reasoning-inductive

name	reasoning-inductive
description	Extract patterns and generalizations from multiple observations. Use when detecting recurring themes, building predictive rules, or identifying systemic behaviors from accumulated data. Produces validated patterns with confidence bounds and exception handling.

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 structure multiple instances for analysis.

Minimum Sample Requirements:

Confidence Target	Minimum N	Notes
Exploratory	3-5	Hypothesis generation only
Tentative	6-10	Directional confidence
Moderate	11-20	Actionable patterns
High	21+	Strong generalizations

Components:

observations:
  dataset:
    - instance_id: "deal-001"
      timestamp: ISO8601
      context: "Enterprise sales"
      attributes:
        deal_size: 400000
        sales_cycle: 120
        stalled_at: "legal_review"
        outcome: "won"
        
    - instance_id: "deal-002"
      timestamp: ISO8601
      context: "Enterprise sales"
      attributes:
        deal_size: 350000
        sales_cycle: 150
        stalled_at: "legal_review"
        outcome: "lost"
        
    # ... more instances
    
  metadata:
    total_instances: 12
    time_range: "Q3-Q4 2024"
    source: "threads/sales/*/6-learning.md"
    collection_method: "automated scan"
    
  quality:
    completeness: 0.92  # % of fields populated
    consistency: 0.88   # % following same schema
    recency:        

Stage 2: Pattern Detection

Purpose: Identify regularities in the dataset.

Pattern Types:

Type	Description	Example
Frequency	How often X occurs	"7/12 deals stall at legal"
Correlation	X and Y co-occur	"Large deals AND long cycles"
Sequence	X follows Y	"Stall → lose within 30 days"
Cluster	Groups emerge	"Two deal archetypes exist"
Trend	Direction over time	"Cycles getting longer"
Threshold	Breakpoint exists	"Deals >$300K behave differently"

Detection Process:

patterns:
  detected:
    - pattern_id: P1
      type: frequency
      description: "Legal review stalls"
      evidence: "7 of 12 deals (58%) stalled at legal review"
      strength: 0.78
      
    - pattern_id: P2
      type: correlation
      description: "Deal size correlates with cycle length"
      evidence: "r=0.72 between deal_size and sales_cycle"
      strength: 0.72
      
    - pattern_id: P3
      type: threshold
      description: "CFO involvement threshold"
      evidence: "Deals >$250K require CFO, adding 30+ days"
      strength: 0.85
      
    - pattern_id: P4
      type: sequence
      description: "Stall duration predicts outcome"
      evidence: "Stalls >21 days → 80% loss rate"
      strength: 0.80
      
  rejected:
    - pattern: "Industry affects outcome"
      reason: 
      
  
      
       

Stage 3: Generalization

Purpose: Form rules from validated patterns.

Rule Formation:

generalizations:
  rules:
    - rule_id: R1
      statement: "Enterprise deals >$250K require CFO approval, adding 30+ days to cycle"
      derived_from: [P2, P3]
      
      structure:
        condition: "deal_size > 250000"
        prediction: "sales_cycle += 30 days"
        mechanism: "CFO approval requirement"
        
      applicability:
        domain: "Enterprise sales"
        segments: ["all enterprise"]
        exceptions: ["existing customers with MSA"]
        
    - rule_id: R2
      statement: "Legal review stalls >21 days predict deal loss with 80% probability"
      derived_from: [P1, P4]
      
      structure:
        condition: "stall_duration > 21 AND stall_stage = 'legal'"
        prediction: "outcome = 'lost' (p=0.80)"
        mechanism: "Budget cycle expiration, champion fatigue"
        
      applicability:
        domain: "Enterprise sales"
        segments: ["new customers"]
        exceptions: ["government deals with known long cycles"]
        
    -  
       
       []
      
      
         
         
         
        
      
         
         []
         []

Stage 4: Confidence Bounds

Purpose: Quantify reliability and identify exceptions.

Confidence Calculation:

Confidence = f(sample_size, pattern_strength, consistency, recency)

Base confidence from sample size:
  N < 5:   max 0.40
  N 5-10:  max 0.60
  N 11-20: max 0.80
  N > 20:  max 0.95

Adjustments:
  × pattern_strength (0-1)
  × consistency (0-1)
  × recency_weight (0.5-1.0)

Components:

confidence_analysis:
  rules:
    - rule_id: R1
      confidence: 0.72
      calculation:
        base: 0.80        # N=12, moderate sample
        strength: 0.85    # Strong pattern
        consistency: 0.88 # Good data quality
        recency: 0.95     # Recent data
        final: 0.72       # base × min(strength, consistency, recency)
        
      bounds:
        lower: 0.58       # Pessimistic estimate
        upper: 0.82       # Optimistic estimate
        
      exceptions:
        identified:
          - "Existing customer deal closed in 45 days despite $400K size"
            explanation: "Pre-existing MSA eliminated legal review"
          - "Government deal took 180 days but won"
            explanation: "Known government procurement cycle"
        exception_rate: 0.17  # 2/12 instances
        
      validity:
        expires: "2025-06-01"  # Re-validate after 6 months
        invalidated_by: 
           
           
        
           
           
          
      
