Explanatory Induction
Updated 2026-08-17
INTRODUCTION
English translation pending.
CORE DEFINITION
Explanatory induction is the move from observed phenomena to a general principle that explains them, not merely summarizes them. Ordinary induction reports what co-varies with what; explanatory induction asks why the co-variation occurs and proposes a mechanism that would produce it. The mechanism is what makes the generalization scientific, because it implies new predictions that can fail, so the hypothesis is falsifiable rather than decorative. Mill's methods, hypothesis deduction, and the search for causes all belong to this family, which is why a good explanation is worth more than a pile of consistent observations.
SCAFFOLDING EFFECT
Reduce cognitive load
- Ask why it holds: after any correlation, demand the mechanism that would generate it. - Derive a testable prediction: state what new cases must show if the mechanism is real. - Welcome the counterexample: let a disconfirming case revise or retire the hypothesis instead of explaining it away.
Anchor fast decisions
A mechanism links a pattern to the process that produces it, and that link is what turns description into science. Once the generative process is stated, it implies further observations that the original data did not contain, so the hypothesis becomes checkable rather than merely consistent. A mechanism also survives contact with new cases, because it explains both the instances that fit and, when revised, the ones that do not. This is why explanatory induction resists the corruptions of pure pattern-finding: it asks the claim to answer for itself.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/Abductive_reasoningverified
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