Estimate real option abandonment boundary

What it's for

Gives leaders an evidence-based stop/continue boundary for troubled investments, valuing the option to abandon without letting sunk cost or an overfit simulation dictate the decision.

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.

{
  "abandonment_costs": [
    0,
    0,
    0
  ],
  "cross_fit_folds": 5,
  "current_projects": [
    {
      "id": "weak-case",
      "period": 1,
      "project_value": 20,
      "remaining_cost": 40
    },
    {
      "id": "strong-case",
      "period": 1,
      "project_value": 110,
      "remaining_cost": 40
    }
  ],
  "minimum_option_uplift": 1,
  "paths": [
    {
      "continue_cash_flow_path": [
        0,
        0,
        0
      ],
      "id": "option-path-000",
      "project_value_path": [
        20,
        20,
        20
      ],
      "remaining_cost_path": [
        60,
        40,
        20
      ]
    },
    {
      "continue_cash_flow_path": [
        0,

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": [
    "Paths are coherent prospective simulations or point-in-time historical trajectories under the contemplated continuation policy; project value, remaining cost, continue cash flow, salvage, abandonment cost, period cadence, discounting, and terminal completion value share one governed horizon and value unit.",
    "Continuation value is approximated by a quadratic least-squares Monte Carlo surface in project value and remaining cost; exercise decisions use out-of-fold predictions, while published current-state boundaries use the full fitted surface and explicitly flag state-range extrapolation and nonmonotonicity.",
    "The option uplift comparison is against forced continuation on the same paths and requires a confidence lower bound; it does not prove the path generator, causal effects, salvage executability, legal ability to stop, or absence of restart, reputation, dependency, and workforce consequences.",
    "Projects and paths are aggregate investment cases, never named employees; sunk cost is excluded from forward continuation value, and the output is decision support for accountable stage-gate owners rather than an automatic shutdown, funding, employment, or investor instruction."
  ],
  "boundary_diagnostics": [
    {
      "boundary_project_value": null,
      "boundary_status": "continue_through_observed_value_grid",
      "continuation_state_transitions": 0,
      "observed_project_value_range": [
        20,
        100
      ],
      "period": 0,
      "remaining_cost": 60
    },
    {
      "boundary_project_value": null,
      "boundary_status": "continue_through_observed_value_grid",
      "continuation_state_transitions": 0,
      "observed_project_value_range": [
        20,
        100
      ],
      "period": 0,
      "remaining_cost": 60
    },
    {
      "boundary_project_value": null,
      "boundary_status": "continue_through_observed_value_grid",
      "continuation_state_transitions": 0,
      "observed_project_value_range": [
        20,
        100
      ],
      "period": 0,
      "remaining_cost": 60
    },
    {
      "boundary_project_value": null,
      "boundary_status": "continue_through_observed_value_grid",

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 coherent prospective paths for project value, remaining cost and continuation cash flow, plus executable salvage and abandonment cost by period; exclude sunk cost and preserve the same value unit and horizon.
2. 2 Work backward from terminal completion versus abandonment, fitting quadratic continuation-value surfaces on training folds and making every simulated stopping decision with held-out predictions to limit in-sample policy optimism.
3. 3 Compare the learned stopping policy with forced continuation on identical paths, require a confidence lower bound on option uplift, publish exercise timing and current-project actions, and flag extrapolation or nonmonotone boundary surfaces.

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": "learn a continuousstate project abandonment policy" }
  → finds "estimate_real_option_abandonment_boundary"

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

gitrevio_capability_run
  { "capability_id": "estimate_real_option_abandonment_boundary", "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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