thinking-model-selection

What this skill does

# Model Selection

> **Overlaps thinking-model-router.** If you already know which model fits, skip routing entirely and just invoke that model. If you don't, **thinking-model-router** is the single entry point — start there. This skill goes deeper on *how* to classify a problem and *when to abandon* a model once chosen. (Audit recommends merging this into thinking-model-router; for now, prefer the router as the front door.)

## Overview

Every mental model has a domain where it excels and domains where it fails. Model selection is the meta-skill of recognizing which model fits which problem. The expert doesn't just know many models—they know when to apply each one. Using the wrong model is often worse than using no model at all.

**Core Principle:** The map is not the territory. Choose the map that best serves your journey.

## When to Use

- Facing a new problem type
- Current approach isn't working
- Multiple stakeholders suggest different frameworks
- Deciding how to structure analysis
- Teaching others which tools to use
- Building decision-making processes

Decision flow:

```
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
```

## When NOT to Use

- **You don't know which thinking tool to reach for.** Start with `thinking-model-router` — it is the single entry point that routes to the right skill. This skill goes deeper on *how* to classify problems and match models, but only after the router has narrowed the field.
- **You've already classified the problem type.** If you know it's a debugging problem, just invoke `thinking-scientific-method` or `thinking-five-whys-plus` directly. Don't run a full model-selection exercise to confirm what you already know.
- **The problem is routine and your default approach fits.** Model selection adds overhead; on a familiar problem, use the model you know works.
- **You're tempted to run this as a first step on every task.** That turns a meta-skill into a tax. Most tasks don't need model selection — they need execution. Reserve this for when your usual approach has demonstrably failed.

> **Redirect:** For nearly all cases, start with `thinking-model-router` — it classifies the problem and dispatches to the right skill in one pass. This skill is the reference for *how* that classification works, not a replacement for the router.

## Trigger Card

When your usual problem-solving approach has demonstrably failed and you need to match a different tool to the task:

1. **Classify the problem:** Diagnostic / Decision / Understanding / Creative / Evaluation.
2. **Check the constraint:** Time pressure? Information gaps? High stakes? Complexity?
3. **Match:** Use the Problem-Model Matching table below to find the category → select the specific model.
4. **Set an exit criterion:** If no insight in 15+ minutes or key facts don't fit, switch.

If you don't know which approach to use at all, start with `thinking-model-router` — it does the classification and dispatch in one pass.

## Problem-Model Matching

### Step 1: Classify the Problem

```markdown
## 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] |
```

### Step 2: Match to Model Categories

```markdown
## 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 |
```

### Step 3: Select Specific Model

```markdown
## 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]
```

## Model Selection Matrix

### By Problem Type

```
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
```

### By Domain

```markdown
## 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 Failure Modes

### Using the Wrong Model

```markdown
## 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

```markdown
## Model Overuse Patterns

"When you have a hammer, ev