       
      

Output Summary:

inductive_output:
  summary:
    rules_generated: 3
    highest_confidence: R1 (0.72)
    total_observations: 12
    time_range: "Q3-Q4 2024"
    
  actionable_rules:
    - rule: R1
      action: "Add 30 days to forecast for deals >$250K"
      confidence: 0.72
      
    - rule: R2
      action: "Escalate intervention when legal stall exceeds 14 days"
      confidence: 0.68
      
  tentative_rules:
    - rule: R3
      action: "Plan for legal stall in 60% of deals (resource accordingly)"
      confidence: 0.55
      needs: "5+ more observations to reach actionable confidence"
      
  canvas_implications:
    validate:
      - assumption: "A4: Enterprise sales cycle is 90 days"
        finding: "Actually 120 days for deals >$250K"
        action: "Update assumption"
        
    new_hypothesis:
      - "H17: Standard contract template would reduce legal stalls by 50%"
        basis: 
         

Quality Gates

Gate	Requirement	Failure Action
Sample size	≥5 instances	Collect more data
Data quality	≥80% completeness	Clean dataset
Pattern strength	≥0.6 for at least one	Lower threshold or collect more
Exception rate	<30% for actionable rules	Narrow rule scope
Mechanism identified	Plausible explanation	Add abductive analysis

Common Failure Modes

Failure	Symptom	Fix
Small N	High variance, unstable patterns	Wait for more data
Survivorship bias	Only successful cases analyzed	Include failures
Confounding	Correlation ≠ causation	Test mechanism with intervention
Overfitting	Rule too specific to sample	Simplify rule, test holdout
Recency bias	Old patterns weighted equally	Apply recency weighting
Cherry-picking	Only confirming instances	Systematic collection

Pattern Validation

Before promoting rule to actionable:

Statistical Validation

validation:
  method: "holdout"
  training_set: 8 instances
  test_set: 4 instances
  rule_accuracy_on_test: 0.75
  passed: true

Causal Validation

validation:
  method: "mechanism_test"
  proposed_mechanism: "CFO approval adds 30 days"
  test: "Interview 3 CFOs about approval process"
  result: "Confirmed - CFO review averages 25-35 days"
  passed: true

Temporal Validation

validation:
  method: "stability_check"
  pattern_in_Q3: 0.62
  pattern_in_Q4: 0.54
  drift: -0.08 (acceptable)
  passed: true

Automated Pattern Detection

For continuous learning, run inductive scans:

automated_scan:
  frequency: weekly
  sources:
    - "threads/sales/*/6-learning.md"
    - "threads/marketing/*/6-learning.md"
    - "threads/operations/*/6-learning.md"
    
  thresholds:
    min_instances: 5
    min_pattern_strength: 0.6
    
  output:
    location: "ops/patterns.md"
    alert_threshold: 0.75  # Flag high-confidence new patterns
    
  actions:
    new_pattern_detected: "Flag in ops/today.md for review"
    existing_pattern_strengthened: "Update confidence, log"
    pattern_invalidated: "Alert, review rule"

Output Contract

inductive_output:
  observations:
    count: int
    time_range: string
    sources: [string]
    quality_score: float
    
  patterns:
    detected: [{
      pattern_id: string
      type: frequency | correlation | sequence | cluster | trend | threshold
      description: string
      strength: float
      evidence: string
    }]
    rejected: [{pattern: string, reason: string}]
    
  rules:
    - rule_id: string
      statement: string
      confidence: float
      bounds: {lower: float, upper: float}
      exceptions: [{instance: string, explanation: string}]
      applicability: { ,  [],  []}
       { ,  []}
      
  
     [{ ,  ,  }]
     [{ ,  ,  }]
     [{ ,  ,  }]
    
  
     []        
     []          
     []          
    
  
     
     []
    
  
     
     
     

Example Execution

Context: "Review last 6 months of marketing content performance"

Stage 1 - Observations:

Collected: 24 content pieces
Sources: threads/marketing/*/6-learning.md
Attributes: topic, format, channel, sessions, conversions, time_to_demo
Quality: 0.88 completeness

Stage 2 - Patterns:

P1 (frequency): Case studies convert 2.3x average (8/24, all above average)
P2 (correlation): Technical depth correlates with enterprise demos (r=0.68)
P3 (threshold): Posts >2000 words perform better on SEO (breakpoint identified)
P4 (trend): LinkedIn declining, organic search rising over 6 months

Stage 3 - Generalizations:

R1: "Case studies should be prioritized for bottom-funnel conversion"
    Confidence: 0.75, based on 8 instances

R2: "Technical content attracts enterprise prospects"
    Confidence: 0.68, based on correlation analysis

R3: "SEO content should target >2000 words"
    Confidence: 0.70, based on threshold analysis

Stage 4 - Confidence Bounds:

R1: 0.75 [0.62, 0.85] - Actionable
R2: 0.68 [0.54, 0.78] - Actionable with caution
R3: 0.70 [0.58, 0.80] - Actionable

Canvas update: 
  - Validate H8 (case studies convert)
  - New H18: "Long-form SEO content drives organic growth"
  
Action: Shift content mix toward case studies and long-form technical guides