MENTAL MODEL · M13248

Inductive Logic

StructureHigh supportLogic

Included

Updated 2026-09-10

Loading revision record…

INTRODUCTION

English translation pending.

CORE DEFINITION

Inductive logic is a system of reasoning that characterizes, by evaluating arguments, how statements of evidence affect the truth of hypotheses. Unlike deductive logic, the truth of the premises of a good inductive argument does not guarantee the truth of the conclusion, but gives it some degree of support. Its central tool is the Bayesian account of confirmation and likelihood.

SCAFFOLDING EFFECT

psychology

Reduce cognitive load

- Assess evidence: use likelihood ratios to measure how strongly a piece of evidence supports competing hypotheses. - Update belief: convert new evidence into a shift in confidence using Bayes's theorem. - Extrapolate: estimate from a sample the probability that an unobserved case falls into some class.

anchor

Anchor fast decisions

Inductive support is formalized as a probabilistic support function: given background assumptions and evidence, the credence of a hypothesis is updated by Bayes's theorem, and the discriminating power of the evidence shows up in the likelihood ratio, the ratio of support the same evidence gives to competing hypotheses. Support is therefore not two-valued but changes continuously as evidence accumulates.

MINIMUM ACTION

In progress 0/1

Practice this model in one real situation:

List the hypothesis to be tested and several rival hypotheses. 2. Specify the evidence and estimate the likelihood of that evidence under each hypothesis. 3. Update confidence with the ratio or odds form of Bayes's theorem. 4. Check how sensitive the conclusion is to the prior and to auxiliary assumptions.

Check to track your progress (stored locally)

Learning progress0%

account_treeGenealogyexpand_more

menu_bookReferencesexpand_more

Source support: Explicit

link
plato.stanford.eduhttps://plato.stanford.edu/entries/logic-inductive/ZH · Explicit
verified

RELATED MODELS