# Inspect a ranking when weights change

Illustrative planning brief; no automatic product import.

Synthetic 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.

## Decision

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.

## Owned work

- Name both preference dimensions
  - Owner role: Decision owner
  - Acceptance evidence: The invented points and commensurate scoring convention are declared.
- Inspect the weight-sensitive choice
  - Owner role: Planner
  - Acceptance evidence: The 0.75 and 0.25 policies select different options.
- Agree the governing policy
  - Owner role: Receiving stakeholders
  - Acceptance evidence: The weight is accepted before presenting one winner as the team’s decision.

## Workflow

1. Name both preference dimensions. Check: The invented points and commensurate scoring convention are declared.
2. Inspect the weight-sensitive choice. Check: The 0.75 and 0.25 policies select different options.
3. Agree the governing policy. Check: The weight is accepted before presenting one winner as the team’s decision.

## Judgment

Weighted scores require a defensible local convention. Ordinal labels are not automatically cardinal preference points.


## Filled manual planning note

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.


## Before / after

Before: At w=0.75, A scores 6.5 and B five.

After: At w=0.25, A scores 3.5 and B seven; the policy changes the winner.


## Workflow questions

### 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.

## Product connection

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 account availability before adopting this manual outline.

## Original worked case

Synthetic records, manual planning only. No account import or live analytics.

### Inspect the invented case records

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

### Reasoning

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.

### 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.

### Distinct decision

The artifact locates a ranking reversal under two explicit weight policies.

### Limits

Weighted scores require a defensible local convention. Ordinal labels are not automatically cardinal preference points.

### Definitions and method context

- 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.