reasoning-inductivelisted

# Inductive Reasoning Generalize from instances to rules. The logic of pattern extraction and empirical learning. ## Type Signature ``` Inductive : [Observation] → Pattern → Generalization → ConfidenceBounds Where: Observations : [Instance] → Dataset Pattern : Dataset → (Regularity × Frequency) Generalization : (Regularity × Frequency) → Rule ConfidenceBounds : Rule × SampleSize → (Confidence × Exceptions) ``` ## When to Use **Use inductive when:** - Multiple similar observations accumulate - Looking for recurring patterns across threads - Building predictive rules from experience - Identifying systemic behaviors - Validating or discovering Canvas assumptions - "This keeps happening" situations **Don't use when:** - Explaining single observation → Use Abductive - Known causal chain exists → Use Causal - Transferring one case to another → Use Analogical - Resolving disagreement → Use Dialectical ## Distinction from Other Modes | Mode | Input | Output | Question | |------|-------|--------|----------| | **Abductive** | Single anomaly | Explanation | "Why did this happen?" | | **Inductive** | Multiple instances | Pattern/Rule | "What keeps happening?" | | **Analogical** | One source case | Transferred solution | "How is this like that?" | **Key difference from Abductive:** - Abductive: 1 observation → 1 explanation - Inductive: N observations → 1 generalization ## Four-Stage Process ### Stage 1: Observation Collection **Purpose:** Gather and st