{
  "axis": "Continuity of Computation (orthogonal axis, second proof)",
  "axis_question": "What can be reproduced?",
  "build_question": "Can we reproduce the OUTCOME?",
  "canonical_continuity_tenant_dimensions": [
    "#15  Replayable Insurance Claim",
    "#184 Institutional Death Replay",
    "#174 Model Influence Replay",
    "#167 Decision Reproducibility"
  ],
  "computation_axis_so_far": [
    "Model influence is replayable.      (#174 proven)",
    "Decisions are reproducible.         (#167 proven now)",
    "Reasoning survives systems.         (candidate — to be evaluated)"
  ],
  "input": {
    "decision_id": "decision_loan_84711_recommendation",
    "event_count_total": 12,
    "t_replay_ms": 2064614400000,
    "tenant": "tenant_insurance_claim_44962d9b-25f5-5622-bd9a-98d5580bb8a2",
    "tenant_root": "princ_root_claim_44962d9b-25f5-5622-bd9a-98d5580bb8a2"
  },
  "killer_query": "reproduce_decision(decision_loan_84711_recommendation)",
  "money_quote": "Reproducibility ≠ Justification.",
  "money_quote_pattern": [
    "Institutional Memory ≠ Legal Truth (#184)",
    "Influence ≠ Causation (#174)",
    "Reproducibility ≠ Justification (#167)"
  ],
  "proof_id": "first-decision-reproducibility",
  "reproduce_decision_returned": {
    "confidence": {
      "caption": "Decision Reproduction Confidence: 82/100. 3 of 5 components fully anchored.",
      "components": [
        {
          "component": "inputs",
          "explanation": "inputs inferred from ModelInfluenceRecord.feature_attribution (5 features). No inputs_hash anchor.",
          "max_score": 20,
          "score": 12,
          "status": "partially_anchored"
        },
        {
          "component": "policy",
          "explanation": "policy_version_ref pointer reconstructed for pol_credit_underwriting:1 but no AST hash anchor",
          "max_score": 20,
          "score": 10,
          "status": "pointer_only"
        },
        {
          "component": "model_influence",
          "explanation": "ModelInfluenceRecord present (attribution_frame: feature_weights, 5 features, 3 counterfactual probes)",
          "max_score": 20,
          "score": 20,
          "status": "fully_anchored"
        },
        {
          "component": "responsibility",
          "explanation": "ResponsibilityChain present (actor: princ_credit_risk_agent_001, asset_owner: princ_borrower_loan_84711, supervisor: princ_credit_officer_001)",
          "max_score": 20,
          "score": 20,
          "status": "fully_anchored"
        },
        {
          "component": "outcome",
          "explanation": "Decision.outcome = 'recommend_approve' present on the signed event",
          "max_score": 20,
          "score": 20,
          "status": "fully_anchored"
        }
      ],
      "decision_id": "decision_loan_84711_recommendation",
      "max_score": 100,
      "total_score": 82
    },
    "decision_actor": "princ_credit_risk_agent_001",
    "decision_capability": "recommend_credit",
    "decision_id": "decision_loan_84711_recommendation",
    "decision_subject": "loan_84711_borrower_principal_princ_borrower_loan_84711",
    "decision_threshold": 0.75,
    "inferred_inputs": [
      [
        "debt_to_income",
        "0.42"
      ],
      [
        "credit_utilization",
        "0.78"
      ],
      [
        "employment_tenure_years",
        "8.2"
      ],
      [
        "payment_history_pct",
        "0.96"
      ],
      [
        "loan_amount_to_income_ratio",
        "10.5"
      ]
    ],
    "model_influence": {
      "at_ms": 1932000000000,
      "attribution_frame": "feature_weights",
      "counterfactual_probes": [
        {
          "alternative_value": "5.0",
          "feature_name": "loan_amount_to_income_ratio",
          "resulting_score": 0.68,
          "would_have_changed_outcome": true
        },
        {
          "alternative_value": "0.30",
          "feature_name": "credit_utilization",
          "resulting_score": 0.91,
          "would_have_changed_outcome": false
        },
        {
          "alternative_value": "0.65",
          "feature_name": "debt_to_income",
          "resulting_score": 0.61,
          "would_have_changed_outcome": true
        }
      ],
      "decision_id": "decision_loan_84711_recommendation",
      "decision_threshold": 0.75,
      "feature_attribution": [
        {
          "feature_name": "debt_to_income",
          "value": "0.42",
          "weight": 0.31
        },
        {
          "feature_name": "credit_utilization",
          "value": "0.78",
          "weight": 0.22
        },
        {
          "feature_name": "employment_tenure_years",
          "value": "8.2",
          "weight": 0.18
        },
        {
          "feature_name": "payment_history_pct",
          "value": "0.96",
          "weight": 0.12
        },
        {
          "feature_name": "loan_amount_to_income_ratio",
          "value": "10.5",
          "weight": -0.21
        }
      ],
      "influence_id": "influence_loan_84711_recommendation_v1",
      "model_id": "model_credit_underwriting",
      "model_version": 1,
      "prediction_score": 0.84,
      "recognized_by": "princ_model_owner_credit_underwriting"
    },
    "outcome": "recommend_approve",
    "policy_version_ref": [
      "pol_credit_underwriting",
      1
    ],
    "responsibility_chain": {
      "actor": "princ_credit_risk_agent_001",
      "asset_owner": "princ_borrower_loan_84711",
      "delegated_from": "princ_credit_officer_001",
      "model_owner": "princ_model_owner_credit_underwriting",
      "policy_owner": "princ_policy_owner_underwriting",
      "responsibility_timestamp_ms": 1744675200000,
      "supervisor": "princ_credit_officer_001"
    }
  },
  "state_id_at_replay_2035": "e72d3c0e71a11ce0aaf1e8c9eb5c720aff49a6238c76976b4f4435b50e43bee2",
  "verb_shift": "Replay is evidence. Reproduce is computation."
}