Facing a problem?
  → Have you identified the problem type? → no → CLASSIFY THE PROBLEM FIRST
  → Does your usual model fit? → no → CONSIDER ALTERNATIVES
  → Are you using a model by habit? → yes → QUESTION THE FIT

## Problem Classification

Problem: [Describe the problem]

Problem dimensions:
| Dimension | Assessment |
|-----------|------------|
| Predictability | Can outcomes be predicted? [High/Medium/Low] |
| Complexity | How many interacting parts? [Simple/Complicated/Complex] |
| Time horizon | When do consequences matter? [Immediate/Short/Long] |
| Reversibility | Can decisions be undone? [Easily/With difficulty/Not at all] |
| Information | How much do you know? [Complete/Partial/Minimal] |
| Stakeholders | Who's affected? [Individual/Team/Organization/Society] |

## Model Category Matching

Based on classification, which category fits?

| Problem Type | Model Category | Examples |
|--------------|----------------|----------|
| Root cause unknown | Diagnostic models | 5 Whys, Scientific Method, Kepner-Tregoe |
| Decision under uncertainty | Probabilistic models | Bayesian, Expected Value, Regret Minimization |
| System behavior | Systems models | Feedback Loops, Leverage Points, Archetypes |
| Cognitive bias risk | Debiasing models | Pre-mortem, Red Team, Steel-manning |
| Resource allocation | Constraint models | Theory of Constraints, Opportunity Cost |
| Innovation/exploration | Generative models | First Principles, TRIZ, Effectuation |
| Domain classification | Meta models | Cynefin, Circle of Competence |

## Model Selection

Category: [From Step 2]

Candidate models:
| Model | Fit Score | Strengths for This Problem | Weaknesses |
|-------|-----------|---------------------------|------------|
| [Model 1] | [1-5] | [Why it fits] | [Limitations] |
| [Model 2] | [1-5] | [Why it fits] | [Limitations] |
| [Model 3] | [1-5] | [Why it fits] | [Limitations] |

Selected model: [Choice]
Rationale: [Why this model for this problem]

DIAGNOSTIC PROBLEMS (What's causing this?)
├── Known categories exist → Kepner-Tregoe (systematic analysis)
├── Need quick root cause → 5 Whys Plus (iterative drilling)
├── Hypothesis-driven → Scientific Method (test and falsify)
└── System-wide issue → Feedback Loops (find reinforcing patterns)

DECISION PROBLEMS (What should we do?)
├── High stakes, irreversible → Regret Minimization, Pre-mortem
├── Under uncertainty → Bayesian, Probabilistic Thinking
├── Resource constrained → Opportunity Cost, Theory of Constraints
├── Multiple options → Kepner-Tregoe (decision analysis)
└── Type 1 vs Type 2 → Reversibility Framework

UNDERSTANDING PROBLEMS (How does this work?)
├── Complex system → Systems Thinking, Feedback Loops
├── Human behavior → Jobs to be Done, Incentive Analysis
├── Organizational → Archetypes, Leverage Points
└── Competitive → Red Team, Game Theory

CREATIVE PROBLEMS (How might we...?)
├── Break assumptions → First Principles, TRIZ
├── Limited resources → Effectuation, Via Negativa
├── Technical contradiction → TRIZ
└── Unknown territory → Thought Experiments, Cynefin (probe)

EVALUATION PROBLEMS (Is this good?)
├── Arguments/proposals → Steel-manning, Red Team
├── Predictions → Probabilistic, Calibration
├── Longevity → Lindy Effect
├── Safety → Margin of Safety, Pre-mortem
└── Expertise fit → Circle of Competence

## Domain-Model Mapping

| Domain | Primary Models | Why |
|--------|---------------|-----|
| Debugging | Scientific Method, 5 Whys | Hypothesis-differential localization, root cause |
| Architecture | Systems Thinking, Leverage Points | Interconnections, intervention |
| Product | Jobs to be Done, Cynefin | User needs, complexity |
| Strategy | Red Team, Pre-mortem | Adversarial, risk |
| Performance | Theory of Constraints, Fermi | Bottlenecks, estimation |
| Decisions | Reversibility, Regret Minimization | Stakes assessment |
| Innovation | First Principles, TRIZ | Breakthrough thinking |
| Risk | Margin of Safety, Probabilistic | Uncertainty handling |

## Model Mismatch Indicators

Signs you're using the wrong model:
- Analysis feels forced or awkward
- Key aspects don't fit the framework
- You're ignoring important factors
- Results don't match intuition consistently
- Stakeholders don't recognize the framing

Common mismatches:
| Situation | Wrong Model | Right Model |
|-----------|-------------|-------------|
| Complex adaptive system | Root cause analysis | Systems thinking |
| Simple process problem | Systems thinking | Checklist/SOP |
| Uncertain future | Detailed planning | Effectuation |
| Known domain | First principles | Best practices |
| Political problem | Technical analysis | Stakeholder mapping |

## Model Overuse Patterns

"When you have a hammer, everything looks like a nail"

Signs of overuse:
- You use the same model for every problem
- You haven't learned new models recently
- Problems that don't fit are "forced" into the model
- You dismiss problems that don't fit your model

Fix: Deliberately practice with unfamiliar models
     Ask: "What model would someone else use?"
     Rotate models intentionally

## Quick Model Selection

1. What type of problem? [Diagnostic/Decision/Understanding/Creative/Evaluation]
2. What's the constraint? [Time/Information/Stakes/Complexity]
3. What's the default model for this type?
4. Any reason NOT to use the default?
5. Proceed or reconsider

Default models by type:
- Diagnostic → 5 Whys Plus
- Decision → Reversibility check first
- Understanding → Systems Thinking
- Creative → First Principles
- Evaluation → Steel-manning

## Deliberate Model Selection

1. Characterize the problem fully
2. Identify 3-5 candidate models
3. Score each on fit
4. Consider combining models
5. Select and document rationale
6. Plan to reassess if model doesn't illuminate

Selection criteria:
| Criterion | Weight | Model A | Model B | Model C |
|-----------|--------|---------|---------|---------|
| Problem fit | 30% | | | |
| Available info | 20% | | | |
| Time to apply | 15% | | | |
| Stakeholder acceptance | 15% | | | |
| My competence with model | 20% | | | |

# Model Selection: [Problem]

## Problem Characterization
Type: [Diagnostic/Decision/Understanding/Creative/Evaluation]
Complexity: [Simple/Complicated/Complex/Chaotic]
Stakes: [Low/Medium/High]
Reversibility: [High/Medium/Low]
Information: [Complete/Partial/Minimal]

## Candidate Models
| Model | Fit | Strengths | Weaknesses |
|-------|-----|-----------|------------|
| | | | |

## Selected Model
Model: [Choice]
Rationale: [Why this model]

## Fallback
If selected model doesn't work: [Alternative]
Signs to switch: [Indicators]

## Application Plan
How I'll use this model:
1. [Step]
2. [Step]

## Model Combination Triggers

Use multiple models when:
- Problem spans multiple types
- Single model leaves blind spots
- Stakes are very high
- Time allows deeper analysis

Combination patterns:
- Sequential: Use A to narrow, then B to decide
- Parallel: Use A and B independently, compare results
- Nested: Use A at macro level, B at micro level

## Model Exit Criteria

Stop using a model when:
- 15+ minutes with no insight
- Key facts don't fit the framework
- You're forcing the analysis
- Someone suggests a better fit

Don't stop just because:
- The answer is uncomfortable
- It requires more work
- Results disagree with intuition (investigate why)