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boc/docs/design/DECISION_MODEL_REVIEW.md
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Bernt f05fded74e docs: Decision Pipeline v1.0 + Learning Loop + Control Intelligence + 3 Customer Cases
- Added Step 7: Learning (feedback loop from Business Impact to Intelligence)
- Control Intelligence definition: LandveX produces Control Intelligence, not AI
- Three target customer cases defined:
  1. Municipality — Inspect or wait? (Maintenance prioritization)
  2. Property Owner — Repair now or plan later? (Cost vs risk)
  3. Contractor/Operations — Which action first? (Operational planning)
- Validation requirements: Run each case through full pipeline, document breaks,
  revise only after data contradicts model
- Communication principle: Observation → Analysis → Recommendation
  (AI is implementation, recommendation is product)

Rationale: Stop modeling, start observing. Model changes when data contradicts
it, not before. Three real customer cases before freezing.
2026-07-02 12:13:25 +00:00

7.5 KiB

DECISION MODEL v1.0 — MANUAL REVIEW

Validation of 8 Scenarios Against Decision Object Structure

Version 1.0
Date 2026-07-02
Reviewer AI Agent
Rule No changes to model during review

Review Protocol

For each scenario, answer:

# Control Question Result
1 Decision Is this a real decision, not just an observation? Yes / No
2 Verb Can the decision be expressed as a verb? Yes / No
3 Information Is more information needed before deciding? Yes / No
4 Decision Object Are all 7 fields used? List unused
5 Missing Fields Is anything missing? List missing
6 Explainability Can the chain Reality → Observation → Evidence → Finding → Decision be followed? Yes / No
7 Domain Object Does the Decision Object reference at least one concrete domain object? Yes / No
8 Outcome Pass / Observation / Fail

Scenario 1: Road Crack (Maintenance)

# Control Result Notes
1 Decision Yes "Prioritize inspection" is a decision
2 Verb Yes "Inspect"
3 Information No All needed info present
4 Decision Object All used Decision, Why, Evidence, Confidence, Consequence, Action, Business Impact
5 Missing Fields None
6 Explainability Yes Photo → crack detected → 3 observations → degraded 15% → inspect
7 Domain Object Yes "Road 1132"
8 Outcome PASS

Scenario 2: Damaged Facade (Safety)

# Control Result Notes
1 Decision Yes "Immediate safety inspection" is a decision
2 Verb Yes "Inspect" (urgent)
3 Information No All needed info present
4 Decision Object All used All 7 fields
5 Missing Fields None
6 Explainability Yes Drone video → panel loose → weather data → integrity compromised → immediate inspection
7 Domain Object Yes "Building A7"
8 Outcome PASS

Scenario 3: Broken Road Sign (Compliance)

# Control Result Notes
1 Decision Yes "Replace sign" is a decision
2 Verb Yes "Replace"
3 Information No All needed info present
4 Decision Object All used All 7 fields
5 Missing Fields None
6 Explainability Yes Photo → sign damaged → traffic data → control compromised → replace
7 Domain Object Yes "Intersection X"
8 Outcome PASS

Scenario 4: Vegetation Blocking Sight (Risk Reduction)

# Control Result Notes
1 Decision Yes "Schedule vegetation removal" is a decision
2 Verb Yes "Remove"
3 Information No All needed info present
4 Decision Object All used All 7 fields
5 Missing Fields None
6 Explainability Yes Photos → vegetation high → growth trend → degradation → schedule removal
7 Domain Object Yes "Intersection Y"
8 Outcome PASS

Scenario 5: Parking Area Wear (Investment Priority)

# Control Result Notes
1 Decision Yes "Include in budget" is a decision
2 Verb Yes "Include" / "Budget"
3 Information ⚠️ Observation May need cost estimate for full decision
4 Decision Object All used All 7 fields
5 Missing Fields ⚠️ Observation "Cost estimate" could strengthen decision
6 Explainability Yes Photos → wear → usage data → need resurfacing → budget
7 Domain Object Yes "Parking Area Z"
8 Outcome ⚠️ OBSERVATION Decision valid but could be strengthened with cost estimate

Scenario 6: Cosmetic Scratch (No Action)

# Control Result Notes
1 Decision Yes "No action, continue monitoring" is a conscious decision
2 Verb Yes "Monitor"
3 Information No All needed info present
4 Decision Object All used All 7 fields
5 Missing Fields None
6 Explainability Yes Photo → scratches → no functional impact → normal wear → monitor
7 Domain Object Yes "Sign S15"
8 Outcome PASS

Scenario 7: Mixed Evidence Sources (Complex)

# Control Result Notes
1 Decision Yes "Inspect drainage, prioritize if recurs" is a decision
2 Verb Yes "Inspect" / "Prioritize"
3 Information No All needed info present
4 Decision Object All used All 7 fields
5 Missing Fields None
6 Explainability Yes Photo + sensor + weather → pooling → historical data → drainage inadequate → inspect
7 Domain Object Yes "Road Segment R42"
8 Outcome PASS

Scenario 8: Insufficient Evidence (No Recommendation)

# Control Result Notes
1 Decision ⚠️ Observation "No recommendation yet" is not a decision, it's a deferral
2 Verb ⚠️ Observation "Collect" is an action, not a final decision
3 Information Yes More data needed
4 Decision Object ⚠️ Observation "Consequence" and "Business Impact" are weak
5 Missing Fields ⚠️ Observation "Confidence" is low by definition — could be explicit
6 Explainability Yes Blurry photo → unclear → conflicting AI → inconclusive
7 Domain Object ⚠️ Observation Location unclear due to low GPS precision
8 Outcome ⚠️ OBSERVATION Valid outcome but not a decision — model handles it correctly

Review Summary

Metric Count
Total Scenarios 8
Pass 6
Observation 2
Fail 0

Recurring Unused Fields

None. All 7 fields used across all scenarios.

Recurring Missing Fields

  • Cost estimate (Scenario 5) — could strengthen investment decisions
  • Explicit low confidence (Scenario 8) — could clarify insufficient evidence

Recurring Unclear Verbs

None. All decisions expressible as verbs.

Domain Object References

Scenario Domain Object Status
1 Road 1132
2 Building A7
3 Intersection X
4 Intersection Y
5 Parking Area Z
6 Sign S15
7 Road Segment R42
8 (unclear GPS) ⚠️

Conclusion

Recommendation: READY FOR INVARIANCE TEST

The Decision Object structure handles all 8 scenarios without modification. Two scenarios generate observations (not failures) — the model correctly handles "insufficient evidence" and "needs more data" cases.

No changes needed to Decision Model v1.0.


Next Steps

  1. Manual Review — COMPLETE
  2. Decision Invariance Test — Ready to run
  3. Evidence Variation Test — Ready to run
  4. Empirical validation with real data — Pending

ändringshistoria

Version Datum Beskrivning
1.0 2026-07-02 Manual review of 8 scenarios — 6 pass, 2 observation, 0 fail

STATUS

REVIEW COMPLETE — READY FOR INVARIANCE TEST