five-point scale. How one enters data for a five-point scale has a dramatic impact on the outcome. Typical approaches to scoring involve an ascending value scale (1→5) or a Likert-type mid-point scale. Both of these yield inconsistent, m-sensitive and unsatisfactory results, since MCDM's are affected mainly by the ratio of the relationship scores for each goal criterion. A descending value scale (5→1) is recommended here to make computer-aided decisions more consistent and sensitive, and therefore, more objective, Keywords: multi-attribute utility technique (MAUT), analytical hierarchy process (AHP), multi-criteria decision making (MCDM), P/G% Analysis, ascendmg value scale, descending value scale, sensitivity analysis, threshold analysis, what if analysis, uncertainty

Analytic hierarchy process · Data mining · Decision analysis · Economics · Hierarchy · Likert scale · Machine learning · Management science · Multiple-criteria decision analysis · Operations research · Point (geometry · Scale (ratio · Statistics · Value (mathematics · Computer Science · Engineering · Law, Economics, and Judicial Systems · Mathematics · Multi-Criteria Decision Making

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