Analogical Reasoning

---
provider: OpenAI
model: gpt-4o
temperature: 0.7
---

<step>
# Source Domain Analysis

Let's first deeply understand our source domain (the familiar concept):

## Source Domain:
{{ source_domain }}

## Source Analysis:
1. **Core Elements**: What are the key components of this domain?
2. **Relationships**: How do these components interact with each other?
3. **Processes**: What are the main processes or dynamics at work?
4. **Constraints**: What limits or governs the behavior in this domain?
5. **Properties**: What notable characteristics or patterns exist?
</step>

<step>
# Target Domain Analysis

Now let's analyze our target domain (the concept we want to understand):

## Target Domain:
{{ target_domain }}

## Target Analysis:
1. **Core Elements**: What are the key components of this domain?
2. **Relationships**: How do these components interact?
3. **Processes**: What are the main dynamics at work?
4. **Constraints**: What limits or governs behavior in this domain?
5. **Properties**: What notable characteristics can we identify?
</step>

<step>
# Systematic Mapping

Let's create a detailed mapping between source and target domains:

## Element Mapping:
- Source element A ↔ Target element X because...
- Source element B ↔ Target element Y because...
- Source element C ↔ Target element Z because...

## Relationship Mapping:
- Relationship A-B in source ↔ Relationship X-Y in target because...
- Relationship B-C in source ↔ Relationship Y-Z in target because...

## Process Mapping:
- Process P in source ↔ Process Q in target because...
- Causal sequence in source ↔ Causal sequence in target because...

## Non-Mappable Elements:
- Source elements with no target correspondence...
- Target elements with no source correspondence...
</step>

<step>
# Knowledge Transfer

Based on our mapping, let's transfer insights from source to target:

## Transferred Insights:
1. If in the source domain we know that A leads to B, then in the target domain...
2. If in the source domain, constraint C limits process P, then in the target domain...
3. If in the source domain, intervention I affects outcome O, then in the target domain...

## Novel Predictions:
1. Based on our analogy, we might predict that...
2. The analogy suggests a previously unnoticed relationship between...
3. By extension, we might discover that...
</step>

<step>
# Analogy Evaluation

Let's critically evaluate the strength and utility of our analogy:

## Strengths:
- Areas where the mapping is particularly strong and insightful...
- Useful predictions generated...
- Clarifications achieved...

## Limitations:
- Points where the analogy breaks down...
- Potential misleading implications...
- Missing aspects in the mapping...

## Overall Utility:
- How useful is this analogy for understanding the target domain?
- For which aspects of the target domain is the analogy most valuable?
- How might we complement this analogy with others?
</step>

---
provider: OpenAI
model: gpt-4o
temperature: 0.7
---

<step>
# Problem Understanding

Let's first understand the target problem thoroughly:

## Target Problem:
{{ target_problem }}

## Problem Analysis:
1. **Goal**: What are we trying to achieve?
2. **Constraints**: What limitations must we work within?
3. **Resources**: What do we have available?
4. **Challenges**: What makes this problem difficult?
5. **Success Criteria**: How will we know if we've solved it?
</step>

<step>
# Analogue Problem Identification

Let's identify problems that share structural similarities:

## Candidate Analogue Problems:
1. **Analogue A**: [Description of a potentially similar problem]
   - Structural similarities: ...
   - Key differences: ...

2. **Analogue B**: [Description of another potentially similar problem]
   - Structural similarities: ...
   - Key differences: ...

3. **Analogue C**: [Description of another potentially similar problem]
   - Structural similarities: ...
   - Key differences: ...

## Selected Analogue:
Based on the analysis above, [selected analogue] provides the strongest structural similarity because...
</step>

<step>
# Solution Transfer

Let's analyze how the analogue problem was solved and transfer that solution:

## Analogue Solution:
The solution approach for [selected analogue] involved:
1. First step/strategy...
2. Second step/strategy...
3. Third step/strategy...
4. Adaptations needed for specific circumstances...

## Solution Mapping:
- Analogue strategy 1 maps to target context as...
- Analogue strategy 2 maps to target context as...
- Analogue strategy 3 maps to target context as...
- Adaptations needed in our target context...
</step>

<step>
# Target Solution Development

Let's develop a complete solution for our target problem:

## Proposed Solution:
1. **First Step**: [Detailed explanation]
   - Rationale: [Why this step works based on analogy]
   - Implementation details: [How specifically to execute]

2. **Second Step**: [Detailed explanation]
   - Rationale: [Why this step works based on analogy]
   - Implementation details: [How specifically to execute]

3. **Third Step**: [Detailed explanation]
   - Rationale: [Why this step works based on analogy]
   - Implementation details: [How specifically to execute]

## Potential Challenges:
- Challenge 1 and how to address it...
- Challenge 2 and how to address it...
- Challenge 3 and how to address it...
</step>

<step>
# Solution Evaluation

Let's evaluate our analogically-derived solution:

## Evaluation:
1. **Goal Achievement**: Does the solution achieve our stated goal?
2. **Constraint Compliance**: Does it work within our constraints?
3. **Resource Efficiency**: Does it effectively use available resources?
4. **Adaptability**: How well does it adapt the source solution to target differences?
5. **Innovation**: Does the analogy bring novel insights to the problem?

## Refinements:
Based on this evaluation, we should refine our solution by:
- Refinement 1...
- Refinement 2...
- Refinement 3...

