inductive-hypothesis-generation

Research

Strategy: induce and distill hypotheses from data/observations

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Inductive Hypothesis Generation

Induce and distill hypotheses from data/observations: in domains with theoretical gaps or insufficient theory, distill regularities from empirical patterns and cautiously generalize them into testable propositions.

When to Use

The domain lacks mature theory but has accumulated abundant empirical observations or data patterns
The research gap appears as "a recurring phenomenon with no systematic explanation yet"
The goal is to distill regularities from data, laying a foundation for subsequent theory construction
An exploratory research stage where it is not yet clear which variables matter

Not applicable: domains that already have a clear theoretical framework → use deductive-hypothesis-generation instead.

Thinking Framework

Observe patterns → Extract regularity → Generalize cautiously → Formulate testable claim

The core logic of induction:

Observe patterns: systematically organize patterns that recur across existing observations, data, and cases (not single anomalies)
Extract regularity: identify the regularity behind the patterns — under what conditions it appears, under what conditions it does not
Generalize cautiously: cautiously generalize the regularity from the specific samples — make the boundary of generalization explicit, do not over-extrapolate
Formulate testable claim: turn the generalized regularity into a proposition that can be tested on new samples

The core risk of induction: over-generalization (jumping from a limited sample to a universal law). Each inductive hypothesis must make explicit:

Which samples the observations come from (sample characteristics, source, time range)
Which population it generalizes to (the boundary of generalization scope)
What evidence would limit or refute the generalization

Budget Gate

Tier	Pattern coverage	Regularity extraction	Hypothesis yield	Generalization boundary
S	≥3 independent observation patterns	≥2 regularities	≥2 structured hypotheses	Each hypothesis specifies its sample source
M	≥5 independent observation patterns	≥3 regularities	≥3 structured hypotheses	Generalization boundary + falsification scenario
L	≥8 independent observation patterns	≥5 regularities	≥4 structured hypotheses	Complete generalization boundary + comparison of competing regularities

Default Reference Flow

Invoke the anomaly-characterization SOP: systematically organize the patterns in existing observations/data (including frequency, conditions, exceptions)
Invoke the explanation-generation SOP (via the anomaly-driven-abduction tactic): generate candidate regularity explanations for each pattern
Invoke the variable-identification SOP: turn the constructs in the regularities into operationalizable variables
Invoke the relationship-specification SOP: specify the directional relationships between variables (including moderating conditions)
Invoke the falsifiability-check SOP (via the falsifiability-audit tactic): generate a falsification scenario + generalization boundary for each hypothesis

context-checkpoint

Record after each round:

The list of organized observation patterns (pattern description, source, frequency of occurrence)
The list of extracted regularities (regularity statement, supporting patterns, exceptions)
The current draft hypothesis set (including generalization-boundary statements)
Falsifiability status + over-generalization risk assessment

Available Tactics

Optional, no fixed order; the final leaf is always a sop.

Tactic	When to use
anomaly-driven-abduction	Tactic: Inductive/abductive path — describe anomalous phenomena, generate candidate explanations, rank by plausibility
falsifiability-audit	Tactic: hypothesis quality assurance — check falsifiability, repair failing hypotheses, complete operationalization and boundary-condition specification

Available SOPs

Optional, no fixed order; the final leaf is always a sop.

SOP	When to use
hypothesis-formation-variable-identification	SOP: identify variables and their roles within a hypothesis
relationship-specification	SOP: specify the direction and form of relationships between variables