deductive-hypothesis-generation

Research

Strategy: deduce hypotheses from existing theory

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Deductive Hypothesis Generation

Deduce hypotheses from existing theory: in domains with mature theory, transform theoretical propositions into specific testable predictions through an explicit reasoning chain.

When to Use

The domain has mature foundational theory (named theories, formal models, or accepted mechanisms)
The research gap manifests as "a discrepancy between theoretical prediction and real-world observation"
The goal is to test, extend, or delimit the scope of an existing theory
Highly defensible hypotheses are needed (reviewers will press for theoretical justification)

Not applicable: emerging domains rich in data but lacking theory → use inductive-hypothesis-generation instead.

Thinking Framework

Theory → Mechanism → Variable Relationship → Testable Prediction

The core logic of deduction:

Theory: identify the foundational theory underpinning the research question (named theory, formal model)
Mechanism: extract the causal mechanism from the theory (the intermediate process by which "X affects Y through Z")
Variable Relationship: translate the mechanism into directional relationships among variables (positive/negative/moderating/mediating)
Testable Prediction: concretize the variable relationship into an observable prediction under specific conditions

Every step must be traceable: each prediction traces back to a mechanism, each mechanism traces back to a theory. This is what distinguishes a deductive hypothesis from a guess.

Common pitfalls:

Theory citation that stays superficial (naming only, no specific propositions) → you must cite the theory's core propositions
Skipping the mechanism and jumping straight from theory to prediction → the mechanism is the key node of the deductive chain and cannot be omitted
Hypothesis scope too broad ("in all contexts") → deduction must state boundary conditions

Budget Gate

Tier	Theory coverage	Mechanism extraction	Hypothesis output	Falsifiability
S	≥2 named theories	≥3 causal mechanisms	≥2 structured hypotheses	1 falsification scenario per hypothesis
M	≥3 named theories	≥5 causal mechanisms	≥3 structured hypotheses	≥1 scenario + boundary conditions per hypothesis
L	≥5 named theories	≥8 causal mechanisms	≥5 structured hypotheses	full falsifiability audit + competing-theory comparison

Default Reference Flow

Call the theory-identification SOP: scan the domain literature and list the named theories relevant to the gap and their core propositions
Call the mechanism-extraction SOP (via the theory-mechanism-extraction tactic): extract causal mechanism chains from each theory
Call the variable-identification SOP: translate the constructs in the mechanisms into operational variables
Call the relationship-specification SOP: specify directional relationships among variables (including moderating/mediating structures)
Call the boundary-condition-specification SOP: identify the preconditions under which the theory applies (population, context, time range, etc.)
Call the falsifiability-check SOP (via the falsifiability-audit tactic): generate a falsification scenario for each hypothesis
Call the operationalization SOP: provide draft measurement methods for the key variables

context-checkpoint

After each round, record:

The list of identified theories (name, core proposition, source)
The list of extracted mechanisms (each tagged with its source theory)
The current set of hypothesis drafts (including variable relationships + boundary conditions)
Falsifiability status (passed / pending review / unfalsifiable, needs revision)

Available Tactics

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

Tactic	When to use
falsifiability-audit	Tactic: hypothesis quality assurance — check falsifiability, repair failing hypotheses, complete operationalization and boundary-condition specification
theory-mechanism-extraction	Tactic: Core of the deductive path — start from theory to extract mechanisms, variables, and relationships, generating hypothesis candidates