## Final Solution:
[Complete description of the final proposed solution]
</step>

---
provider: OpenAI
model: gpt-4o
temperature: 0.7
type: agent
agents:
  - agents/physical_system
  - agents/social_system
  - agents/information_system
---

# Multi-Domain Analogical Understanding

Explore {{ complex_concept }} through multiple domain analogies to build a rich understanding.

## Target Concept:
{{ complex_concept }}

## Basic Understanding:
First, let me understand the fundamental nature of {{ complex_concept }}:
- Core elements and processes
- Key relationships
- Primary challenges

## Cross-Domain Analysis:
Now I'll analyze this concept through multiple analogical lenses:
1. **Physical Systems**: How does this concept relate to physical/mechanical systems?
2. **Social Systems**: What social or organizational systems mirror this concept?
3. **Information Systems**: How might information systems provide an analogical framework?

I'll coordinate with domain experts to develop these analogies and synthesize a comprehensive understanding.

---
provider: OpenAI
model: gpt-4o
temperature: 0.7
---

# Near-Far Analogical Analysis

Let's examine {{ target_concept }} through a spectrum of analogies:

## Target Concept:
{{ target_concept }}

## Near Analogy (Same Domain):
An analogy from within the same domain:
- Selected analogy: [closely related concept]
- Key mappings:
  - [Detailed mappings...]
- Insights gained:
  - [Specific insights from this close analogy]
- Limitations:
  - [Where this close analogy breaks down]

## Intermediate Analogy (Related Domain):
An analogy from a related but different domain:
- Selected analogy: [somewhat related concept]
- Key mappings:
  - [Detailed mappings...]
- Insights gained:
  - [New insights from this more distant analogy]
- Limitations:
  - [Where this intermediate analogy breaks down]

## Far Analogy (Distant Domain):
An analogy from a very different domain:
- Selected analogy: [distant concept]
- Key mappings:
  - [Detailed mappings...]
- Insights gained:
  - [Novel insights from this distant analogy]
- Limitations:
  - [Where this far analogy breaks down]

## Synthesis of Multiple Perspectives:
By combining insights from analogies at different distances, we can understand {{ target_concept }} more comprehensively:
- Common patterns revealed across analogies...
- Unique contributions from each analogical distance...
- Enhanced understanding of the target concept...

---
provider: OpenAI
model: gpt-4o
temperature: 0.8
---

<step>
# Innovation Challenge

Let's understand the innovation challenge we're addressing:

## Challenge:
{{ innovation_challenge }}

## Challenge Analysis:
1. **Core Problem**: What fundamental need or obstacle are we addressing?
2. **Current Approaches**: How is this currently being approached?
3. **Limitations**: Why aren't current solutions sufficient?
4. **Desired Outcomes**: What would an ideal solution achieve?
5. **Constraints**: What practical limitations must solutions work within?
</step>

<step>
# Cross-Domain Exploration

Let's explore diverse domains for analogical inspiration:

## Candidate Domains:
1. **Natural Systems**: Biological, ecological, evolutionary systems
2. **Social Systems**: Cultural, organizational, economic patterns
3. **Physical Systems**: Mechanical, architectural, material properties
4. **Information Systems**: Computational, network, data structures
5. **Historical Precedents**: Past solutions to similar challenges

## Interesting Analogies:
[For each domain, identify 1-2 interesting potential analogies and briefly describe their relevance]
</step>

<step>
# Deep Analogical Analysis

Let's select the most promising analogies and analyze them deeply:

## Selected Analogy 1: [Name]
- **Domain**: [Source domain]
- **Key Components**: [Essential elements]
- **Operating Principles**: [How it works]
- **Notable Features**: [What makes it effective]
- **Mapping to Challenge**: [How it relates to our innovation challenge]

## Selected Analogy 2: [Name]
- **Domain**: [Source domain]
- **Key Components**: [Essential elements]
- **Operating Principles**: [How it works]
- **Notable Features**: [What makes it effective]
- **Mapping to Challenge**: [How it relates to our innovation challenge]
</step>

<step>
# Concept Generation

Let's generate innovative solution concepts inspired by our analogies:

## Concept 1: [Name]
- **Inspiration**: [Which analogy and which specific aspects]
- **Description**: [Detailed concept explanation]
- **Key Features**: [Notable aspects of the solution]
- **Operating Principles**: [How it works]
- **Advantages**: [Benefits compared to existing approaches]

## Concept 2: [Name]
- **Inspiration**: [Which analogy and which specific aspects]
- **Description**: [Detailed concept explanation]
- **Key Features**: [Notable aspects of the solution]
- **Operating Principles**: [How it works]
- **Advantages**: [Benefits compared to existing approaches]

## Concept 3: [Name]
- **Inspiration**: [Which analogy and which specific aspects]
- **Description**: [Detailed concept explanation]
- **Key Features**: [Notable aspects of the solution]
- **Operating Principles**: [How it works]
- **Advantages**: [Benefits compared to existing approaches]
</step>

<step>
# Concept Refinement

Let's select and refine the most promising concept:

## Selected Concept: [Name]

## Refinement Areas:
1. **Technical Feasibility**: How to make this concept practically implementable
2. **User Experience**: How to optimize for user needs and preferences
3. **Resource Efficiency**: How to minimize required resources
4. **Scalability**: How the solution might scale to different contexts
5. **Implementation**: Key steps toward realizing this concept

## Refined Concept:
[Detailed description of the refined solution concept]

## Next Steps:
1. [First implementation step]
2. [Second implementation step]
3. [Third implementation step]
</step>