Estimate value of flexibility

Value a strategy's pre-action signal-contingent flexibility against its best fixed action, including enablement cost, tail loss, and perfect-information headroom.

Statistical audit & measurement Estimate CTOs Tech CEOs IT managers Team leads Investors

What it's for

Puts a defensible price on architecture, contracts, staffing, and roadmap choices that preserve the ability to pivot when an early signal arrives.

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.

Field Type Role Required
max_detail_rows integer ≥ 1, ≤ 500 Numerical control Optional
maximum_cvar_loss_increase number ≥ 0 Your calibration Optional
minimum_net_flexibility_value number ≥ 0 Your calibration Optional
scenarios array of objects (3 fields) ≥ 2 items Evidence Yes
strategies array of objects (4 fields) ≥ 1 item Evidence Yes
tail_probability number ≥ 0.001, ≤ 0.5 Your calibration Optional

Each strategies record

Field Type Required
actions array of objects (3 fields) (≥ 2 items) Yes
baseline_action_id string (non-empty) Yes
flexibility_enablement_cost number (≥ 0) Yes
id string (non-empty) Yes
Example input 
{
  "maximum_cvar_loss_increase": 20,
  "minimum_net_flexibility_value": 5,
  "scenarios": [
    {
      "id": "high-high",
      "probability": 0.25,
      "signal_id": "high"
    },
    {
      "id": "high-mid",
      "probability": 0.25,
      "signal_id": "high"
    },
    {
      "id": "low-low",
      "probability": 0.25,
      "signal_id": "low"
    },
    {
      "id": "low-mid",
      "probability": 0.25,
      "signal_id": "low"
    }
  ],
  "strategies": [
    {
      "actions": [
        {
          "id": "commit",
          "value_scenarios": [
            100,
            80,
            -50,
            -40
          ]
        },
        {
          "id": "pivot",
          "value_scenarios": [
            40,
            50,
            30,
            20

Truncated for display — the full payload is 63 lines.

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.

Example output 
{
  "assumptions": [
    "Each signal is observed before the recourse action and partitions coherent joint scenarios without revealing more than its declared information; scenario probabilities and action values share one horizon and value unit.",
    "Every action is genuinely feasible after every signal unless represented as a separate strategy, exercise and enablement costs are incremental and complete, and value scenarios already include switching delay, lock-in, migration, reliability, and opportunity effects that matter.",
    "Flexibility value is measured against the best fixed action, not merely a weak nominated baseline; perfect-information value is an upper bound and remaining value is not evidence that another signal can actually be purchased.",
    "The result values aggregate technical or commercial recourse and does not justify breaking commitments, ignoring legal/accounting constraints, or taking action toward named employees or counterparties without accountable review."
  ],
  "configuration": {
    "maximum_cvar_loss_increase": 20,
    "minimum_net_flexibility_value": 5,
    "signal_timing_requirement": "observed_before_recourse_action",
    "tail_probability": 0.25
  },
  "decision": "positive_net_flexibility_value_supported",
  "method": "partial_information_contingent_recourse_value_v1",
  "strategy_diagnostics": [
    {
      "baseline_action_id": "commit",
      "baseline_cvar_loss": 50,
      "baseline_expected_value": 22.5,
      "best_rigid_action_id": "pivot",
      "best_rigid_cvar_loss": -20,
      "best_rigid_expected_value": 35,
      "contingent_policy": [
        {
          "conditional_expected_value": 90,
          "selected_action_id": "commit",
          "signal_id": "high",
          "signal_probability": 0.5
        },
        {
          "conditional_expected_value": 25,
          "selected_action_id": "pivot",
          "signal_id": "low",
          "signal_probability": 0.5
        }
      ],
      "cvar_loss_increase_vs_best_fixed": 10,
      "flexibility_enablement_cost": 10,
      "flexibility_supported": true,
      "flexible_policy_cvar_loss": -10,
      "gross_signal_value_over_best_fixed": 22.5,
      "net_flexibility_value_over_baseline": 25,
      "net_flexibility_value_over_best_fixed": 12.5,

Truncated for display — the full payload is 65 lines.

How it works

Statistical audit & measurement — Check whether a number is fit to decide on: coverage, timing, reconciliation, and the gaps a dashboard hides.

  1. 1 Freeze coherent joint scenarios, probabilities, signals observable before action, feasible post-signal actions, and all action, exercise, switching, and enablement values on one horizon.
  2. 2 Find the best fixed action ex ante, then choose the best feasible action conditional on each signal partition without using future information unavailable at action time.
  3. 3 Compare net contingent value and CVaR with the best fixed action, disclose the nominated-baseline comparison and perfect-information upper bound, and recommend only strategies clearing value and tail gates.

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.

Assumptions & guardrails

  • Metric definitions, weights, aggregate grain, sampling, missingness, dependence, and comparison windows correspond to the management claim being audited.
  • Signals precede recourse, scenario probabilities are coherent, values are commensurable, every listed action remains feasible in its signal state, and lock-in, migration, delay, reliability, legal, and opportunity effects are included.
  • Association, instability, or measurement quality is not a causal effect and must not be converted directly into an individual employment decision.
  • Flexibility is valuable only relative to the best fixed alternative after full cost; perfect-information headroom is an upper bound, not proof another signal is obtainable or that commitments may be ignored.

Minimum evidence

  • strategies: at least 1 rows/items
  • scenarios: at least 2 rows/items

How to validate it

Validate on future periods or held-out aggregate units, compare with a simple baseline, and require stability across plausible metric definitions and decision thresholds.

Calibrating it to your org

Same for everyone

The mathematical kernel, validation rules, method version, and JSON output semantics are organization-independent; no tenant-trained coefficients or company benchmark is embedded in the function.

Specific to you

  • coherent joint scenario distribution and signal partition whose information is demonstrably available before the recourse action
  • decision horizon, value unit, strategy/action feasibility, signal timing and quality, enablement and exercise cost perimeter, tail probability, minimum net flexibility value, and maximum tail-loss increase

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": "value a strategys preaction signalcontingent flexibility" }
  → finds "estimate_value_of_flexibility"

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

gitrevio_capability_run
  { "capability_id": "estimate_value_of_flexibility", "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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