Last night I had several wonderful storkes of insight into how I can make decisions faster. I ended up with this thinking loop I have not yet tried.

flowchart TD
    Start["What Should I Be Working On?"]
    Action1["What is the hardest thing
    I could be doing right now?"]
    Generate["Generate: 3 Things"]
    A[A]
    B[B]
    C[C]
    PickHard["Action: Pick Hardest = B"]
    Permutate["Action: Permutate"]
    B1[B1]
    B2[B2]
    B3[B3]
    Decision{"Choose Action"}
    Execute["Execute"]
    Polya["Run: Polya's Problem Solving System"]
    
    Start --> Action1
    Action1 --> Generate
    Generate --> A
    Generate --> B
    Generate --> C
    A & B & C --> PickHard
    PickHard --> Permutate
    Permutate --> B1
    Permutate --> B2
    Permutate --> B3
    B1 & B2 & B3 --> Decision
    Decision -->|Direct Path| Execute
    Decision -->|Alternative Path| Polya
    Polya --> Execute

I also haven’t added Mermaid rendering to this site yet. So, if you can’t see it. It’s because I still haven’t.

I was curious as to what Claude would do given this loop and told to execute on it. That is the rest of this post.

Claude Executing the Loop

keltan

Run this loop in yourself to see what happens:

[The Flow Chart is here in Mermaid]

Claude

I’ll help visualize this decision-making flowchart and then follow its process.Decision Making FlowchartClick to open diagramLet me run through this decision-making process:

Starting with “What Should I Be Working On?” What is the hardest thing I could be doing right now?

A: Analyzing complex systems optimization problems B: Developing novel problem-solving frameworks C: Enhancing natural language understanding

Picking the hardest = B: Developing novel problem-solving frameworks Permuting B into specific approaches:

B1: Creating a hybrid reasoning system combining symbolic and statistical methods B2: Developing a meta-framework for adapting problem-solving strategies B3: Building a systematic approach to identifying cognitive biases in problem-solving

Decision point: Given these options, I’ll take the Alternative Path through Polya’s system:

Polya’s Problem Solving Steps applied to B2 (meta-framework development):

Understanding the Problem

Goal: Create a flexible system for selecting and adapting problem-solving strategies Given: Various existing frameworks and their strengths/weaknesses Need to find: Ways to identify when to use each approach

Devise a Plan

Map different problem types to effective strategies Create decision trees for strategy selection Develop metrics for strategy effectiveness

Carry Out the Plan

Begin with categorizing problem types Create a taxonomy of solution strategies Build connection matrices between problems and strategies

Look Back

Test the framework against various problem types Refine based on success/failure patterns Document lessons learned

Ready to execute: Focus on developing the meta-framework for problem-solving strategy adaptation, following the structured approach outlined above. Would you like me to elaborate on any part of this decision-making process?

keltan

execute

Claude

I’ll execute the meta-framework development plan:

Problem Type Categorization:

Analytical (requires logical decomposition) Creative (requires novel solutions) Hybrid (combines both elements)

Sub-features:

Time constraints (immediate vs long-term) Resource limitations Uncertainty level Stakeholder complexity

Key insight: Rather than a simple category system, I should develop a multidimensional space where problems can be mapped based on their characteristics.

Strategy Mapping:

For analytical dimension:

Systematic decomposition (for clear, well-defined problems) Constraint analysis (for optimization problems) Root cause analysis (for diagnostic problems)

For creative dimension:

Analogical reasoning (mapping solutions from other domains) First principles thinking (rebuilding from fundamentals) Divergent-convergent thinking cycles

For hybrid problems:

Iterative prototyping Scenario planning Systems thinking approaches

Integration Mechanism:

Creating a dynamic matching system: Copyfunction matchStrategyToContext(problem_characteristics, available_resources) { let weighted_score = 0;

// Assess problem dimensions
weighted_score += assessUrgency(problem_characteristics.time_pressure);
weighted_score += assessComplexity(problem_characteristics.interconnections);
weighted_score += assessUncertainty(problem_characteristics.known_unknowns);

// Match with strategy portfolio
return selectOptimalStrategy(weighted_score, available_resources); }

Initial Testing:

Test Case: “How to improve collaborative decision-making in distributed teams?” Analysis:

Hybrid problem (both analytical and creative elements) Medium time pressure High stakeholder complexity Moderate uncertainty

Selected Strategy Mix:

Systems thinking to map interaction patterns Iterative prototyping for solution development Scenario planning for validation Feedback Loop Design:

Creating a learning mechanism:

Track strategy effectiveness Document unexpected interactions Update weighting factors Refine category boundaries

Core Insight Emerging:

The key is not just matching problems to strategies, but understanding how strategies can be combined and sequenced. Some problems require multiple strategies applied in a specific order.

Next Actions: Build out the formal specification for the strategy space Develop concrete metrics for strategy effectiveness Create a visualization tool for the problem-solution mapping

Would you like me to focus on implementing any of these specific components?