Optimize tail risk budget allocation

What it's for

Directs limited resilience or security capital toward the combination that reduces joint portfolio tail loss, rather than ranking controls by average loss alone.

What you give it

Every tool is a pure function: it computes from what you pass it and reads nothing on its own. Inputs split into evidence you supply from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.

{
  "budget": 60,
  "risk_components": [
    {
      "id": "security",
      "mitigation_options": [
        {
          "cost": 20,
          "id": "basic",
          "loss_reduction_fraction": 0.2
        },
        {
          "cost": 60,
          "id": "strong",
          "loss_reduction_fraction": 0.75
        }
      ],
      "scenario_losses": [
        1000,
        200,
        20
      ]
    },
    {
      "id": "reliability",
      "mitigation_options": [
        {
          "cost": 30,
          "id": "basic",
          "loss_reduction_fraction": 0.35
        }
      ],
      "scenario_losses": [
        400,
        250,
        50
      ]
    }
  ],
  "scenario_probabilities": [
    0.1,
    0.3,
    0.6
  ],

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": [
    "Component loss paths share aligned joint scenarios and mitigation fractions scale the represented component loss without changing other components or scenario probabilities.",
    "Options are feasible, mutually exclusive levels for one component with fully loaded incremental cost; behavioral adaptation and control failure are encoded in residual paths or fractions.",
    "CVaR allocation preserves represented dependence but cannot protect against omitted common causes; the greedy fallback has no global optimality certificate."
  ],
  "component_diagnostics": [
    {
      "baseline_expected_loss": 172,
      "risk_component_id": "security",
      "selected_cost": 60,
      "selected_loss_reduction_fraction": 0.75,
      "selected_option_id": "strong"
    },
    {
      "baseline_expected_loss": 145,
      "risk_component_id": "reliability",
      "selected_cost": 0,
      "selected_loss_reduction_fraction": 0,
      "selected_option_id": null
    }
  ],
  "configuration": {
    "budget": 60,
    "scenario_count": 3,
    "tail_probability": 0.1
  },
  "decision": "tail_risk_budget_allocation_supported",
  "method": "scenario_dependent_cvar_risk_budget_allocation_v1",
  "solver_diagnostics": {
    "estimated_complete_state_count": 6,
    "maximum_exact_states": 200000,
    "optimality_proven": true,
    "solver": "exact_option_state_enumeration"
  },
  "summary": {
    "baseline_cvar_loss": 1400,
    "baseline_expected_loss": 317,
    "cvar_reduction": 750,
    "residual_cvar_loss": 650,
    "residual_expected_loss": 188,
    "selected_option_count": 1,
    "total_cost": 60
  },

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 aligned component loss paths, feasible mutually exclusive mitigation levels, fully loaded incremental costs, budget, and tail probability over one loss perimeter.
2. 2 Scale component losses by each chosen level, preserve scenario columns, evaluate portfolio CVaR, and enumerate complete option states through the governed boundary.
3. 3 Above the exact boundary disclose marginal-CVaR-per-cost greedy mode, require approved optimization for material allocation, and stress common causes and control failure absent from the scenarios.

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 20 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": "allocate a finite mitigation budget across" }
  → finds "optimize_tail_risk_budget_allocation"

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

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

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

Related tools

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