reasoning-abductive

name	reasoning-abductive
description	Generate and evaluate explanatory hypotheses from incomplete observations. Use when diagnosing anomalies, explaining unexpected outcomes, or inferring causes from effects. Produces ranked hypotheses with evidence and confidence scores.

Abductive Reasoning

Generate best explanations from observations. The logic of diagnosis and inference to cause.

Type Signature

Abductive : Observation → Hypotheses[] → Evidence → BestExplanation

Where:
  Observation     : RawData × Surprise → AnomalyDescription
  Hypotheses      : AnomalyDescription → [PossibleCause]
  Evidence        : [PossibleCause] × AvailableData → [ScoredHypothesis]
  BestExplanation : [ScoredHypothesis] → (Cause × Confidence × NextSteps)

When to Use

Use abductive when:

• Anomaly or surprise present (metric deviation, unexpected behavior)
• Need to explain an observation (why did X happen?)
• Incomplete data, must infer cause
• Diagnostic context (errors, issues, failures)
• Multiple possible explanations exist

Don't use when:

• Cause is already known (use causal execution instead)
• Need to compare decision alternatives
• Resolving stakeholder disagreements

Four-Stage Process

Stage 1: Observation

Purpose: Transform raw data into structured anomaly description.

Input:

observation:
  raw_data: "Conversion dropped from 12% to 7% in Q4"
  context:
    timeframe: "Q4 2025"
    baseline: "12% historical average"
    current: "7% observed"
  surprise_level: 0.8  # How unexpected is this?

Process:

1. Quantify the deviation (% change, sigma from mean)
2. Identify temporal boundaries (when did it start?)
3. Scope the phenomenon (which segments affected?)
4. Rate surprise level (expected vs unexpected)

Output:

anomaly:
  description: "42% drop in conversion rate (12% → 7%)"
  deviation: "-5 percentage points, -42% relative"
  temporal: "Started week 3 of Q4, persists through Q4"
  scope: "All segments equally affected"
  surprise: 0.8
  baseline_source: "12-month rolling average"

Stage 2: Hypotheses Generation

Purpose: Generate diverse possible explanations without judgment.

Rules:

• Generate ≥5 hypotheses (more is better initially)
• Include obvious AND non-obvious causes
• Consider internal AND external factors
• Don't filter yet - cast wide net

Hypothesis Categories:

Category	Examples
Technical	Site issues, bugs, performance
Product	Features, pricing, positioning
Market	Competition, trends, seasonality
Operational	Team changes, process issues
External	Economy, regulations, events

Output:

hypotheses:
  - id: H1
    cause: "Website performance degradation"
    category: technical
    mechanism: "Slow load times → abandonment"
    
  - id: H2
    cause: "Competitor launched aggressive pricing"
    category: market
    mechanism: "Price undercut → customer diversion"
    
  - id: H3
    cause: "Seasonal Q4 shopping behavior change"
    category: market
    mechanism: "Holiday spending patterns differ"
    
  - id: H4
    cause: "Product-market fit weakening"
    category: product
    mechanism: "Customer needs evolving away"
    
  - id: H5
    cause: "Sales qualification criteria changed"
    category: operational
    mechanism: "Different lead quality entering funnel"
    
  # ... continue until exhaustive

Stage 3: Evidence Evaluation

Purpose: Score each hypothesis against available evidence.

For each hypothesis, evaluate:

Criterion	Question	Score
Explanatory power	Does it fully explain the anomaly?	0-1
Simplicity	Fewest assumptions required?	0-1
Coherence	Consistent with other known facts?	0-1
Testability	Can we verify/falsify it?	0-1
Prior probability	How likely independent of this data?	0-1

Evidence Collection:

evidence:
  H1_technical:
    supporting:
      - "Page load time increased 2s in Q4" (confidence: 0.9)
      - "Mobile bounce rate up 15%" (confidence: 0.85)
    contradicting:
      - "Desktop conversion stable" (confidence: 0.8)
    net_score: 0.65
    
  H2_competitor:
    supporting:
      - "Competitor launched Oct 15" (confidence: 1.0)
      - "Google Trends shows competitor interest up" (confidence: 0.7)
    contradicting:
      - "Our traffic unchanged" (confidence: 0.9)
    net_score: 0.55
    
  # ... evaluate all hypotheses

Scoring Formula:

Score(H) = (Explanatory × 0.3) + (Simplicity × 0.2) + 
           (Coherence × 0.25) + (Testability × 0.1) + 
           (Prior × 0.15)

Stage 4: Best Explanation

Purpose: Select most probable cause with confidence and next steps.

