At w=0.75, A scores 6.5 and B five.
Priority tradeoffs / Delivery leads, planning owners and acceptance reviewers
Before / afterInspect a ranking when weights change
Show which declared preference weight changes the selected option.
The artifact locates a ranking reversal under two explicit weight policies.
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Original worked case · Manual planning resource
Inspect the decision, not just the summary.
Start with the invented evidence, follow the reasoning, and retain its limits when adapting the brief.
01 · Inspect the inputs
Every record stays visible
| Option | Outcome-fit preference points | Implementation-ease preference points | Score at w=0.75 | Score at w=0.25 |
|---|---|---|---|---|
| A | 8 | 2 | 6.5 | 3.5 |
| B | 4 | 8 | 5 | 7 |
Use horizontal scrolling for wide tables. These records are invented, not customer data.
02 · Follow the reasoning
How the case leads to a decision
A score=8w+2(1−w)=2+6w; B=4w+8(1−w)=8−4w. They tie at w=0.6; the two displayed policies sit on opposite sides.
The bounded result
A wins at a 0.75 first-dimension weight, while B wins at 0.25. The choice depends on the declared preference policy; the score is not an objective business-value measurement.
Definitions and method context
Records, decisions and policies are original synthetic examples. External references supply context; they do not validate these cases or TeamBoostAI capabilities.
Original worked-case definitions
Definitions, policy choices, records and calculations are authored for this worksheet. No external standard, statistical validation or live product measurement is claimed.
External references checked 6 October 2026. Demand for these topics has not been measured.
Compare the decision quality
Illustrative before / afterSynthetic planning case: A local decision uses two declared preference dimensions: outcome fit and implementation ease. Option A has points eight and two; B has four and eight. The team is debating the weight of the first dimension.
At w=0.25, A scores 3.5 and B seven; the policy changes the winner.
Name both preference dimensions
Owner role · Decision owner
Evidence: The invented points and commensurate scoring convention are declared.
Inspect the weight-sensitive choice
Owner role · Planner
Evidence: The 0.75 and 0.25 policies select different options.
Agree the governing policy
Owner role · Receiving stakeholders
Evidence: The weight is accepted before presenting one winner as the team’s decision.
Decision to make: A wins at a 0.75 first-dimension weight, while B wins at 0.25. The choice depends on the declared preference policy; the score is not an objective business-value measurement.
Filled manual planning note
Invented planning text. Adapt it to your evidence and confirmed owners.
A wins at a 0.75 first-dimension weight, while B wins at 0.25. The choice depends on the declared preference policy; the score is not an objective business-value measurement. A score=8w+2(1−w)=2+6w; B=4w+8(1−w)=8−4w. They tie at w=0.6; the two displayed policies sit on opposite sides. Weighted scores require a defensible local convention. Ordinal labels are not automatically cardinal preference points.
Put the outline to work
- Name both preference dimensions. Check: The invented points and commensurate scoring convention are declared.
- Inspect the weight-sensitive choice. Check: The 0.75 and 0.25 policies select different options.
- Agree the governing policy. Check: The weight is accepted before presenting one winner as the team’s decision.
From a useful outline to team work
Explore TeamBoostAI for your team
Use the owned checks and downloaded brief to discuss this planning decision alongside your TeamBoostAI tasks. Confirm available fields, roles and account features separately. The example is manual; it does not calculate live analytics, create work or run an experiment in the product. Confirm the workflows available in your account before adopting this outline.
Request TeamBoostAI access
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Questions about this workflow
Does the arithmetic tell us the correct weight?
No. It exposes the consequence of a preference the decision owner must choose.
Can these numbers prove impact?
No. They are declared toy preference points, not observed customer or financial outcomes.
Does this create tasks in the product?
No. The brief is a manual planning resource. Use the product access link to check onboarding and the workflows available in your account.