Solve entropic risk sensitive markov policy

What it's for

Prevents high-average recurring actions from winning when rare transition outcomes are catastrophic to the business.

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": [
    {
      "id": "risky",
      "transition_probabilities": {
        "catastrophe": {
          "catastrophe": 0.5,
          "stable": 0.5
        },
        "stable": {
          "catastrophe": 0.05,
          "stable": 0.95
        }
      },
      "transition_rewards": {
        "catastrophe": {
          "catastrophe": -10,
          "stable": 0
        },
        "stable": {
          "catastrophe": -50,
          "stable": 5
        }
      }
    },
    {
      "id": "safe",
      "transition_probabilities": {
        "catastrophe": {
          "catastrophe": 0.1,
          "stable": 0.9
        },
        "stable": {
          "catastrophe": 0,
          "stable": 1
        }
      },
      "transition_rewards": {
        "catastrophe": {
          "catastrophe": -2,
          "stable": 0
        },
        "stable": {
          "catastrophe": -5,

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": [
    "States are Markov-sufficient, transition probabilities and transition rewards are stationary within the finite horizon, and rewards are commensurable and additive.",
    "Exponential utility represents constant absolute risk aversion; the declared coefficient governs downside sensitivity and must be approved by the decision owner.",
    "Entropic dynamic programming is time-consistent but is not the same objective as static CVaR; Monte Carlo lower-tail CVaR is reported as an interpretable audit cross-check.",
    "Policies govern reversible team, project, service, or portfolio actions and must not automate person-level employment or surveillance decisions."
  ],
  "decision": "risk_sensitive_policy_differs",
  "initial_value": {
    "risk_aversion": 0.1,
    "risk_neutral_expected_value": 8.6199,
    "risk_sensitive_certainty_equivalent": 4
  },
  "method": "finite_horizon_entropic_risk_sensitive_markov_control_v1",
  "policy": [
    {
      "period": 0,
      "policy_changed_by_tail_risk": true,
      "risk_neutral_action": "risky",
      "risk_sensitive_action": "safe",
      "state_id": "stable"
    },
    {
      "period": 0,
      "policy_changed_by_tail_risk": false,
      "risk_neutral_action": "safe",
      "risk_sensitive_action": "safe",
      "state_id": "catastrophe"
    },
    {
      "period": 1,
      "policy_changed_by_tail_risk": true,
      "risk_neutral_action": "risky",
      "risk_sensitive_action": "safe",
      "state_id": "stable"
    },
    {
      "period": 1,
      "policy_changed_by_tail_risk": false,
      "risk_neutral_action": "safe",
      "risk_sensitive_action": "safe",
      "state_id": "catastrophe"
    },
    {

How it works

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

1. 1 Solve a finite-horizon Markov policy under exponential downside utility and compare it with the risk-neutral policy using paired Monte Carlo lower-tail CVaR.
2. 2 Evaluate the method-specific diagnostics and gates returned by the function, then abstain unless the declared decision clears them under locally governed thresholds.

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": "solve a finitehorizon markov policy under" }
  → finds "solve_entropic_risk_sensitive_markov_policy"

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

gitrevio_capability_run
  { "capability_id": "solve_entropic_risk_sensitive_markov_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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