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Defeasible reasoning

Reasoning that is rationally compelling, though not deductively valid

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Through Reason, Deductive reasoning and Argument

Defeasible reasoning is a particular kind of non-demonstrative reasoning, where the reasoning does not produce a full, complete, or final demonstration of a claim, i.e., where fallibility and corrigibility of a conclusion are acknowledged. In other words, defeasible reasoning produces a contingent statement or claim. Defeasible reasoning is also a kind of ampliative reasoning because its conclusions reach beyond the pure meanings of the premises.

Defeasible reasoning finds its fullest expression in jurisprudence, ethics and moral philosophy, epistemology, pragmatics and conversational conventions in linguistics, constructivist decision theories, and in knowledge representation and planning in artificial intelligence. It is also closely identified with prima facie (presumptive) reasoning (i.e., reasoning on the "face" of evidence), and ceteris paribus (default) reasoning (i.e., reasoning, all things "being equal").

According to at least some schools of philosophy, all reasoning is at most defeasible, and there is no such thing as absolutely certain deductive reasoning, since it is impossible to be absolutely certain of all the facts, or to know with certainty that nothing is unknown. Thus all deductive reasoning is in reality contingent and defeasible.

Other kinds of non-demonstrative reasoning

Other kinds of non-demonstrative reasoning are probabilistic reasoning, inductive reasoning, statistical reasoning, abductive reasoning, and paraconsistent reasoning.

The differences between these kinds of reasoning correspond to differences about the conditional that each kind of reasoning uses, and on what premise (or on what authority) the conditional is adopted:

  • Deductive (from meaning postulate or axiom): if p then q (equivalent to q or not-p in classical logic, not necessarily in other logics)
  • Defeasible (from authority): if p then (defeasibly) q
  • Probabilistic (from combinatorics and indifference): if p then (probably) q
  • Statistical (from data and presumption): the frequency of qs among ps is high (or inference from a model fit to data); hence, (in the right context) if p then (probably) q
  • Inductive (theory formation; from data, coherence, simplicity, and confirmation): (inducibly) "if p then q"; hence, if p then (deducibly-but-revisably) q
  • Abductive (from data and theory): p and q are correlated, and q is sufficient for p; hence, if p then (abducibly) q as cause

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