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 |
1---2name: inductive-hypothesis-generation3description: Strategy: induce and distill hypotheses from data/observations4---56# Inductive Hypothesis Generation78Induce 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.910## When to Use1112- The domain lacks mature theory but has accumulated abundant empirical observations or data patterns13- The research gap appears as "a recurring phenomenon with no systematic explanation yet"14- The goal is to distill regularities from data, laying a foundation for subsequent theory construction15- An exploratory research stage where it is not yet clear which variables matter1617Not applicable: domains that already have a clear theoretical framework → use deductive-hypothesis-generation instead.1819## Thinking Framework2021**Observe patterns → Extract regularity → Generalize cautiously → Formulate testable claim**2223The core logic of induction:24251. **Observe patterns**: systematically organize patterns that recur across existing observations, data, and cases (not single anomalies)262. **Extract regularity**: identify the regularity behind the patterns — under what conditions it appears, under what conditions it does not273. **Generalize cautiously**: cautiously generalize the regularity from the specific samples — make the boundary of generalization explicit, do not over-extrapolate284. **Formulate testable claim**: turn the generalized regularity into a proposition that can be tested on new samples2930**The core risk of induction**: over-generalization (jumping from a limited sample to a universal law). Each inductive hypothesis must make explicit:31- Which samples the observations come from (sample characteristics, source, time range)32- Which population it generalizes to (the boundary of generalization scope)33- What evidence would limit or refute the generalization3435## Budget Gate3637| Tier | Pattern coverage | Regularity extraction | Hypothesis yield | Generalization boundary |38|------|---------|---------|---------|---------|39| S | ≥3 independent observation patterns | ≥2 regularities | ≥2 structured hypotheses | Each hypothesis specifies its sample source |40| M | ≥5 independent observation patterns | ≥3 regularities | ≥3 structured hypotheses | Generalization boundary + falsification scenario |41| L | ≥8 independent observation patterns | ≥5 regularities | ≥4 structured hypotheses | Complete generalization boundary + comparison of competing regularities |4243## Default Reference Flow44451. Invoke the `anomaly-characterization` SOP: systematically organize the patterns in existing observations/data (including frequency, conditions, exceptions)462. Invoke the `explanation-generation` SOP (via the `anomaly-driven-abduction` tactic): generate candidate regularity explanations for each pattern473. Invoke the `variable-identification` SOP: turn the constructs in the regularities into operationalizable variables484. Invoke the `relationship-specification` SOP: specify the directional relationships between variables (including moderating conditions)495. Invoke the `falsifiability-check` SOP (via the `falsifiability-audit` tactic): generate a falsification scenario + generalization boundary for each hypothesis5051## context-checkpoint5253Record after each round:54- The list of organized observation patterns (pattern description, source, frequency of occurrence)55- The list of extracted regularities (regularity statement, supporting patterns, exceptions)56- The current draft hypothesis set (including generalization-boundary statements)57- Falsifiability status + over-generalization risk assessment5859<!-- BEGIN available-tables (generated) -->6061## Available Tactics6263Optional, no fixed order; the final leaf is always a sop.6465| Tactic | When to use |66| --- | --- |67| anomaly-driven-abduction | Tactic: Inductive/abductive path — describe anomalous phenomena, generate candidate explanations, rank by plausibility |68| falsifiability-audit | Tactic: hypothesis quality assurance — check falsifiability, repair failing hypotheses, complete operationalization and boundary-condition specification |6970## Available SOPs7172Optional, no fixed order; the final leaf is always a sop.7374| SOP | When to use |75| --- | --- |76| hypothesis-formation-variable-identification | SOP: identify variables and their roles within a hypothesis |77| relationship-specification | SOP: specify the direction and form of relationships between variables |7879<!-- END available-tables (generated) -->
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