Ranking:

ranked_hypotheses:
  - rank: 1
    hypothesis: H1
    cause: "Website performance degradation"
    score: 0.78
    confidence: 0.75
    
  - rank: 2
    hypothesis: H3
    cause: "Seasonal behavior change"
    score: 0.62
    confidence: 0.60
    
  - rank: 3
    hypothesis: H2
    cause: "Competitor pricing"
    score: 0.55
    confidence: 0.50

Best Explanation Output:

conclusion:
  primary_cause: "Website performance degradation"
  confidence: 0.75
  mechanism: "2s increase in load time caused 42% more abandonment, 
              consistent with industry benchmarks (1s = ~7% conversion loss)"
  
  contributing_factors:
    - "Seasonal patterns may account for 10-15% of drop"
    
  ruled_out:
    - "Competitor pricing (traffic unchanged, not price-sensitive segment)"
    
  remaining_uncertainty:
    - "Whether mobile-specific or site-wide"
    - "Whether fix will fully restore conversion"
    
  next_steps:
    - "Verify: Run A/B test with performance fix (high priority)"
    - "Measure: Mobile vs desktop split post-fix"
    - "Monitor: Competitor activity (low priority)"
    
  suggested_next_mode: causal  # Ready to act on diagnosis

Quality Gates

Gate	Requirement	Failure Action
Hypothesis count	≥5 hypotheses	Generate more before proceeding
Category diversity	≥3 categories	Expand hypothesis search
Evidence present	≥1 data point per top-3	Gather more evidence
Confidence threshold	≥0.6 for best	Flag as inconclusive
Testability	Best hypothesis testable	Propose test design

Common Failure Modes

Failure	Symptom	Fix
Anchoring	First hypothesis gets all attention	Force diversity in Stage 2
Confirmation bias	Only seek supporting evidence	Require contradicting evidence
Complexity creep	Elaborate explanations preferred	Weight simplicity appropriately
Premature closure	Stop at first plausible cause	Complete all 4 stages

Output Contract

abductive_output:
  conclusion:
    primary_cause: string
    confidence: float  # 0.0-1.0
    mechanism: string  # How cause produces effect
    
  hypotheses:
    ranked: [ScoredHypothesis]  # All evaluated
    ruled_out: [string]         # Definitively excluded
    
  evidence:
    supporting: [EvidenceItem]
    contradicting: [EvidenceItem]
    gaps: [string]  # What evidence is missing?
    
  uncertainty:
    remaining_questions: [string]
    confidence_bounds: [float, float]  # Low, high
    
  next:
    immediate_actions: [string]
    tests_to_run: [string]
    suggested_mode: optional<ReasoningMode>
    
  trace:
    stages_completed: [1, 2, 3, 4]
    duration_ms: int
    hypotheses_generated: int
    evidence_points: int

Example Execution

Context: "Enterprise conversion dropped 40% last quarter"

Stage 1 - Observation:

Anomaly: 40% drop (15% → 9%) in enterprise conversion
Temporal: Started week 5 of Q3, accelerated Q4
Scope: Enterprise only, SMB stable
Surprise: 0.85

Stage 2 - Hypotheses:

H1: Enterprise buyer behavior changed (economic uncertainty)
H2: Sales team restructuring disrupted relationships
H3: Competitor launched enterprise-specific offering
H4: Our enterprise pricing became uncompetitive
H5: Product gaps for enterprise use cases
H6: Longer sales cycles (not drop, just delay)
H7: Key account manager departures

Stage 3 - Evidence:

H1: Supporting (CFO involvement up 40%), Contradicting (overall enterprise IT spend flat)
H2: Supporting (3 senior reps left Q3), Contradicting (coverage maintained)
H6: Supporting (average cycle +45 days), Strong supporting
H7: Supporting (2 key AMs left), Moderate supporting

Stage 4 - Conclusion:

Primary: Sales cycle elongation (not true drop) + AM departures (relationship gaps)
Confidence: 0.72
Mechanism: Economic uncertainty extended CFO approval cycles by 45 days;
           AM departures created relationship gaps in 6 key accounts

Next: Wait 45 days to see if "delayed" deals close (causal monitoring)
      Immediately backfill AM roles (causal action)