Optimize learning vs earning allocation

What it's for

Makes the exploration-versus-exploitation tradeoff explicit: when should scarce product or engineering capacity earn now, and when is one uncertain action worth trying because it improves later choices?

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.

{
  "decision_horizon_units": 5,
  "known_baseline_reward_per_unit": 0.6,
  "maximum_exact_states": 10000,
  "uncertain_actions": [
    {
      "cost_per_attempt": 0,
      "id": "new-channel",
      "posterior_alpha": 1,
      "posterior_beta": 1,
      "reward_per_success": 1
    }
  ]
}

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.

{
  "action_diagnostics": [
    {
      "action_id": "new-channel",
      "adaptive_option_value_over_commit": 0.5867,
      "commit_same_action_value": 2.5,
      "expected_units_under_optimal_policy": 2.4667,
      "initial_bayesian_q_value": 3.0867,
      "initial_expected_net_reward_per_unit": 0.5,
      "myopic_first": false,
      "posterior_success_probability": 0.5,
      "selected_first": true
    },
    {
      "action_id": "known-baseline",
      "adaptive_option_value_over_commit": 0.0333,
      "commit_same_action_value": 3,
      "expected_units_under_optimal_policy": 2.5333,
      "initial_bayesian_q_value": 3.0333,
      "initial_expected_net_reward_per_unit": 0.6,
      "myopic_first": true,
      "posterior_success_probability": null,
      "selected_first": false
    }
  ],
  "assumptions": [
    "Each uncertain action has a stationary Bernoulli success process with the submitted beta posterior, reward and attempt cost; every attempt produces both its declared immediate earning and an unbiased promptly observed outcome that updates the same future decision state.",
    "One capacity unit is allocated per decision step, the known baseline earns a certain reward and produces no learning, rewards are additive and comparable, and discounting captures timing; switching, batching, delays, constraints, correlated arms, nonstationarity, harms, censoring, and delayed feedback must be modeled elsewhere or make this policy invalid.",
    "The function solves the declared finite-horizon Bayesian bandit exactly inside a hard state budget and compares it with a recursively evaluated myopic policy; exploration value is a model-conditional option value, not causal proof or permission for an uncontrolled experiment.",
    "Actions are aggregate interventions, product choices, experiments, technologies, or investment channels, never named employees; the first allocation is governed decision support and requires safety, causal-design, finance, and accountable-owner approval before deployment."
  ],
  "configuration": {
    "discount_rate_per_unit": 0,
    "exact_dynamic_program": true,
    "known_baseline_reward_per_unit": 0.6,
    "maximum_exact_states": 10000,
    "minimum_expected_policy_value": 0,
    "posterior_updates_after_every_uncertain_action": true
  },
  "decision": "explore_uncertain_action_before_committing",
  "method": "exact_finite_horizon_beta_bernoulli_bandit_v1",
  "reachable_policy_state_diagnostics": [
    {
      "posterior_success_probabilities": {

How it works

Sequential Bayesian & bandits — Learn while deciding — update beliefs as evidence arrives and choose where the next unit of effort is worth spending.

1. 1 Freeze up to four aggregate uncertain actions with tenant-calibrated beta posteriors, reward per success and attempt cost, plus the certain baseline reward, finite capacity horizon, discounting and exact-state work budget.
2. 2 Use backward Bayesian dynamic programming over every reachable success/failure posterior state, comparing the known baseline with uncertain actions whose immediate reward and observation update all later choices.
3. 3 Evaluate the recursively adaptive policy against a recursively evaluated myopic policy, identify when first-step exploration overrides immediate earning, and reconcile expected capacity units across every reachable policy branch.

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 the exact finitehorizon betabernoulli bandit" }
  → finds "optimize_learning_vs_earning_allocation"

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

gitrevio_capability_run
  { "capability_id": "optimize_learning_vs_earning_allocation", "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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