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Weight Subjective
Swing Weighting - importance weights derived from worst-to-best swing utility gains
Weight_Subjective (swing from worst to best, relative gain assessment)
von Winterfeldt, D., Edwards, W.1986
Overview
Swing weighting asks the DM: 'Starting from a baseline where all attributes are at their worst level, how much would you gain by swinging each attribute from worst to best?' The attribute with the highest swing gain gets the highest weight. It is closely related to SMART but grounds the ratings in an explicit comparative context.
- Output
- Weight, higher is better
- Data
- Crisp, expert input required
- Weights
- Derived internally, no weight source needed
- Size
- 2+ alternatives, 3-12 criteria works best
- Used for
- Expert-driven decision making, MAGDM
How it works
- 1
Normalise swing scores: w_j = s_j / Σ_k s_k.
von Winterfeldt & Edwards 1986, Ch.8 p.272 (swing weighting; pending PDF page verification)
Fits when / Look elsewhere when
Fits when
- •Native group-decision support (multi-DM aggregation built into the pipeline)
Look elsewhere when
- •No experts available. Use objective weighting.
- •High inconsistency. Discard and re-elicit.
Assumptions to verify
- Domain experts available
- Experts can express consistent comparisons
Edge cases and pitfalls
Swing scores must reflect importance within the specific range [worst, best] chosen for each criterion. Changing the range changes the weights - define ranges carefully.
Works with
Commonly takes its weights from
Its derived weights can feed
How to cite
von Winterfeldt, D.; Edwards, W. (1986). Decision Analysis and Behavioral Research. Cambridge University Press.
System ID, as it appears in reports and the API
SWING