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When a decision model gives different scores or rankings, first check whether the two results used the same alternatives, evidence, scoring definitions, weights, normalization, and calculation method. A difference may reveal an input or formula error—or it may be a legitimate result of changing assumptions in a weighted decision matrix or multi-criteria decision analysis (MCDA). This guide focuses on those models; probabilistic, machine-learning, and vendor-specific models may require different checks.
What “inconsistent scores” can mean
Pin down the difference before investigating it. A criterion-level score may have changed, the total may differ, or the totals may be similar while the rank order changes. Each points to a different part of the model: evidence or scoring, aggregation arithmetic, or the trade-offs that determine the ranking.
In a basic weighted decision matrix, each alternative is assessed against criteria. Each criterion score is multiplied by its weight, and the weighted contributions are summed. A changed input, weight, or calculation can therefore change a total. In MCDA, the final preference ranking also depends on the performance assessments and the trade-offs between criteria. PMI describes the weighted-sum procedure, while HM Treasury’s MCDA guidance explains how assessments and trade-offs shape comparisons.
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1. Reproduce the comparison
Write down exactly what differs: a criterion score, a total, or the preferred option. Then verify that both results use the same alternatives, criteria, model version, and method. A ranking is not a like-for-like comparison if one run includes a different option or criterion, or uses a changed model structure. NASA’s decision-analysis guidance stresses documenting methods and assumptions.
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2. Audit the evidence and scoring definitions
Compare the evidence behind every alternative-by-criterion score. Check that units match, that higher and lower values are interpreted consistently, and that each scale has a clear, repeatable meaning. If a value is missing, find out whether it was omitted, imputed, or transformed—and whether both runs handled it the same way.
When scores come from people rather than direct measurements, clarify what each criterion means before averaging or combining judgments. The Government Analysis Function’s MCDA guide recommends documenting performance in a matrix and using subject-matter-expert judgment when objective evidence is unavailable. It also cautions that simple scoring can be unreliable when participants do not share an understanding of a criterion. Inspect outliers and disagreements rather than treating an average as proof of consensus.
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3. Check weights and normalization
Confirm that the weights are identical, expressed on the same basis, and applied at the correct level when criteria are hierarchical. In a hierarchy, local weights apply within a branch, while global weights reflect a criterion’s contribution to the overall model. The Government Analysis Function guide explains this distinction.
Check how weights were chosen, too. Swing weighting asks how valuable the difference between a criterion’s worst and best performance is; it is not simply a measure of how important the criterion sounds. The guide warns that an MCDA’s overall output is sensitive to weights, so they need to be determined validly.
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If criteria use different units, such as cost and delivery time, check that both runs use the same normalization method. Normalization puts values on a common basis for mathematical operations, but it does not remove the need for sound scales. NASA emphasizes operational definitions that are repeatable and measurable. The European Commission’s sensitivity-analysis guidance also addresses how analytic choices and assumptions can affect composite results.
4. Recalculate the score path
For a simple weighted matrix, calculate each alternative independently: multiply every criterion score by its corresponding weight, then add the contributions. Compare your result with the model’s displayed total. If they disagree, inspect the formulas and references before changing the assumptions.
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- Look for omitted cells, duplicated contributions, or references pointing to the wrong row or column.
- Check for text stored where a number is expected, and for formulas that treat blank cells differently.
- Verify that rounding happens at the same stage in both calculations.
- Confirm that weights follow the method’s convention—for example, whether they are normalized—and that the same aggregation rule is used.
Document the calculation method and assumptions so another person can reproduce the result, as NASA’s guidance recommends.
5. Test which plausible changes affect the ranking
Once the inputs and arithmetic are reproducible, vary uncertain assumptions to see what drives the result. Start with one change at a time, such as a disputed score or a weight, and record whether the preferred option changes. Then test combinations of plausible changes when they could interact.
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The Government Analysis Function guide describes using one-at-a-time changes to identify influential inputs, followed by combinations to assess overall risk. AHRQ distinguishes sensitivity analysis, which examines the influence of inputs, from uncertainty propagation, which considers the overall uncertainty in outputs. Ask, as PMI puts it, “how sensitive is the end result to changes in various factors affecting it?” Focus on whether the preferred option changes—not merely whether a decimal moves.
For example, HM Treasury’s guidance presents an illustrative MCDA in which changing weights from 25%, 50%, 10%, and 15% to 20%, 60%, 10%, and 10% changes the first-ranked option from A to C. These are example values, not a measured rate or general prediction. The useful lesson is to test whether plausible weight changes alter the choice in your own model.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to interpret the result after checks
If the result changes only under implausible assumptions, document why those assumptions are implausible and show the result under the credible range. If reasonable changes can switch the preferred option, report that the ranking is sensitive and identify the scores, weights, or analytic choices responsible. Do not present a fragile ordering as certain.
If you find no calculation error, explain the result in terms of the model’s structure, available evidence, and assumptions. AHRQ’s guidance for modeling and simulation studies recommends interpreting findings in that context. NASA advises considering whether uncertainty could credibly alter the ranking; if it could, reducing uncertainty may help the decision. When alternatives remain close, present them for final selection rather than implying that the model has resolved a distinction it cannot reliably make. For consequential or technically complex decisions, the UK MCDA guidance points to competent facilitation.
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