Tactic: Budget-Constrained Design
Orchestration Pattern
- Assess Budget → Determine available GPU-hours, wall-clock time, and cost ceiling
- factor-identification → Identify all candidate factors
- Estimate Cost Per Run → Calculate time/compute for a single experiment run
- Compute Maximum Runs → budget / cost_per_run = max feasible runs
- level-specification → Constrain levels to fit within run budget
- Select Design Type → Choose most information-efficient design for the budget
- design-matrix-construction → Build the constrained design matrix
Decision Criteria
| Available Runs | Recommended Approach |
|---|---|
| < 10 | One-factor-at-a-time or Plackett-Burman screening |
| 10-30 | Fractional factorial (Resolution III-IV) |
| 30-60 | Fractional factorial (Resolution V) or Taguchi |
| 60-120 | Full factorial on top factors + screening on rest |
| 120+ | Full factorial or RSM with replication |
Optimization Strategies
- Sequential Design: Run screening first, then detailed study on important factors
- Adaptive Allocation: Allocate more runs to high-variance conditions
- Early Stopping: Define stopping criteria for clearly dominated configurations
- Transfer from Pilot: Use pilot study results to inform main study design
- Shared Controls: Reuse control/baseline runs across multiple comparisons
Quality Checks
- Does the design have sufficient power for the primary hypothesis?
- Are the most important factors given priority in the allocation?
- Is there a contingency plan if budget is cut mid-experiment?
- Are early stopping criteria pre-defined (not post-hoc)?
- Is the design balanced despite budget constraints?
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| design-matrix-construction | Build the experiment design matrix with proper orthogonality and balance |
| factor-identification | Identify independent, dependent, and control variables for an experiment |
| level-specification | Determine appropriate levels for each experimental factor |