Feature: Influence DAG traversal in correction affected subtree
  The affected subtree computation must traverse BOTH the structural
  tree (parent-child) AND the influence DAG (decision_dependencies)
  edges so that corrections cascade through influence relationships.

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  # Structural-only (existing behaviour preserved)
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  Scenario: Structural-only subtree with no influence edges
    Given an influence-aware correction service
    And a correction request targeting decision "D1" in plan "P1" for revert
    And a structural tree where "D1" has children "D2,D3" and "D2" has children "D4"
    And no influence edges
    When I analyze the impact with influence support
    Then the affected decision set should be "D1,D2,D3,D4"


  Scenario: Structural-only single node
    Given an influence-aware correction service
    And a correction request targeting decision "D1" in plan "P1" for revert
    And a structural tree with no children for "D1"
    And no influence edges
    When I analyze the impact with influence support
    Then the affected decision set should be "D1"


  # ================================================================
  # Influence-only (new behaviour)
  # ================================================================

  Scenario: Influence-only affected subtree finds linked decisions
    Given an influence-aware correction service
    And a correction request targeting decision "D1" in plan "P1" for revert
    And a structural tree with no children for "D1"
    And influence edges where "D1" influences "D5,D6" and "D5" influences "D7"
    When I analyze the impact with influence support
    Then the affected decision set should be "D1,D5,D6,D7"


  Scenario: Influence-only with no structural children
    Given an influence-aware correction service
    And a correction request targeting decision "DX" in plan "P1" for revert
    And a structural tree with no children for "DX"
    And influence edges where "DX" influences "DY"
    When I analyze the impact with influence support
    Then the affected decision set should be "DX,DY"


  # ================================================================
  # Combined structural + influence (union)
  # ================================================================

  Scenario: Combined structural and influence subtree is the union
    Given an influence-aware correction service
    And a correction request targeting decision "D1" in plan "P1" for revert
    And a structural tree where "D1" has children "D2,D3"
    And influence edges where "D1" influences "D4" and "D3" influences "D5"
    When I analyze the impact with influence support
    Then the affected decision set should be "D1,D2,D3,D4,D5"


  Scenario: Overlapping structural and influence edges deduplicate
    Given an influence-aware correction service
    And a correction request targeting decision "D1" in plan "P1" for revert
    And a structural tree where "D1" has children "D2"
    And influence edges where "D1" influences "D2"
    When I analyze the impact with influence support
    Then the affected decision set should be "D1,D2"


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  # Cycle detection (corrupt data)
  # ================================================================

  Scenario: Cycle in influence DAG is handled gracefully
    Given an influence-aware correction service
    And a correction request targeting decision "D1" in plan "P1" for revert
    And a structural tree with no children for "D1"
    And influence edges with a cycle "D1" to "D2" to "D3" back to "D1"
    When I analyze the impact with influence support
    Then the affected decision set should be "D1,D2,D3"
    And no infinite loop occurred


  Scenario: Self-loop in influence DAG is handled gracefully
    Given an influence-aware correction service
    And a correction request targeting decision "D1" in plan "P1" for revert
    And a structural tree with no children for "D1"
    And influence edges where "D1" influences "D1"
    When I analyze the impact with influence support
    Then the affected decision set should be "D1"


  # ================================================================
  # Decision creation populates decision_dependencies
  # ================================================================

  Scenario: Decision creation records influence dependencies
    Given a decision service for influence tracking
    When I record a root decision "strategy_choice" for the tracked plan
    And I record a child decision "implementation_choice" depending on the root decision
    Then the influence edges for the tracked plan should link the root to the child


  Scenario: Decision creation without dependencies records no edges
    Given a decision service for influence tracking
    When I record a root decision "strategy_choice" for the tracked plan
    Then the influence edges for the tracked plan should be empty


  # ================================================================
  # Revert execution with influence edges
  # ================================================================

  Scenario: Revert with influence edges invalidates all affected
    Given an influence-aware correction service
    And a correction request targeting decision "D1" in plan "P1" for revert
    And a structural tree where "D1" has children "D2"
    And influence edges where "D1" influences "D3"
    When I execute the revert with influence support
    Then the reverted decisions should include "D1"
    And the reverted decisions should include "D2"
    And the reverted decisions should include "D3"