Forecast dependency adjusted consensus

What it's for

Turns multiple roadmap, delivery or board opinions into one honest probability without pretending correlated management reports and models are four independent confirmations.

What you give it

Inputs split into evidence read from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.

{
  "as_of_ms": 1004,
  "bootstrap_draws": 200,
  "current_forecasts": [
    {
      "evidence_verified": true,
      "forecast_at_ms": 1003,
      "id": "current-forecast-expert-a",
      "information_cutoff_ms": 1002,
      "probability": 0.88,
      "question_id": "current-roadmap",
      "sealed_independent": true,
      "source_ref": "expert-a"
    },
    {
      "evidence_verified": true,
      "forecast_at_ms": 1003,
      "id": "current-forecast-copier-a",
      "information_cutoff_ms": 1002,
      "probability": 0.86,
      "question_id": "current-roadmap",
      "sealed_independent": true,
      "source_ref": "copier-a"
    },
    {
      "evidence_verified": true,
      "forecast_at_ms": 1003,
      "id": "current-forecast-expert-b",
      "information_cutoff_ms": 1002,
      "probability": 0.78,
      "question_id": "current-roadmap",
      "sealed_independent": true,
      "source_ref": "expert-b"
    },
    {
      "evidence_verified": true,
      "forecast_at_ms": 1003,
      "id": "current-forecast-noise",
      "information_cutoff_ms": 1002,
      "probability": 0.45,
      "question_id": "current-roadmap",
      "sealed_independent": true,
      "source_ref": "noise"
    }

What you get back

This is the actual output of running the example above — computed by the same function the platform calls, not an illustration.

{
  "assumptions": [
    "Historical forecasts were independently sealed before resolution, question contexts are stable enough to estimate base rates, and declared source dependencies are complete.",
    "Positive forecast-innovation correlation is treated conservatively as redundant evidence; source coefficients and context rates are fitted only on the earlier split before validation."
  ],
  "counts": {
    "current_questions": 1,
    "declared_dependencies": 1,
    "empirical_dependencies": 6,
    "historical_forecasts": 400,
    "historical_questions": 100,
    "sources": 4
  },
  "decision": "dependency_adjusted_consensus_validated",
  "failed_gates": [],
  "forecasts": [
    {
      "context_base_probability": 0.6623,
      "context_id": "roadmap",
      "dependency_adjusted_probability": 0.8734,
      "dominant_source_weight": 0.4063,
      "effective_independent_sources": 1.1028,
      "leave_one_source_out_probability_range": 0.0651,
      "posterior_probability_interval_90": [
        0.8141,
        0.8705,
        0.9158
      ],
      "question_id": "current-roadmap",
      "source_diagnostics": [
        {
          "dependency_adjusted_weight": 0.40629,
          "reliability_coefficient": 1.110489,
          "source_ref": "expert-b"
        },
        {
          "dependency_adjusted_weight": 0.324557,
          "reliability_coefficient": 1.366382,
          "source_ref": "expert-a"
        },
        {
          "dependency_adjusted_weight": 0.269153,
          "reliability_coefficient": 1.32017,
          "source_ref": "copier-a"

How it works

Forecasting & survival — Estimate when something completes or fails, with censoring and unresolved work handled honestly rather than dropped.

1. 1 Chronologically split whole resolved questions and fit context base rates plus regularized source-specific logit reliability only on the earlier split.
2. 2 Estimate positive forecast-innovation dependence from overlapping earlier questions, take the conservative maximum with declared common-source links, and solve covariance-adjusted nonnegative source weights.
3. 3 Require later log-loss gain over context base rates and unadjusted consensus plus calibration support; only then refit and return posterior intervals, effective independent-source count and leave-one-source-out fragility.

Before you trust it

Every tool in the catalog ships with the conditions under which its answer is meaningful — and the conditions under which it should abstain instead of guessing.

Calibrating it to your org

Calibration workflow

1. 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
2. 2 Build a tenant-scoped historical cohort using only information available before each prediction or decision; preserve zero periods, censoring, assignment probabilities, and unresolved outcomes when the method requires them.
3. 3 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
4. 4 Validate on later time windows or held-out aggregate units at the deployment grain, against a simple baseline and the function-specific validation strategy.
5. 5 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
6. 6 Monitor realized outcomes, data drift, coverage, and decision regret; recalibrate at a governed cadence or after a detected regime/definition change, never merely because a stakeholder dislikes the result.

Call it from your AI

You don't wire up 388 tools in your MCP client. The GitRevio MCP server exposes 18 tools, three of which let an agent search the catalog, read a tool's schema, and run it — so the assistant finds this one on its own.

gitrevio_capabilities_search
  { "q": "combine independently sealed human and model" }
  → finds "forecast_dependency_adjusted_consensus"

gitrevio_capability_describe
  { "capability_id": "forecast_dependency_adjusted_consensus" }
  → returns the input schema and agent guidance shown on this page

gitrevio_capability_run
  { "capability_id": "forecast_dependency_adjusted_consensus", "arguments": { ... } }
  → returns the result shown above

Works in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.

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