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Author	SHA1	Message	Date
Bernt	e3e70c52d4	docs: Decision Model v1.0 — 7 validation scenarios + invariance tests - Added 7 validation scenarios covering diverse decision types: 1. Road Crack (Maintenance) — repair now or later? 2. Damaged Facade (Safety) — act immediately? 3. Broken Road Sign (Compliance) — violates requirements? 4. Vegetation Blocking Sight (Risk Reduction) — gradual deterioration 5. Parking Area Wear (Investment Priority) — multiple small → big decision 6. Cosmetic Scratch (No Action) — conscious decision to wait 7. Mixed Evidence Sources — photo + sensor + weather API - Decision Invariance Test: - Same Decision Object structure across all scenarios? - No fields added/removed? - No field meaning changes? - Fail = model needs revision - Evidence Variation Test: - Single image, multiple images, video+GPS, historical, external data, mixed - Decision Object structure unchanged regardless of evidence type - Decision Quality Gate: - Verifiable evidence chain - Motivated confidence - Action or conscious 'no action' - Explainability chain works - Pass criteria: All 7 scenarios valid + invariance + evidence + quality gate Rationale: Validate model against diverse decision types before freezing. No action scenario is as important as action scenarios. Mixed evidence sources test robustness. Invariance test ensures generality.	2026-07-02 12:01:38 +00:00
Bernt	ff136e8aae	docs: Decision Model v1.0 — Evidence-backed decisions + explainability + validation scenarios - Six layers (added Evidence between Observation and Finding): 1. Reality 2. Observation 3. Evidence (linked observations with context) 4. Finding 5. Decision 6. Business Impact - Decision Object restructured: 1. Decision — what should user decide? 2. Why — why system recommends this 3. Evidence — what observations support this 4. Confidence — how certain (3 dimensions) 5. Consequence — what if nothing done 6. Action — next step 7. Business Impact — economic/operational meaning - Confidence Model (3 dimensions): - Observation Confidence: how certain is detection? - Evidence Strength: how strongly supported? - Recommendation Confidence: how certain is recommendation? - Explainability Principle: - Every Decision Card must be explorable - User can click: Decision → Finding → Evidence → Observations → Reality - Competitive advantage: traceability to source material - Business Impact Model (4 dimensions): - Risk, Cost, Time, Opportunity - Three validation scenarios: 1. Road Crack — simple, common 2. Damaged Facade — complex, critical 3. Broken Road Sign — simple, regulatory - Pass criteria: Same Decision Object works for all three Rationale: Decision Intelligence, not BI. Evidence-backed decisions are the core product. Explainability is competitive advantage. Validation against real scenarios before freezing.	2026-07-02 11:59:33 +00:00

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SHA1

Message

Date

Bernt

e3e70c52d4

docs: Decision Model v1.0 — 7 validation scenarios + invariance tests

- Added 7 validation scenarios covering diverse decision types:
  1. Road Crack (Maintenance) — repair now or later?
  2. Damaged Facade (Safety) — act immediately?
  3. Broken Road Sign (Compliance) — violates requirements?
  4. Vegetation Blocking Sight (Risk Reduction) — gradual deterioration
  5. Parking Area Wear (Investment Priority) — multiple small → big decision
  6. Cosmetic Scratch (No Action) — conscious decision to wait
  7. Mixed Evidence Sources — photo + sensor + weather API

- Decision Invariance Test:
  - Same Decision Object structure across all scenarios?
  - No fields added/removed?
  - No field meaning changes?
  - Fail = model needs revision

- Evidence Variation Test:
  - Single image, multiple images, video+GPS, historical, external data, mixed
  - Decision Object structure unchanged regardless of evidence type

- Decision Quality Gate:
  - Verifiable evidence chain
  - Motivated confidence
  - Action or conscious 'no action'
  - Explainability chain works

- Pass criteria: All 7 scenarios valid + invariance + evidence + quality gate

Rationale: Validate model against diverse decision types before freezing.
No action scenario is as important as action scenarios. Mixed evidence
sources test robustness. Invariance test ensures generality.

2026-07-02 12:01:38 +00:00

Bernt

ff136e8aae

docs: Decision Model v1.0 — Evidence-backed decisions + explainability + validation scenarios

- Six layers (added Evidence between Observation and Finding):
  1. Reality
  2. Observation
  3. Evidence (linked observations with context)
  4. Finding
  5. Decision
  6. Business Impact

- Decision Object restructured:
  1. Decision — what should user decide?
  2. Why — why system recommends this
  3. Evidence — what observations support this
  4. Confidence — how certain (3 dimensions)
  5. Consequence — what if nothing done
  6. Action — next step
  7. Business Impact — economic/operational meaning

- Confidence Model (3 dimensions):
  - Observation Confidence: how certain is detection?
  - Evidence Strength: how strongly supported?
  - Recommendation Confidence: how certain is recommendation?

- Explainability Principle:
  - Every Decision Card must be explorable
  - User can click: Decision → Finding → Evidence → Observations → Reality
  - Competitive advantage: traceability to source material

- Business Impact Model (4 dimensions):
  - Risk, Cost, Time, Opportunity

- Three validation scenarios:
  1. Road Crack — simple, common
  2. Damaged Facade — complex, critical
  3. Broken Road Sign — simple, regulatory

- Pass criteria: Same Decision Object works for all three

Rationale: Decision Intelligence, not BI. Evidence-backed decisions
are the core product. Explainability is competitive advantage.
Validation against real scenarios before freezing.

2026-07-02 11:59:33 +00:00

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