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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsIf a weighted decision matrix produces different scores or rankings, first check whether the two runs used the same options, evidence, scoring scales, weights, normalization method, and calculation rules. Differences can come from changed inputs or assumptions, inconsistent treatment of data, or an arithmetic error. If the inputs and calculations match, test plausible changes to see whether the ranking is genuinely sensitive to the assumptions.
This guide focuses on weighted decision matrices and multi-criteria decision analysis (MCDA). The checks can help with other models, but their precise causes and remedies depend on how those models work.
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What “inconsistent scores” can mean
Start by identifying what changed: a score for one option on one criterion, the total score, or only the ranking. These are different symptoms. A changed cell points first to the evidence or scoring definition; a changed total points to weights, aggregation, or arithmetic; and a changed rank may occur even when totals move only slightly, particularly when options are close.
In a basic weighted matrix, each option is assessed against criteria, each criterion has a weight, and each score is multiplied by its weight before the contributions are added. The result depends on both the judgments and the calculation. MCDA guidance describes the output as a preference ranking based on performance assessments and trade-offs, rather than a score that stands independently of its assumptions (HM Treasury’s MCDA guidance).
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Troubleshoot the model in a reproducible order
1. Reproduce the comparison
- Confirm that both runs include the same alternatives and criteria.
- Check the model version, method, and any settings or rules that affect scoring.
- Pinpoint whether the discrepancy is in a criterion score, a weighted contribution, a total, or the final rank.
Compare the underlying cells, not just the displayed totals. A changed alternative or criterion can alter the comparison even when the remaining entries are identical.
2. Audit evidence and scoring definitions
For every option–criterion cell, compare the evidence used and how it was translated into a score. Check for changed source data, inconsistent units, missing values, different imputation or transformation choices, and reversed scale direction—for example, whether a higher value is better or worse. The Government Analysis Function’s MCDA guide recommends using subject-matter-expert judgment when objective evidence is unavailable and documenting performance in a matrix.
A score is meaningful only in relation to its criterion and scale. Define what each scale value means in operational, repeatable terms; otherwise, two people—or two runs—may use the same number to represent different judgments. NASA emphasizes operational scale definitions and understanding how tools normalize criteria measured in different units (NASA’s decision analysis guidance).
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When people supplied the scores, inspect disagreements and outliers before averaging them. A simple rating can blur an option’s measured performance with how strongly someone prefers that performance. If participants interpret a criterion differently, clarify its meaning before aggregating their judgments.
3. Check weights and normalization
Verify that the weights match, use the same basis, and are attached to the intended criteria. If criteria are hierarchical, confirm that local and global weights have been handled consistently. The Government Analysis Function warns that MCDA outputs are sensitive to weights, so they need to be determined validly (MCDA guide).
Ask how each weight was chosen. Swing weighting considers the value of moving a criterion from its worst to its best performance; it is not simply a measure of how important the criterion sounds by name. If criteria use different units, also verify that both runs use the same normalization method and scale definitions. Normalization puts values on a common basis for mathematical operations, but it does not remove the need for defensible scales.
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4. Recalculate the score path
For a simple weighted-sum model, independently multiply each criterion score by its corresponding weight and add the weighted contributions for each option. Then compare that hand-check with the spreadsheet or software output. Check for:
- Omitted cells or formulas that point to the wrong row or column.
- Text values or blank cells treated differently from numeric values.
- Weights applied to the wrong criteria or at the wrong level.
- Rounding performed at different stages.
- A mismatch between the weight total and the model’s normalization convention.
Do not assume that matching displayed totals proves the calculation is identical: rounding can conceal differences in underlying values. Document the methods and assumptions used so another person can reproduce the result, as NASA advises (NASA’s decision analysis guidance).
5. Test which assumptions can change the result
Once obvious errors are ruled out, vary plausible uncertain inputs. Begin with one-at-a-time changes to contested scores or weights and note whether the preferred option changes. If several uncertainties could plausibly occur together, test credible combinations as well. The European Commission’s sensitivity-analysis guidance discusses examining how methodological choices and assumptions affect results (Step 8: Sensitivity analysis).
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Focus on rank stability, not just decimal movement. Sensitivity analysis asks how much the outcome responds to changes in factors affecting it; it is distinct from propagating uncertainty through the model to estimate overall output uncertainty. AHRQ’s guidance distinguishes these questions and recommends interpreting results in light of model structure, data, and current understanding (AHRQ modeling and simulation guidance).
For illustration—not as an empirical finding—the UK government’s guidance gives an example in which revising weights from 25%, 50%, 10%, and 15% to 20%, 60%, 10%, and 10% changes the top-ranked option from A to C. It recommends stress-testing where appropriate (HM Treasury’s illustrative MCDA example).
6. Report the result honestly
If an error is found, correct it and record what changed. If the arithmetic is sound but plausible assumptions can change the ranking, report which assumptions drive the switch and that the result is sensitive or inconclusive; do not force a single winner from an unstable ordering. NASA recommends assessing whether uncertainty could credibly alter the ranking and considering uncertainty reduction when it would matter to the decision. Close alternatives may need to be presented together for final selection (NASA’s decision analysis guidance).
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For consequential or technically complex choices, involve people competent in MCDA facilitation and document the evidence, assumptions, and limitations. A transparent explanation of why the model favors an option is more useful than presenting its score as certainty.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Distinguish a calculation defect from a sensitive result
| What to compare | What a difference may indicate | What to check |
|---|---|---|
| Inputs and evidence | Data or judgment changed between runs | Source values, missing-data treatment, units, and cell-level scores |
| Model method | Structure, settings, or aggregation rules changed | Version, criteria, formula, normalization, and aggregation |
| Scales and weights | Performance meanings or trade-offs differ | Scale direction and definitions, weight basis, and hierarchy |
| Arithmetic | Implementation or spreadsheet error | Cell references, formula coverage, rounding, and independent recomputation |
| Ranking under plausible changes | The result may be genuinely sensitive | Whether reasonable input or weight variations change the preferred option |
Sources and scope
The troubleshooting steps apply to weighted decision matrices and MCDA, where scores, criteria, weights, and aggregation can be inspected. They are not universal diagnostics for every probabilistic, machine-learning, or vendor-specific model; for those, use checks suited to the model’s inputs, design, and implementation.
Quick Recap
- Project Management Institute: Quantitative decision-making methods
- HM Treasury: Use of MCDA in options appraisal
- Government Analysis Function: Introductory guide to MCDA
- NASA: Decision analysis
- AHRQ: Modeling and simulation guidance
- European Commission: Sensitivity analysis
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