Calculate decision debt liability

What it's for

Converts slow or unresolved executive decisions into an auditable financial liability, showing CEOs and investors the reserve required and which decisions dominate severe-loss exposure.

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.

{
  "current_decision_debt_reserve": 50,
  "decisions": [
    {
      "cost_of_delay_per_hour_scenarios": [
        1,
        1,
        1
      ],
      "current_age_hours": 10,
      "decision_value_at_risk_scenarios": [
        100,
        100,
        100
      ],
      "id": "architecture-choice",
      "resolution_cost_scenarios": [
        5,
        5,
        5
      ],
      "rework_loss_fraction_scenarios": [
        0.5,
        0.5,
        0.5
      ],
      "rework_probability_scenarios": [
        0.2,
        0.2,
        0.2
      ],
      "stale_decision_probability_scenarios": [
        0.1,
        0.1,
        0.1
      ],
      "stale_loss_fraction_scenarios": [
        1,
        1,
        1
      ]
    },
    {
      "cost_of_delay_per_hour_scenarios": [

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.

{
  "configuration": {
    "maximum_acceptable_cvar_loss": null,
    "reserve_confidence_level": 0.9,
    "tail_probability": 0.2
  },
  "decision": "decision_debt_liability_action_required",
  "decision_liability_contributions": [
    {
      "decision_id": "vendor-choice",
      "expected_delay_loss": 10,
      "expected_resolution_cost": 10,
      "expected_rework_loss": 10,
      "expected_stale_loss": 20,
      "expected_total_liability": 50,
      "tail_cvar_contribution": 50
    },
    {
      "decision_id": "architecture-choice",
      "expected_delay_loss": 10,
      "expected_resolution_cost": 5,
      "expected_rework_loss": 10,
      "expected_stale_loss": 10,
      "expected_total_liability": 35,
      "tail_cvar_contribution": 35
    }
  ],
  "failed_gates": [
    "decision_debt_reserve"
  ],
  "guardrails": [
    "Decision debt is finance-owned exposure from unresolved delay, likely rework, staleness and resolution—not the number of meetings, approvals or open tickets.",
    "Scenario columns must preserve common shocks across decisions on one currency, horizon and price basis. Marginal probabilities cannot reconstruct portfolio tail dependence, and current age does not prove avoidable delay.",
    "Tail contributions allocate represented liability for remediation and reserve planning; they are not causal blame, reviewer performance or authority to bypass mandatory governance."
  ],
  "method": "coherent_scenario_decision_debt_liability_v1",
  "summary": {
    "current_decision_debt_reserve": 50,
    "decision_count": 2,
    "decision_debt_reserve_shortfall": 35,
    "expected_decision_debt_liability": 85,
    "required_decision_debt_reserve": 85,
    "reserve_breach_probability": 1,
    "tail_conditional_value_at_risk_loss": 85

How it works

Simulation & stress testing — Run the system forward many times to see what the bad tail actually looks like.

1. 1 Freeze every unresolved decision, current age and coherent finance-owned scenarios for cost of delay, value at risk, rework/staleness probabilities and loss fractions, and the complete cost to resolve it.
2. 2 Calculate delay, expected rework, expected stale-decision and resolution liability within each unchanged joint scenario, then aggregate portfolio expected loss, confidence reserve, breach probability and tail CVaR.
3. 3 Reconcile every decision's expected components and exact CVaR contribution, compare the required reserve with current reserve and escalate only the represented financial shortfall or tail-appetite breach.

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 unresolved management decision debt from" }
  → finds "calculate_decision_debt_liability"

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

gitrevio_capability_run
  { "capability_id": "calculate_decision_debt_liability", "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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