aiskillstore/reasoning-analogical

Analogical Reasoning

Transfer structured knowledge across domains. The logic of pattern recognition and adaptation.

Type Signature

Analogical : Source → StructuralMap → Target → Adaptation

Where:
  Source        : PriorExperience × Relevance → SourceDomain
  StructuralMap : SourceDomain → (Objects × Relations × Constraints)
  Target        : StructuralMap × NewContext → MappedStructure  
  Adaptation    : MappedStructure × ContextDifferences → AdaptedSolution

When to Use

Use analogical when:

• Entering new market with experience in similar markets
• Building new product with experience in similar products
• Facing novel situation with structural similarity to past cases
• Need to transfer playbooks across contexts
• "This is like..." patterns in thinking

Don't use when:

• Cause-effect chain is known → Use Causal
• Need to explain observation → Use Abductive
• Competing positions to resolve → Use Dialectical

Four-Stage Process

Stage 1: Source Retrieval

Purpose: Identify relevant prior experience with documented outcomes.

Source Selection Criteria:

| Criterion | Question | Weight | |-----------|----------|--------| | Structural similarity | Same type of problem/situation? | 0.35 | | Outcome documented | Do we know what happened? | 0.25 | | Recency | How recent is the experience? | 0.15 | | Success level | Did the approach work? | 0.15 | | Context overlap | Similar constraints/resources? | 0.10 |

Source Retrieval Process:

retrieval:
  query: "Entering B2B marketplace vertical"
  
  candidates:
    - source: "Shopify DTC launch (2024)"
      similarity: 0.75
      outcome: "Validated in 6 months, $200K ARR"
      success: high
      
    - source: "Fashion brand pilot (2023)"
      similarity: 0.60
      outcome: "Slow start, pivoted twice"
      success: medium
      
    - source: "Enterprise SDK launch (2024)"
      similarity: 0.50
      outcome: "$400K first deal, strong pipeline"
      success: high
      
  selected: "Shopify DTC launch"
  reason: "Highest structural similarity (platform integration, 
           API-first, self-serve onboarding)"

Output:

source:
  case: "Shopify DTC launch"
  domain: "E-commerce platform integration"
  timeframe: "Q1-Q2 2024"
  outcome: 
    result: "success"
    metrics: "$200K ARR, 50 merchants, 6-month validation"
  key_factors:
    - "Strong app store presence"
    - "Self-serve onboarding"
    - "Integration-first positioning"
  documented_in: "threads/operations/shopify-dtc-launch/"

Stage 2: Structural Mapping

Purpose: Extract transferable structure from source domain.

Mapping Components:

| Component | Source Example | Abstracted | |-----------|----------------|------------| | Objects | Shopify merchants | Platform users | | Relations | Merchant → App → Customer | User → Integration → End-user | | Constraints | App store rules | Platform policies | | Mechanisms | App store discovery → trial → purchase | Discovery → trial → convert | | Success factors | Reviews, featured placement | Social proof, visibility |

Structural Map:

structure:
  objects:
    - User: "Entity adopting our solution"
    - Platform: "Ecosystem we integrate with"
    - EndUser: "Final beneficiary of solution"
    - Solution: "Our product/integration"
    
  relations:
    - Platform ⊃ Marketplace: "Platform has discovery mechanism"
    - User → Solution: "User adopts solution"
    - Solution → EndUser: "Solution serves end users"
    - EndUser feedback → User: "Value demonstration"
    
  mechanisms:
    acquisition:
      - "Platform marketplace discovery"
      - "Peer recommendations"
      - "Content marketing to users"
    activation:
      - "Self-serve trial"
      - "Quick time-to-value"
      - "Integration simplicity"
    retention:
      - "Embedded in workflow"
      - "Switching cost creation"
      - "Continuous value delivery"
      
  constraints:
    - "Platform approval required"
    - "Platform policies must be followed"
    - "Revenue share with platform"
    
  success_factors:
    - "Marketplace ranking/visibility"
    - "User reviews/ratings"
    - "Platform relationship quality"

Stage 3: Target Application

Purpose: Map structure to new context, identifying what transfers and what doesn't.

Target Context:

target:
  domain: "B2B marketplace integration"
  platform: "Faire wholesale marketplace"
  user: "Wholesale brands"
  end_user: "Retailers"
  goal: "Return reduction for wholesale fashion"

Mapping Execution:

mapping:
  objects:
    Platform: "Shopify" → "Faire"
    User: "DTC merchant" → "Wholesale brand"
    EndUser: "Consumer" → "Retailer"
    Solution: "Fit recommendation app" → "Wholesale sizing tool"
    
  relations:
    preserved:
      - "Platform marketplace discovery" (Faire has app marketplace)
      - "User adopts solution" (brands install integrations)
      - "Value to end user" (retailers get better sizing)
      
    modified:
      - "Self-serve trial" → "Account executive assisted"
        reason: "B2B decision process differs"
      - "Individual purchase" → "Contract-based"
        reason: "Wholesale pricing models"
        
    broken:
      - "App store reviews drive adoption"
        reason: "Faire marketplace less review-driven"
        replacement: "Case studies and referrals"
        
  mechanisms:
    acquisition:
      transfers: "Platform marketplace presence"
      adapts: "Content marketing → Trade show presence"
      new: "Wholesale buyer referral program"
      
    activation:
      transfers: "Integration simplicity"
      adapts: "Self-serve → Assisted onboarding"
      new: "Pilot with single retail partner"
      
    retention:
      transfers: "Embedded in workflow"
      transfers: "Value demonstration"
      adapts: "Individual metrics → Fleet metrics"

Stage 4: Adaptation

Purpose: Produce concrete plan adjusted for context differences.

