Abductive Reasoning
/æbˈdʌktɪv ˈriːzənɪŋ/
What is Abductive Reasoning?
Abductive reasoning is a form of logical inference that starts with an observation and seeks the simplest or most likely explanation. Unlike deductive reasoning (which moves from general rules to specific conclusions) or inductive reasoning (which generalizes from specific observations), abductive reasoning involves forming hypotheses to explain what we observe.
The Three Types of Reasoning
| Type | Direction | Example |
|---|---|---|
| Deductive | General → Specific | All humans are mortal. Socrates is human. Therefore, Socrates is mortal. |
| Inductive | Specific → General | Every swan I've seen is white. Therefore, all swans are white. |
| Abductive | Observation → Hypothesis | The lawn is wet. The best explanation is that it rained last night. |
Why Abductive Reasoning Matters for AI
According to Stanford professor Yejin Choi, current AI models are fundamentally limited because they primarily perform inductive and deductive reasoning, which she characterizes as "regurgitation of the same information that you already had."
Abductive reasoning, by contrast, is:
- Creative - It involves forming new hypotheses, not just applying existing rules
- What scientists do - Scientific discovery requires hypothesizing explanations for unexplained phenomena
- What detectives do - Sherlock Holmes' famous "deductions" are actually abductions
The Sherlock Holmes Misconception
Arthur Conan Doyle famously has Holmes declare "Elementary, my dear Watson" after making brilliant "deductions." But Yejin Choi points out this is a misnomer:
"When you look at detectives like Sherlock Holmes, the author incorrectly thinks the detective is doing deductive reasoning. But no, it's abductive reasoning because you have to jump to the conclusion with a bit of a leap of faith."
Holmes observes evidence (mud on shoes, calluses on hands) and abduces the most likely explanation. True deduction would require certainty; abduction embraces uncertainty and likelihood.
Implications for AI Development
The limitation Choi identifies is profound: current AI systems excel at interpolating within "the neighborhood of internet data" but struggle to discover genuinely new knowledge.
"There are truths about how to cure cancer that are not on the internet. The current way of doing curation of data isn't going to really teach the model how to come up with personalized drugs that could prevent or cure cancer."
For AI to make genuine scientific breakthroughs, it may need to develop robust abductive reasoning capabilities - the ability to hypothesize explanations for observations that don't fit existing patterns.
Historical Context
The term "abduction" in this sense was coined by American philosopher Charles Sanders Peirce (1839-1914), who distinguished it from the better-known forms of deduction and induction. Peirce considered abduction essential to scientific inquiry and creative problem-solving.
Related Reading
- Yejin Choi - Stanford professor researching reasoning in AI
- Reinforcement Learning - Another approach to model improvement that Choi critiques
Mentioned In
Yejin Choi at 00:18:30
"Abduction is this mental act of coming up with the best possible explanation of your partial observation. You're coming up with the hypothesis."
Yejin Choi at 00:19:15
"When you look at detectives like Sherlock Holmes, the author incorrectly thinks the detective is doing deductive reasoning. But no, it's abductive reasoning because you have to jump to the conclusion with a bit of a leap of faith."