TL;DR

Dimensionalization is the practice of identifying and using quasi-independent axes or "dials" along which things vary. It’s the mental move behind tradeoff analysis, optimization, and design thinking. You are doing it already!

Unlike categorization or decomposition, dimensionalization preserves complexity but renders it navigable. Mastering it helps you think more clearly, decide more effectively, and communicate with greater precision.

A good decision isn’t just “good.” It’s good along the right dimensions, and good enough on the others to be worth it. You already know this; you just may not have named it. That naming, structuring mental move is dimensionalization. It is what enables us to see nuance.

You probably already dimensionalize:

• Watching: familiar vs new, funny vs serious, short vs long.
• Weekend: social vs solo, restful vs productive.
• Shopping: quality vs price, speed vs reliability.

You don’t need to learn dimensionalization. But getting better at it makes you more precise, faster, and harder to bullshit.

So how does dimensionalization work?

Meta-Dimension 1: Fidelity

Dimensionalization is a perceptual act. It’s how you make fuzzy, complex phenomena legible. You take something like “vibe” or “quality” or “pain” and ask: what are the independent axes along which this varies?

Fidelity is how well your sliders map to reality. High-Fidelity dimensions reveal meaningful differences. They don’t collapse when you zoom out or vanish when you compare two related objects.

What makes dimensions high-Fidelity?

• Validity: They track actual meaningful differences.
• Stability: They hold up over time, context, or zoom level.

Examples:

• High-Fidelity examples:
  • Quant investing: factor exposure, drawdown, alpha decay.
  • Software engineering: latency, modularity, throughput.
  • Listening to music: tempo, rhythmic complexity, texture, spatiality.
  • Homeowning: layout, light quality, maintenance, appreciation potential.
  • Making art: balance, composition, color harmony, movement.
• Low-Fidelity examples:
  • Buzzwords without clarification ("Tech Debt", “Efficiency”).
  • Aesthetic terms with no shared reference point (“Vibe”, “Quality”).

When Fidelity is low, you can still win, but you’re flying blind.

Meta-Dimension 2: Leverage

Leverage is how much change you get per dial twist. A good dimension is one you can actually move—and that, when moved, actually matters.

Dimensionalization unlocks Leverage. When you know the dimensions of a situation, you can manipulate inputs and predict outputs. You move from “is this good?” to “how does this score along the axes I care about?” Tradeoffs stop being vague feelings and become something you can reason about.

What makes a dimension high-Leverage?

• Action: You can turn the dial.
• Impact: The dial does something when you turn it.

High-Leverage examples:

• Parenting: autonomy vs stimulation, structure vs flexibility.
• Climbing: difficulty, exposure, endurance.
• Career: compensation, autonomy, growth potential, mission.
• Software engineering: modularity, performance, maintainability.

Low-Leverage examples:

• Platitudes you can’t directly control (e.g. “more strategic” or “better parent”).
• Metrics you don't need (e.g. “innovativeness” or “meeting satisfaction score”).
• Dials that don’t matter (e.g. “gym playlist bpm”).

High-Leverage dimensions are the ones that make tradeoffs visible and worthwhile. When you can dimensionalize, you can identify levers and see what happens when you pull them.

Meta-Dimension 3: Complexity

Dimensionalization only works if you can actually use it. Too many dials and you’ll burn out. Dimensionalization is powerful because it’s selective. It lets you summarize experience in low-dimensional space that your brain can handle.

Complexity is the price you pay to keep your dimensional model in RAM. If you can’t hold the dials in your head, you’ll fall back on gut or default heuristics.

What contributes to Complexity?

• Cognitive Load: More sliders = more juggling.
• Overfitting: Some dimensions give you theoretical resolution but no practical benefit.

Low-Complexity (good) examples:

• Exercise: strength, endurance, recovery.
• Personal finance: savings rate, risk tolerance, liquidity.
• Parenting: hunger, tiredness, stimulation, attention.

High-Complexity (bad) examples:

• Modeling your fitness plan on 30 biometric streams.
• Tracking 50+ OKRs in a five-person company.
• Running a life dashboard that requires updating a database.

Good dimensionalizations are efficient, not exhaustive. They give you a small set of sliders that explain a lot.

A dimensionalization of emotion. How does it do on Fidelity/Leverage/Complexity?

Summary and Other Considerations

We have dimensionalized dimensionalization into three parts:

• Fidelity: how well the axes map to reality
• Leverage: what changes when we move along an axis
• Complexity: the price we pay to use the model

These meta-dimensions are dials you can tune as you practice dimensionalizing in your own life.

The meta-dimensions do trade off against one another. If you add a dimension to improve Fidelity, you pay for it in Complexity. If you swap an inflexible dimension to one you can modify, improving Leverage, you may reduce Fidelity if the new dimension isn’t causal.

See below for some more considerations, including:

• Continuous vs Categorical Dimensions¹
• Using AI Tools to Dimensionalize²
• Add or Remove an Axis?³
• Related Terms Across Fields⁴

Conclusion

Dimensionalization is a lens. It is what smart judgment looks like under the hood.

It’s what you do when you shift from asking “is this good?” to “where is this strong, and where is it weak?”.

It lets you reason instead of react. It lets you compare things that feel incomparable. It helps you optimize without being reductive.

You can try dimensionalizing the next time you need to make a decision, review a meal, or understand a feeling.

Learn to see dimensions as dials. It makes the world editable.

1

Continuous vs Categorical Dimensions

Categorization says: this is a drama, that is a comedy. Dimensionalization asks: how funny? how tragic? how character-driven? how plot-heavy?

Continuous sliders give you nuance, tradeoff analysis, optimization. Categorical labels are sometimes better for simplicity or communication.

• Continuous: Return %, risk level, time to delivery.
• Categorical: Genre, cuisine, product tier.

Tradeoffs again! Dimensionalize when it helps. Categorize when it doesn’t hurt.

2

Using LLMs to Dimensionalize

LLMs can be powerful allies in dimensionalization. You can use them to brainstorm plausible axes in a new domain, rephrase or cluster raw input data into latent dimensions, or simulate how tweaking one variable might cascade through others. LLMs can help extract candidate dimensions, rank them by salience, and even map them onto continuous scales. The key is to treat the LLM not as a decision-maker, but as a dimension-spotter: it helps you surface and test possible ways the world might be sliced.

Try this prompt:

“I am thinking about ______. Dimensionalize it, by identifying dimensions that map to reality (Fidelity), that are in my control (Leverage), and that don’t overfit (Complexity).”

Then you can try:

“Now rank the dimensions by Leverage”

or

“Try making 10 more and then combining them all into a MECE grouping”.

3

Add or Remove an Axis?

Adding an axis is only worth it if:

• It explains variance not captured by others.
• It changes the ranking of options.
• It lets you make a better decision.

Otherwise, it’s a shiny distraction. Often you are better off combining dimensions to reduce Complexity at the potential cost of Fidelity.

4

Related Terms Across Fields

Different domains name the same move differently:

• English (Subtlety): Balancing conflicting effects—tone, pace, diction—without maxing any one.
• Machine Learning (Featurization): Choosing axes of input variation that shadow causality.
• Parenting (Maturity): Seeing and balancing many developmental needs instead of maximizing a single virtue.
• Software Engineering (Architecture): Designing around modularity, latency, scaling, cost.
• Art (Composition): Spatial arrangement of elements with rhythm, emphasis, balance.
• Exercise (Periodization): Coordinating training variables across time—intensity, recovery, volume.
• Spirituality (Integration): Weaving together mindfulness, ethics, discipline, insight, and grace into a practice.

These are all examples of dimensionalization in domain jargon.