Calculate shared assumption risk exposure

What it's for

Turns a board's hidden 'all these projects assume the same thing' concern into a dollar reserve, severe-tail exposure and exact premise-by-premise attribution without double counting.

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.

{
  "assumptions": [
    {
      "id": "adoption",
      "retained_value_fraction_scenarios": [
        0.8,
        0.4
      ]
    },
    {
      "id": "price",
      "retained_value_fraction_scenarios": [
        0.9,
        0.5
      ]
    }
  ],
  "current_shared_assumption_risk_reserve": 100,
  "initiatives": [
    {
      "approved_value_scenarios": [
        100,
        80
      ],
      "assumption_ids": [
        "adoption",
        "price"
      ],
      "id": "shared-platform"
    },
    {
      "approved_value_scenarios": [
        60,
        40
      ],
      "assumption_ids": [
        "adoption"
      ],
      "id": "enterprise-product"
    }
  ],
  "scenarios": [
    {
      "id": "base",

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.

{
  "assumption_risk_attribution": [
    {
      "assumption_id": "adoption",
      "expected_loss_shapley_contribution": 39.7,
      "expected_retained_value_fraction": 0.68,
      "linked_initiative_count": 2,
      "tail_cvar_contribution_fraction": 0.6818,
      "tail_cvar_loss_shapley_contribution": 60
    },
    {
      "assumption_id": "price",
      "expected_loss_shapley_contribution": 14.7,
      "expected_retained_value_fraction": 0.78,
      "linked_initiative_count": 1,
      "tail_cvar_contribution_fraction": 0.3182,
      "tail_cvar_loss_shapley_contribution": 28
    }
  ],
  "configuration": {
    "maximum_acceptable_cvar_loss": null,
    "maximum_single_assumption_tail_contribution_fraction": 1,
    "minimum_expected_retained_value_fraction": 0,
    "reserve_confidence_level": 0.9,
    "scenario_count": 2,
    "tail_probability": 0.3
  },
  "decision": "shared_assumption_risk_supported",
  "failed_gates": [],
  "guardrails": [
    "Every scenario column is one coherent joint state across approved values and all assumptions. Independently sorted marginals destroy shared downside dependence and can manufacture diversification.",
    "Initiative value survives only when every declared premise survives. Exact Shapley allocation distributes that nonlinear modeled loss without double counting; it is fair risk accounting, not causal blame or evidence that a premise will fail.",
    "Approved values must be incremental, non-overlapping and finance governed. Assumption links must reflect necessary premises rather than every remotely related factor, and scenario probabilities require local calibration and periodic backtesting."
  ],
  "initiative_risk_diagnostics": [
    {
      "assumption_ids": [
        "adoption",
        "price"
      ],
      "expected_approved_value": 94,
      "expected_assumption_loss": 38.8,
      "expected_retained_value": 55.2,
      "initiative_id": "shared-platform",

How it works

Constrained optimization — Pick the best feasible option under real limits — budget, headcount, dependencies, capacity — rather than ranking a list and hoping it fits.

1. 1 Freeze finance-approved initiative values, necessary assumption links and assumption retained-value fractions inside identically ordered joint scenarios.
2. 2 Multiply retained fractions within each initiative so value survives only when all declared necessary premises survive, then aggregate losses without independently sorting common shocks.
3. 3 Allocate nonlinear loss exactly with the Shapley integral, reuse the realized portfolio tail weights for coherent CVaR contributions, and gate reserve, retained value, tail loss and single-premise concentration.

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": "price coherent portfolio value loss when" }
  → finds "calculate_shared_assumption_risk_exposure"

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

gitrevio_capability_run
  { "capability_id": "calculate_shared_assumption_risk_exposure", "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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