Estimate platform network option value

What it's for

Quantifies the value of waiting for adoption momentum before funding a platform, while preserving a global optimality certificate inside the declared Markov model and exposing every assumption executives must calibrate.

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.

{
  "base_adoption_probability": 0.05,
  "contribution_value_per_adopter_per_period": 3,
  "eligible_units": 10,
  "horizon_periods": 4,
  "initial_adopters": 0,
  "network_log_odds_coefficient": 5,
  "platform_investment_cost": 30,
  "platform_operating_cost_per_period": 10,
  "post_investment_log_odds_lift": 1,
  "terminal_value_per_adopter": 2
}

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": {
    "base_adoption_probability": 0.05,
    "discount_rate_per_period": 0,
    "network_log_odds_coefficient": 5,
    "post_investment_log_odds_lift": 1,
    "solver_global_optimality_certificate": true,
    "state_count": 110,
    "transition_model": "binomial_nonadopter_conversion_with_logistic_network_effect"
  },
  "decision": "preserve_state_contingent_platform_option",
  "failed_gates": [],
  "guardrails": [
    "Adoption states are aggregate operational units, not people; the binomial/logistic transition law must be calibrated on comparable tenant cohorts and monitored for saturation, interference, selection and regime change.",
    "The network coefficient is a modeled adoption externality rather than causal proof; product quality, mandates, migration friction, capacity, multi-homing and competitor response require explicit evidence or scenarios.",
    "The exact dynamic program proves optimality only inside the submitted stationary Markov state, action, reward and horizon model; parameter uncertainty and omitted actions require separate sensitivity analysis.",
    "A positive platform option is not procurement, architecture, rollout, hiring, or employee-performance authority; security, compliance, finance, capacity and human approvals remain binding."
  ],
  "investment_timing_distribution": [
    {
      "period": 1,
      "probability_invest": 0.4013
    },
    {
      "period": 2,
      "probability_invest": 0.0069
    },
    {
      "period": 3,
      "probability_invest": 0.0037
    }
  ],
  "method": "exact_binomial_markov_platform_network_real_option_v1",
  "reachable_policy_states": [
    {
      "action": "wait",
      "adopters": 0,
      "inactive_state_value": 2.919,
      "period": 0,
      "platform_active": false,
      "state_probability": 1
    },
    {
      "action": "wait",

How it works

Markov & state-space control — Choose a policy over evolving states when today's action changes tomorrow's options.

1. 1 Define aggregate eligible operational units, initial adopters, horizon, baseline adoption odds, modeled network and investment lifts, contribution, operating cost, investment cost, terminal value, and governed value gates.
2. 2 Build exact binomial non-adopter transition distributions for each adopter state, then solve the finite-horizon active and inactive Bellman recursions to obtain the state-contingent invest-or-wait policy.
3. 3 Propagate state probabilities forward, reconcile expected NPV to the backward solution, compare against never and every fixed investment date, and report option value, investment timing, and final-adopter quantiles.

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": "value when to activate a shared" }
  → finds "estimate_platform_network_option_value"

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

gitrevio_capability_run
  { "capability_id": "estimate_platform_network_option_value", "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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