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:

1. Observe patterns: systematically organize patterns that recur across existing observations, data, and cases (not single anomalies)
2. Extract regularity: identify the regularity behind the patterns — under what conditions it appears, under what conditions it does not
3. Generalize cautiously: cautiously generalize the regularity from the specific samples — make the boundary of generalization explicit, do not over-extrapolate
4. 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

1. Invoke the anomaly-characterization SOP: systematically organize the patterns in existing observations/data (including frequency, conditions, exceptions)
2. Invoke the explanation-generation SOP (via the anomaly-driven-abduction tactic): generate candidate regularity explanations for each pattern
3. Invoke the variable-identification SOP: turn the constructs in the regularities into operationalizable variables
4. Invoke the relationship-specification SOP: specify the directional relationships between variables (including moderating conditions)
5. 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

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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 |

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