yogsoth-ai Strategy: Factor-Level Design
Question: Which factors to test at what levels in combination?
Methodology
- Full Factorial (Fisher): All combinations of all factor levels. Gold standard but exponential cost.
- Fractional Factorial: Systematic subset using defining relations. Sacrifices high-order interactions.
- Plackett-Burman: Screening design for many factors. Identifies main effects only.
- Response Surface Methodology (RSM): Central Composite / Box-Behnken for optimization after screening.
- Taguchi Orthogonal Arrays: Robust design minimizing variance from noise factors.
Execution Flow
- factor-identification → Identify all independent, dependent, and control variables
- level-specification → Determine discrete levels for each factor (2-5 levels typical)
- budget-constrained-design (tactic) → Select design type given budget constraints
- design-matrix-construction → Build the actual design matrix
- metric-specification → Define primary/secondary metrics and significance thresholds
- sample-size-estimation → Power analysis to determine runs per cell
Budget Gate
| Design Type | Factors | Runs (k factors, 2 levels) | When to Use |
|---|---|---|---|
| Full Factorial | 2-4 | 2^k | Budget allows, need all interactions |
| Fractional (Res V) | 4-6 | 2^(k-1) | Need 2-factor interactions |
| Fractional (Res III) | 5-8 | 2^(k-p) | Screening, main effects only |
| Plackett-Burman | 8-15 | k+1 (nearest multiple of 4) | Many factors, screening phase |
| Taguchi L9/L18 | 4-8 | 9 or 18 | Robust design with noise factors |
| RSM (CCD) | 2-5 | 2^k + 2k + center | Optimization after screening |
Available Tactics
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use |
|---|---|
| budget-constrained-design | Optimize experiment design under compute and time budget constraints |
| statistical-method-selection | Select appropriate statistical methods for experiment analysis |
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 |
| experiment-config-generation | SOP: generate executable experiment configuration files |
| factor-identification | Identify independent, dependent, and control variables for an experiment |
| level-specification | Determine appropriate levels for each experimental factor |
| metric-specification | Define experiment metrics and significance standards |
| sample-size-estimation | SOP: power analysis and required experiment count estimation |