feasibility-constrained-formulation

Research

Strategy: reshape a research question under resource constraints — pragmatic adjustment that preserves core value

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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