Optimize time consistent capital policy

What it's for

Turns a board scenario tree into an executable capital playbook—what to commit now and what to do later—without hindsight leakage or risk-policy reversals.

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.

{
  "actions": [
    {
      "budget_cost": 1,
      "capacity_units": 1,
      "cash_effect_scenarios": [
        40,
        0
      ],
      "dependency_ids": [],
      "eligible_node_ids": [
        "adverse"
      ],
      "exclusion_ids": [],
      "id": "adverse-bridge",
      "terminal_value_effect_scenarios": [
        -10,
        0
      ],
      "transaction_cost_scenarios": [
        2,
        0
      ]
    }
  ],
  "budget_by_node": {
    "adverse": 1,
    "favorable": 0,
    "root": 0
  },
  "capacity_by_node": {
    "adverse": 1,
    "favorable": 0,
    "root": 0
  },
  "maximum_liquidity_breach_probability": 0,
  "maximum_nested_cvar_loss": 0,
  "minimum_cash_buffer": 20,
  "scenarios": [
    {
      "base_cash_flows": [
        0,
        -60
      ],

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": {
    "action_count": 1,
    "maximum_liquidity_breach_probability": 0,
    "maximum_nested_cvar_loss": 0,
    "minimum_cash_buffer": 20,
    "risk_aversion": 0,
    "scenario_count": 2,
    "stage_count": 2,
    "starting_cash": 50,
    "tail_probability": 0.4,
    "terminal_value_floor": 0
  },
  "decision": "time_consistent_capital_policy_supported",
  "guardrails": [
    "Every selected action is attached to an observable scenario-tree node and therefore shared by all descendant scenarios. The optimizer never chooses a different action for futures that are indistinguishable at decision time.",
    "Nested conditional CVaR is recomputed from terminal loss through every information stage, avoiding the time inconsistency of optimizing one static terminal tail measure and later reversing the policy. Probabilities, cash effects, value effects and transaction costs still require local prospective calibration.",
    "Exact enumeration certifies only the declared finite action/tree model. Beam search explicitly withholds global optimality; neither mode establishes legal financing availability, causal effects, solvency or permission to execute without accountable finance and technology owners."
  ],
  "method": "nonanticipative_multistage_capital_policy_with_nested_cvar",
  "scenario_diagnostics": [
    {
      "liquidity_breach": false,
      "loss": 0,
      "minimum_cash": 28,
      "node_path": [
        "root",
        "adverse"
      ],
      "probability": 0.4,
      "scenario_id": "adverse",
      "selected_action_ids": [
        "adverse-bridge"
      ],
      "terminal_value": 38
    },
    {
      "liquidity_breach": false,
      "loss": 0,
      "minimum_cash": 50,
      "node_path": [
        "root",
        "favorable"
      ],

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 one rooted terminal scenario tree with joint probabilities, ordered node paths, base cash flows and terminal values; align every candidate action's cash, value and transaction-cost effects to that common scenario order.
2. 2 Generate budget/capacity-feasible action bundles at each observable node, enforce same-node and ancestor dependencies plus pathwise exclusions, and evaluate each policy by applying one shared node decision to all descendant futures.
3. 3 Roll cash pathwise, calculate terminal net value and monetary shortfall, recursively compose conditional tail-CVaR from leaves to root, enforce liquidity chance and nested-risk gates, then maximize expected terminal value less governed risk aversion using exact enumeration or a deterministic disclosed beam.

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": "optimize a finite multistage capital policy" }
  → finds "optimize_time_consistent_capital_policy"

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

gitrevio_capability_run
  { "capability_id": "optimize_time_consistent_capital_policy", "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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