glm-5.1
7bfcfecc7a
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
57 lines
3.3 KiB
Markdown
57 lines
3.3 KiB
Markdown
---
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id: cost-benefit/dag-propagation
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name: Implement DAG-propagation effective probability computation
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status: completed
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depends_on:
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- cost-benefit/ev-calculation
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- graph/queries
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- schema/numeric-methods-and-defaults
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scope: moderate
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risk: high
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impact: phase
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level: implementation
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---
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## Description
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Implement the DAG-propagation cost model in `src/analysis/cost-benefit.ts`. This is the core algorithmic contribution beyond the Rust CLI — it captures the structural reality that upstream failures multiply downstream damage.
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Per [cost-benefit.md](../../../docs/architecture/cost-benefit.md), the algorithm:
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1. Processes tasks in topological order
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2. For each task with prerequisites, computes `pEffective` from intrinsic probability + upstream propagation
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3. Upstream propagation: `parentP + (1 - parentP) × qualityRetention` for each parent
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4. `pEffective` = intrinsic × product of all inherited quality factors
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## Acceptance Criteria
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- [x] `computeEffectiveP(taskId, graph, upstreamSuccessProbs, defaultQualityRetention, propagationMode)` — internal helper
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- [x] In `dag-propagate` mode: for each task in topological order:
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- Get intrinsic probability from `resolveDefaults(risk).successProbability`
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- For each prerequisite, compute inherited quality: `parentP + (1 - parentP) × qualityRetention`
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- `pEffective` = intrinsic × product of all inherited quality factors
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- Store task's **actual** success probability for downstream propagation (use `pEffective` if this is the task's real probability)
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- [x] In `independent` mode: `pEffective = pIntrinsic` (no propagation)
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- [x] Completed tasks (`status: "completed"`): propagate with `p = 1.0` when `includeCompleted: false`
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- [x] `qualityRetention` per edge defaults to 0.9, can be overridden per-edge via `defaultQualityRetention` option or edge attributes
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- [x] Throws `CircularDependencyError` if graph is cyclic (needs topo sort)
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- [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
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## References
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- docs/architecture/cost-benefit.md — DAG-propagation algorithm, qualityRetention semantics
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- docs/architecture/decisions/004-workflow-cost-dag-propagation.md — ADR-004
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- docs/architecture/decisions/005-no-depth-escalation-v1.md — no depth escalation in v1
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## Notes
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No depth escalation in v1 per ADR-005 — multiplicative propagation captures depth effects implicitly.
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Per-edge qualityRetention on edges takes precedence over defaultQualityRetention option.
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With default EvConfig (no fallbackCost/timeLost), EV = scopeCost × impactWeight regardless of p, so totalEv is similar across modes but pEffective differs.
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## Summary
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Implemented DAG-propagation effective probability computation with `computeEffectiveP` internal helper and `workflowCost` public function.
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- Modified: `src/analysis/cost-benefit.ts` — added `computeEffectiveP` and `workflowCost` functions
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- Modified: `test/cost-benefit.test.ts` — added 30+ new tests for DAG propagation
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- Tests: 63 total in cost-benefit.test.ts, all 476 across test suite passing
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- Key features: topological ordering, per-edge qualityRetention, independent/dag-propagate modes, completed task exclusion/propagation semantics, CircularDependencyError for cyclic graphs |