yogsoth-ai Feasibility-Constrained Formulation
Reshape a research question under constraints — when the ideal question exceeds available resources, pragmatically adjust it to be feasible while preserving core research value.
When to Use
- The ideal research question exceeds available resources (time/data/compute/manpower)
- A trade-off between ambition and feasibility is needed
- There are explicit constraints (deadline, budget, data availability)
Thinking Framework
Core logic: constraints are not the enemy, they are design parameters. Good reshaping under constraints = finding "the most valuable question answerable within these constraints."
Constraint Types
| Constraint | Adjustment strategy | Example |
|---|---|---|
| Insufficient time | narrow scope / use proxy metrics | 3 months → pilot study only |
| Data unavailable | switch data source / switch study object | cannot obtain X → use public dataset Y |
| Insufficient compute | simplify method / reduce scale | cannot train a large model → use fine-tuning |
| Insufficient expertise | narrow the domain / collaborate | no biology background → focus on the computational part |
Adjustment Principles
- Preserve the core: adjust scope and method, but keep the essence of the core research question
- Use proxies: if direct measurement is infeasible, find a reasonable proxy metric
- Phase it: split a big problem into pilot → full study
- Make trade-offs explicit: explicitly state what was given up due to constraints
Budget Gate
| Tier | Constraint analysis | Adjustment options | Output |
|---|---|---|---|
| S | list main constraints | 1 adjustment option | a feasible RQ |
| M | constraint classification + impact assessment | 2-3 adjustment options + comparison | best feasible RQ + trade-off statement |
| L | full constraint map + priorities | multiple options + Pareto analysis | best RQ under constraints + phased plan |
Default Reference Flow
- List all constraints (resources, time, data, capability)
- Assess each constraint's impact on the ideal RQ
- Design adjustment options (narrow/proxy/phase)
- Assess whether the adjusted RQ still has value
- FINER check (paying special attention to F = Feasible)
- Define success criteria
- Explicitly state trade-offs
context-checkpoint
After the Strategy completes, you must call context-checkpoint and record:
- The list of constraints
- Ideal RQ vs adjusted RQ
- The adjustment strategy and rationale
- The trade-off statement
- The final feasible RQ
Available Tactics
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use |
|---|---|
| question-refinement-loop | Tactic: iteratively refine a research question until it passes all 5 FINER criteria |