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DecisionObjectSchemaDraft.md
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DecisionObjectSchemaDraft.md
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# Decision Object Schema Draft v0.1
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This is an intentionally loose structural draft.
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It is not yet a normative JSON Schema.
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The purpose is to identify modules and boundaries before freezing field names or validation rules.
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## 1. Top-level shape
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```yaml
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decision:
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identity:
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question:
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kind:
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state:
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frame:
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alternatives:
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evidence:
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world_model:
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values:
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analysis:
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uncertainty:
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information_options:
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governance:
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commitment:
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effects:
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execution:
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outcome:
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learning:
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optionality:
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relationships:
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temporal:
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metadata:
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```
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## 2. Identity
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```yaml
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identity:
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id: DEC-...
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title:
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namespace:
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version:
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```
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## 3. Question
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```yaml
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question:
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text:
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decision_class:
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trigger:
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```
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## 4. Kind
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```yaml
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kind:
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primary: choice
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secondary:
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- experiment
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```
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Candidate values:
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```text
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choice
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bet
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experiment
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commitment
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delegation
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allocation
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authorization
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exception
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policy
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constitutional
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```
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## 5. State
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```yaml
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state:
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lifecycle: committed
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since:
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```
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Candidate lifecycle values:
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```text
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noticed
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framed
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open
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recommended
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committed
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effective
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observed
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reviewed
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closed
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deferred
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delegated
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cancelled
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expired
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superseded
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revoked
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```
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## 6. Frame
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```yaml
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frame:
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situation:
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scope:
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horizon:
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boundaries:
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assumptions: []
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exclusions: []
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stakeholders: []
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dependencies: []
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```
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## 7. Alternatives
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```yaml
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alternatives:
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- id: A
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label:
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action:
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description:
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prerequisites: []
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reversibility:
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level:
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cost:
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delay:
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```
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## 8. Evidence
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```yaml
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evidence:
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claims:
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- id:
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statement:
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status:
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confidence:
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source:
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valid_at:
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observations: []
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assumptions: []
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sources: []
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```
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Candidate epistemic statuses:
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```text
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unknown
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not-applicable
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not-investigated
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estimated
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assumed
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observed
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derived
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inferred
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decided
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```
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## 9. World model
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```yaml
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world_model:
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states: []
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variables: []
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relationships: []
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causal_model:
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scenarios: []
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```
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## 10. Values
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```yaml
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values:
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objectives: []
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constraints: []
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preferences: []
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thresholds: []
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vetoes: []
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tradeoffs: []
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```
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## 11. Analysis
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```yaml
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analysis:
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analyses:
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- id:
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method:
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model:
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inputs:
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result:
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confidence:
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sensitivity:
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robustness:
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recommendation:
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```
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## 12. Uncertainty
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```yaml
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uncertainty:
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known_unknowns: []
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model_uncertainty: []
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outcome_uncertainty: []
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ambiguity: []
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reducible: []
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irreducible: []
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```
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## 13. Information options
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```yaml
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information_options:
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- question:
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method:
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cost:
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delay:
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expected_information_value:
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```
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## 14. Governance
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```yaml
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governance:
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owner:
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authority:
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source:
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scope:
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can:
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may:
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must:
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advisors: []
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approvers: []
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challengers: []
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affected_parties: []
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delegation:
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escalation:
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oversight:
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```
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## 15. Commitment
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```yaml
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commitment:
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selected:
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by:
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at:
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mode:
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confidence:
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conditions: []
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reservations: []
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```
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## 16. Effects
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```yaml
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effects:
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- id:
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type:
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target:
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value:
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effective_when:
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expires_when:
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```
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Candidate effect types:
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```text
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action
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allocation
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authorization
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constraint
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delegation
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policy
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incentive
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default
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prohibition
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requirement
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```
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## 17. Execution
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```yaml
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execution:
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intended_actions: []
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responsible: []
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status:
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started_at:
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completed_at:
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evidence: []
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```
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## 18. Outcome
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```yaml
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outcome:
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observations: []
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metrics: []
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unintended_effects: []
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stakeholder_effects: []
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occurred_at:
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```
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## 19. Learning
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```yaml
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learning:
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expected:
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observed:
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deviations: []
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hypothesis_updates: []
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calibration:
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lessons: []
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followup_decisions: []
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```
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## 20. Optionality
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```yaml
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optionality:
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reversible:
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reversal_cost:
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reversal_delay:
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locks_in: []
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options_created: []
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options_destroyed: []
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```
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## 21. Relationships
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```yaml
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relationships:
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- type: governed-by
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target: POLICY-SEC-001
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```
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Candidate relationship types:
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```text
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derived-from
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implements
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governed-by
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delegated-by
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authorized-by
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triggered-by
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depends-on
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challenges
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reviews
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supersedes
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revokes
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exception-to
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resulted-in
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learned-from
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```
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## 22. Temporal
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```yaml
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temporal:
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created_at:
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decision_time:
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effective_from:
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effective_until:
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observed_at:
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reviewed_at:
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```
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## 23. Metadata
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```yaml
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metadata:
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formality_level: D3
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tags: []
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schema_version:
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```
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## 24. Open design questions
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- Which fields belong in the immutable kernel?
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- Which fields should be versioned instead of overwritten?
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- How should competing analyses be represented?
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- How should probabilities and utility models be encoded?
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- Should authority assertions use a separate canonical model?
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- How should policy primitives map to executable rule systems such as DMN or OPA-style policy engines?
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- How should nested or dependent decisions be modelled?
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- Which relationship semantics should be normative?
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- How should decision-time snapshots be preserved?
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- What minimum structure is necessary for reliable decision lineage?
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