abductive-hypothesis-generation

Abductive Hypothesis Generation

Inference to the best explanation in the face of anomalies: when an anomalous phenomenon that existing theory cannot explain is observed, systematically generate candidate explanations and select the most plausible one as the hypothesis.

When to Use

• A clear anomalous phenomenon is observed (a result inconsistent with existing theoretical predictions)
• Existing theory cannot adequately explain a known phenomenon
• One of several competing explanations must be selected as the most worth testing
• The research starting point is "this result is strange, why?"

Not applicable: no clear anomaly, just wanting to explore a new field → use inductive-hypothesis-generation instead.

Thinking Framework

Anomaly → Generate candidate explanations → Rank by plausibility → Best explanation = hypothesis

The core logic of abductive reasoning:

1. Anomaly: precisely describe the anomaly — what phenomenon, inconsistent with what expectation, how large the deviation
2. Generate candidate explanations: systematically generate all candidate explanations that can account for the anomaly (no premature filtering)
3. Rank by plausibility: rank by plausibility — which explanation is most parsimonious, most consistent with known facts, most testable
4. Best explanation = hypothesis: select the most plausible explanation as the working hypothesis, retaining the rest as competing hypotheses

Core principles of abduction:

• Occam's razor: when explanatory power is comparable, prefer the explanation with fewer assumptions
• Consistency: the best explanation should not contradict other known facts
• Testability: the best explanation must be able to produce observable predictions (otherwise it cannot be verified)
• Generation completeness: candidate explanations must be exhausted before ranking, to avoid premature convergence

Budget Gate

| Tier | Anomaly description | Candidate explanations | Hypothesis output | Competing hypotheses | |------|---------|---------|---------|---------| | S | 1 precisely described anomaly | ≥2 candidate explanations | 1 best-explanation hypothesis | ≥1 competing hypothesis retained | | M | 1–2 anomalies | ≥3 candidate explanations | ≥2 structured hypotheses | complete plausibility ranking | | L | ≥2 related anomalies | ≥5 candidate explanations | ≥3 structured hypotheses | complete ranking + discriminating prediction design |

Default Reference Flow

1. Call the anomaly-characterization SOP: precisely describe the anomaly (phenomenon, expectation, deviation, excluded trivial explanations)
2. Call the explanation-generation SOP (via the anomaly-driven-abduction tactic): systematically generate candidate explanations (no premature filtering)
3. Call the plausibility-ranking SOP: rank candidate explanations by parsimony, consistency, and testability
4. Call the falsifiability-check SOP: generate a falsification scenario for the best explanation, confirming its testability

context-checkpoint

Record after each round:

• Anomaly description (precise version, with deviation quantification)
• Candidate explanation list (including excluded trivial explanations and exclusion reasons)
• Plausibility ranking result (including ranking basis)
• Best-explanation hypothesis + competing hypothesis list
• Discriminating predictions (what experiment can distinguish the best explanation from competing explanations)

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Available Tactics

Optional, no fixed order; the final leaf is always a sop.

| Tactic | When to use | | --- | --- | | anomaly-driven-abduction | Tactic: Inductive/abductive path — describe anomalous phenomena, generate candidate explanations, rank by plausibility |

Available SOPs

Optional, no fixed order; the final leaf is always a sop.

| SOP | When to use | | --- | --- | | falsifiability-check | SOP: check whether a hypothesis meets the falsifiability criterion |

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