calculate_expected_value

shallow

io.github.simonmak-ascent/fair-value · Verify this server

Expected-value engine. Compute expected values over discrete, continuous, simulated, or tree-structured uncertainty, plus football-field ranges. Use this for generic probability weighting of scenarios, Monte-Carlo and decision trees; it does NOT perform IFRS/HKFRS measurement of provisions, insurance or employee-benefit obligations (use calculate_actuarial_pv), and it does not price path-dependent payoffs (use calculate_structured_product). Methods: discrete: outcomes, probabilities; continuous: distribution, mean, std, lower, upper; scenario: scenarios; monte_carlo: iterations, distributions, base_params, seed; decision_tree: tree; football_field: estimates. Supply exactly one method with its declared inputs (no defaults). An unknown method, a missing input, or an extra field returns an error envelope (code INVALID_ARGUMENT) instead of raising.

100.0/100

1 trials · measured 1 day ago

calculate_expected_value scores 100.0/100 on Vouch's measured behaviour index, from 1 real invocation trials against io.github.simonmak-ascent/fair-value, measured 6 Oct 2026 under methodology v0.2.0. Every measured component scored 100.

Component breakdown

ComponentWeightValue
Reliability35%not applicable
Schema integrity25%100.0
Failure behaviour15%not applicable
Latency15%not applicable
Concurrency10%not applicable

Tool details

Transport
remote + stdio
Credential class
self-provisionable
Input schema
not declared
Output schema
not declared
Side-effect classification
unclassified

Score history

DayScoreTierMethodology
2026-10-06100.0shallowv0.2.0

Probe evidence

ProbeOutcomes
schema_integritypass: 1

Raw request/response logs are not archived yet — the outcome counts above are drawn directly from every recorded trial.

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Vouch score: calculate_expected_value
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calculate_expected_value — Vouch