glm-5.1
131e3e929b
Break the @alkdev/taskgraph architecture specs into dependency-ordered implementation tasks across 8 component directories: setup, schema, error, graph, analysis, cost-benefit, frontmatter, api, and review. Each task has clear acceptance criteria referencing specific architecture docs. Three review tasks serve as quality gates at critical junction points (schemas-and-errors, graph-complete, complete-library). The dependency graph is validated acyclic with 9 topological levels enabling significant parallelism across independent work streams.
51 lines
2.4 KiB
Markdown
51 lines
2.4 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: pending
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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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- [ ] `computeEffectiveP(taskId, graph, upstreamSuccessProbs, defaultQualityRetention, propagationMode)` — internal helper
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- [ ] 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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- [ ] In `independent` mode: `pEffective = pIntrinsic` (no propagation)
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- [ ] Completed tasks (`status: "completed"`): propagate with `p = 1.0` when `includeCompleted: false`
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- [ ] `qualityRetention` per edge defaults to 0.9, can be overridden per-edge via `defaultQualityRetention` option or edge attributes
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- [ ] Throws `CircularDependencyError` if graph is cyclic (needs topo sort)
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- [ ] 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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> To be filled by implementation agent
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## Summary
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> To be filled on completion |