optimize-design-under-budget git:20260923.cbc38aaA

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---
name: optimize-design-under-budget
description: "Given cost per run and resource budget, choose the most information-efficient feasible design while preserving essential validity constraints."
---

# optimize-design-under-budget

## Purpose

Given cost per run and resource budget, choose the most information-efficient feasible design while preserving essential validity constraints.

## Input contract

```yaml
required: [candidate_designs, run_costs, resource_budget, validity_constraints]
optional: [evidence, assumptions, prior_results]
constraints: [use named scientific objects; retain provenance and missingness; $\alpha$ = 0.05 and power = 0.8 where applicable]
```

## Procedure

1. Validate the typed inputs and state the decision this operation must support.
2. Apply the declared operation to the named object; record intermediate values that affect interpretation.
3. Check boundary conditions and counterexamples, then emit the result with uncertainty and source links.

## Output contract

```yaml
produces: [optimize_design_under_budget_result, evidence_trace, uncertainties]
delta_fields: [evidence_updates, uncertainties]
```

## Quality gates

- Inputs are named scientific objects with compatible schemas.
- Every material result has a derivation or source reference.
- Fixed statistical criteria remain exact where applicable: $\alpha$ 0.05 and power 0.8.

## Failure and counterexamples

Return a failed operation with the violated precondition when inputs are incomplete, assumptions are unsupported, or a counterexample defeats the result.

## Provenance map

- intermediate: experiment-execution/budget-constrained-design [tactic]