reasoning-analogical

name	reasoning-analogical
description	Transfer knowledge from source domains to novel target situations through structural mapping. Use when facing new markets, products, or situations where past experience provides relevant patterns. Produces adapted solutions with explicit mappings and context adjustments.

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:
      - 
       
       
      
  
     
     
     
    
  
     
     
     

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"
         
         
        
  
    
       
       
       
      
    
       
       
       
      
    
       
       
       

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"
     

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]
    
     
   []
  
  
       
     []          
    
  
     
       
     

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