mss-model-space-search

name	mss - Model Space Search
description	Understanding = finding a model that fits.
output	{"format":"prose"}

Model Space Search

Overview

Understanding = finding a model that fits. Instead of settling on first model that seems plausible:

1. Generate multiple competing models
2. Score each against fit criteria
3. Select the model with best support

This prevents premature closure on a suboptimal model.

Goal

Generate multiple possible models/explanations for a phenomenon, then search for the one that best fits the evidence.

Steps

Step 1: Describe the Phenomenon

Clearly state what you're trying to model. What patterns, behaviors, or outcomes need explaining?

Output: Phenomenon description

Step 2: List Observations

Document all relevant observations/data. These are what models must account for.

Output: Observation list

Step 3: Generate Null Model

What's the simplest explanation? This is the baseline to beat.

Output: Null model

Step 4: Generate Causal Models

For each plausible cause, build a model. State cause, mechanism, predictions.

Output: Causal models

Step 5: Generate Mechanistic Models

What mechanisms could produce the observations? Build models around different mechanisms.

Output: Mechanistic models

Step 6: Generate Analogical Models

What similar phenomena have known models? Import and adapt.

Output: Analogical models

Step 7: Compile Model List

List all generated models. State each clearly with:

• Core claim
• Mechanism
• Key predictions
• Required assumptions

Output: Master model list

Step 8: Score Each Model

For each model, score on criteria:

• Explains observations
• No contradictions
• Predictive accuracy
• Simplicity
• Generalizability
• Coherence
• Mechanism clarity

Output: Scored models

Step 9: Compare Models

Create comparison table. Identify which model wins on which criteria. Look for dominant model (wins on most).

Output: Model comparison

Step 10: Select Best Model

Select model with highest score. Note:

• Confidence (how much better than alternatives?)
• What would change the selection?
• What further evidence would help?

Output: Selected model

Step 11: State Implications

If this model is correct:

• What else should be true?
• What should we expect to see?
• What actions does this suggest?

Output: Model implications

When to Use

• Trying to understand a phenomenon
• Multiple explanations seem plausible
• Need to choose between theories
• Building mental models
• Scientific/analytical inquiry

Verification

• Multiple models were generated (not just favorite)
• Null model was included as baseline
• Models are clearly stated with mechanisms
• Scoring was consistent across models
• Best model accounts for anomalies
• Implications and next steps identified