Context Differences Analysis:

differences:
  critical:
    - name: "Decision process"
      source: "Individual merchant, fast"
      target: "Buying committee, slow"
      adaptation: "Add sales support, longer cycle expectations"
      
    - name: "Value demonstration"
      source: "Per-order metrics visible"
      target: "Aggregate across retailers"
      adaptation: "Build analytics dashboard for brands"
      
  moderate:
    - name: "Pricing model"
      source: "Per-store subscription"
      target: "Volume-based or percentage"
      adaptation: "Explore usage-based pricing"
      
  minor:
    - name: "Technical integration"
      source: "Shopify API"
      target: "Faire API"
      adaptation: "Standard integration work"

Adapted Solution:

adaptation:
  strategy: "Platform-assisted B2B wholesale launch"
  
  what_transfers:
    - "Integration-first positioning"
    - "Platform relationship investment"
    - "Quick time-to-value focus"
    - "Embedded workflow stickiness"
    
  what_adapts:
    - "Self-serve → Assisted onboarding with demo"
    - "App store discovery → Trade shows + referrals"
    - "Individual reviews → Case studies"
    - "Per-order metrics → Brand-level analytics"
    
  what's_new:
    - "Sales motion for wholesale buyers"
    - "Multi-retailer aggregation features"
    - "B2B pricing model (volume-based)"
    
  execution_plan:
    phase_1: "Platform partnership + 3 pilot brands"
    phase_2: "Case study development + trade show presence"
    phase_3: "Scale via referrals + platform promotion"
    
  expected_timeline: "9-12 months (vs 6 months for DTC)"
  reason: "B2B sales cycle longer, relationship-building required"
  
  confidence: 0.70
  uncertainty:
    - "Faire marketplace dynamics unknown"
    - "Wholesale brand decision process may vary"
    - "Volume-based pricing acceptance unclear"

Quality Gates

| Gate | Requirement | Failure Action | |------|-------------|----------------| | Source quality | Documented outcome with metrics | Find better source | | Structural clarity | ≥3 objects, ≥3 relations explicit | Complete mapping | | Mapping coverage | All source elements mapped or marked broken | Complete mapping | | Adaptation specificity | Concrete actions, not abstract | Add specificity | | Confidence threshold | ≥0.6 confidence | Flag high uncertainty |

Common Failure Modes

| Failure | Symptom | Fix | |---------|---------|-----| | Surface similarity | Mapped by superficial features, not structure | Focus on relations, not objects | | Over-transfer | Assume everything applies | Explicitly check each element | | Under-adaptation | Copy-paste without adjustment | Force context difference analysis | | Single source | Only one analogy considered | Retrieve multiple candidates |

Output Contract

analogical_output:
  source:
    case: string
    domain: string
    outcome: {result: string, metrics: string}
    thread_ref: optional<string>
    
  mapping:
    objects: {source_name: target_name}
    relations:
      preserved: [string]
      modified: [{from: string, to: string, reason: string}]
      broken: [{relation: string, reason: string, replacement: string}]
      
  adaptation:
    transfers: [string]      # What applies directly
    adapts: [string]         # What needs modification
    new: [string]            # What's genuinely new
    
  plan:
    phases: [{name: string, actions: [string]}]
    timeline: string
    milestones: [string]
    
  confidence: float  # 0.0-1.0
  uncertainty: [string]
  
  next:
    suggested_mode: ReasoningMode  # Usually causal
    canvas_refs: [string]          # Assumptions being tested
    
  trace:
    sources_considered: int
    mapping_coverage: float  # % of source elements mapped
    duration_ms: int

Example Execution

Context: "Expand to home goods vertical (currently in fashion)"

Stage 1 - Source Retrieval:

Selected: Fashion DTC success (highest similarity)
Alternatives considered: 
  - Beauty vertical (rejected: different return dynamics)
  - B2B wholesale (rejected: different buyer)

Stage 2 - Structural Mapping:

Objects: Fashion brand → Home goods brand
Relations: 
  - Fit concern → Dimension/space concern
  - Style matching → Aesthetic matching
  - Return reason: fit → Return reason: scale/compatibility
Mechanisms:
  - Visual AI → Transfer (image analysis)
  - Size recommendation → Adapt (dimension recommendation)
  - Color matching → Transfer (palette matching)

Stage 3 - Target Application:

Preserved: Visual AI core, recommendation engine, integration model
Modified: Fit algorithm → Dimension/space algorithm
Broken: Body measurement input → Room/space measurement input

Stage 4 - Adaptation:

Plan:
  Phase 1: Partner with 2 home goods DTC brands (furniture focus)
  Phase 2: Adapt algorithm for dimension-based recommendations
  Phase 3: Develop room visualization feature (new capability)
  
Timeline: 4-6 months (faster than fashion - simpler measurements)
Confidence: 0.75
Key uncertainty: Room visualization technical complexity