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feat(cost-benefit/dag-propagation): implement DAG-propagation effective probability computation

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Implement computeEffectiveP internal helper and workflowCost public function
that captures the structural reality that upstream failures multiply downstream
damage, per ADR-004 and the Python research model.

- computeEffectiveP: computes pEffective from intrinsic probability + upstream
  propagation using inherited quality factors (parentP + (1-parentP) × qualityRetention)
- workflowCost: processes tasks in topological order, computes per-task EV with
  degraded effective probability, includes pIntrinsic/pEffective split
- Supports independent and dag-propagate modes
- Completed tasks excluded from results but propagate p=1.0 when includeCompleted: false
- Per-edge qualityRetention overrides defaultQualityRetention option
- Throws CircularDependencyError for cyclic graphs via topologicalOrder
- 30+ new tests covering chain compounding, diamond graph, mode comparison,
  completed task semantics, cycle detection, per-edge qualityRetention
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glm-5.1
2026-04-27 12:52:47 +00:00
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commit 7bfcfecc7a
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26
tasks/implementation/cost-benefit/dag-propagation.md
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@@ -1,7 +1,7 @@
---
id: cost-benefit/dag-propagation
name: Implement DAG-propagation effective probability computation
status: pending
status: completed
depends_on:
- cost-benefit/ev-calculation
- graph/queries
@@ -24,17 +24,17 @@ Per [cost-benefit.md](../../../docs/architecture/cost-benefit.md), the algorithm
## Acceptance Criteria
- [ ] `computeEffectiveP(taskId, graph, upstreamSuccessProbs, defaultQualityRetention, propagationMode)` — internal helper
- [ ] In `dag-propagate` mode: for each task in topological order:
- [x] `computeEffectiveP(taskId, graph, upstreamSuccessProbs, defaultQualityRetention, propagationMode)` — internal helper
- [x] In `dag-propagate` mode: for each task in topological order:
- Get intrinsic probability from `resolveDefaults(risk).successProbability`
- For each prerequisite, compute inherited quality: `parentP + (1 - parentP) × qualityRetention`
- `pEffective` = intrinsic × product of all inherited quality factors
- Store task's **actual** success probability for downstream propagation (use `pEffective` if this is the task's real probability)
- [ ] In `independent` mode: `pEffective = pIntrinsic` (no propagation)
- [ ] Completed tasks (`status: "completed"`): propagate with `p = 1.0` when `includeCompleted: false`
- [ ] `qualityRetention` per edge defaults to 0.9, can be overridden per-edge via `defaultQualityRetention` option or edge attributes
- [ ] Throws `CircularDependencyError` if graph is cyclic (needs topo sort)
- [ ] Unit tests: simple chain (verify compounding effect), diamond graph, independent vs dag-propagate comparison matches Python research model results, completed task exclusion/propagation semantics
- [x] In `independent` mode: `pEffective = pIntrinsic` (no propagation)
- [x] Completed tasks (`status: "completed"`): propagate with `p = 1.0` when `includeCompleted: false`
- [x] `qualityRetention` per edge defaults to 0.9, can be overridden per-edge via `defaultQualityRetention` option or edge attributes
- [x] Throws `CircularDependencyError` if graph is cyclic (needs topo sort)
- [x] Unit tests: simple chain (verify compounding effect), diamond graph, independent vs dag-propagate comparison matches Python research model results, completed task exclusion/propagation semantics
## References
@@ -44,8 +44,14 @@ Per [cost-benefit.md](../../../docs/architecture/cost-benefit.md), the algorithm
## Notes
> To be filled by implementation agent
No depth escalation in v1 per ADR-005 — multiplicative propagation captures depth effects implicitly.
Per-edge qualityRetention on edges takes precedence over defaultQualityRetention option.
With default EvConfig (no fallbackCost/timeLost), EV = scopeCost × impactWeight regardless of p, so totalEv is similar across modes but pEffective differs.
## Summary
> To be filled on completion
Implemented DAG-propagation effective probability computation with `computeEffectiveP` internal helper and `workflowCost` public function.
- Modified: `src/analysis/cost-benefit.ts` — added `computeEffectiveP` and `workflowCost` functions
- Modified: `test/cost-benefit.test.ts` — added 30+ new tests for DAG propagation
- Tests: 63 total in cost-benefit.test.ts, all 476 across test suite passing
- Key features: topological ordering, per-edge qualityRetention, independent/dag-propagate modes, completed task exclusion/propagation semantics, CircularDependencyError for cyclic graphs