yogsoth-ai Strategy: Morphological Scenario
Methodology
General Morphological Analysis (Zwicky) combined with Cross-Consistency Assessment (Ritchey). Systematically enumerate all possible combinations of key uncertainty parameters, filter for internal consistency, and assess surviving configurations as plausible scenarios.
Key principles:
- Completeness: Every relevant parameter dimension is included
- MECE values: Each parameter has mutually exclusive, collectively exhaustive values
- Pairwise consistency: Filter via CCA matrix before narrative construction
- Combinatorial discipline: Let the morphological field drive discovery, not intuition
Execution Flow
Identify drivers → spawn
scenario-driver-identification- Input: research context, planning horizon
- Output: 5-8 key uncertainty drivers
Enumerate parameters → spawn
parameter-enumeration- Input: driver list
- Output: Zwicky Box (parameter × value matrix)
Consistency filtering → spawn
consistency-pair-evaluation- Input: Zwicky Box
- Output: CCA matrix, surviving configurations
Narrative construction → spawn
scenario-narrative-construction(per surviving config)- Input: parameter configuration
- Output: scenario narrative
Impact assessment → spawn
scenario-impact-assessment(per scenario)- Input: scenario narrative, research approach
- Output: impact analysis
Robustness scoring → spawn
robustness-scoring- Input: all impact assessments
- Output: robustness index
Synthesis → spawn
scenario-synthesis- Input: all scenarios, robustness scores
- Output: final scenario portfolio report
Budget Gate
| Step | Token Budget | Notes |
|---|---|---|
| Driver identification | 8K | Single pass |
| Parameter enumeration | 10K | May iterate once |
| Consistency filtering | 15K | O(n²) pairwise |
| Narrative construction | 12K × N | N = surviving configs (typically 4-8) |
| Impact assessment | 10K × N | Per scenario |
| Robustness scoring | 8K | Aggregation |
| Synthesis | 12K | Final compilation |
Available Tactics
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use |
|---|---|
| cross-consistency-filtering | Orchestrates pairwise consistency evaluation and narrative construction to filter the morphological field |
| parameter-space-construction | Orchestrates driver identification and parameter enumeration to build the complete morphological field |
| strategy-robustness-testing | Orchestrates impact assessment and robustness scoring to evaluate research approach resilience across scenarios |
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| experiment-execution-consistency-pair-evaluation | Pairwise consistency assessment using Cross-Consistency Assessment (CCA) matrix |
| parameter-enumeration | Enumerate possible values for each uncertainty driver using MECE principles |
| robustness-scoring | Compute robustness index across scenarios with sensitivity analysis |
| scenario-driver-identification | Identify key uncertainty drivers using PESTEL framework scanning |
| scenario-impact-assessment | Assess each scenario's impact on the research approach across multiple dimensions |
| scenario-narrative-construction | Build rich narratives for surviving morphological configurations using Shell method |
| scenario-synthesis | Comprehensive scenario analysis report synthesizing all scenario work |