abductive

Abductive reasoning, or abduction, is a form of logical inference that begins with an observation or surprising fact and seeks the simplest, most plausible explanation. It differs from deduction, which derives conclusions guaranteed by premises, and from induction, which generalizes from particular instances. Abduction is often described as inference to the best explanation: from what is observed, generate a hypothesis that would, if true, best account for the data. The term was introduced by the American philosopher Charles S. Peirce in the late 19th century.

In practice, abductive reasoning involves proposing explanations and evaluating them for coherence, scope, and simplicity. It is iterative and fallible; the best explanation given current knowledge may be revised in light of new evidence. Abduction is widely used in science, medicine, law, and everyday problem solving to generate working hypotheses rather than definitive conclusions.

Examples include diagnosing a medical condition from symptoms, troubleshooting a malfunction by proposing a plausible fault, or inferring a historical cause from available records. In artificial intelligence and cognitive science, abductive inference is used to generate explanations for observed data and to support reasoning under uncertainty.

Limitations include ambiguity, underdetermination (several explanations may fit the data), and susceptibility to bias. Abduction does not guarantee truth; it aims for the most plausible explanation given current information. Researchers distinguish abductive reasoning from deduction and induction, and use it alongside other forms of reasoning to develop robust theories.