Let's pretend that there are an infinite amount of variables in the observable universe — and all variables affect any and every situation and circumstance at all points. There are easy ways to rule out some of those variables if you have some level of intelligence: you can therefore say that intelligence on some level is the ability to identify relevant variables and discard less-relevant (not irrelevant — no variable is irrelevant) variables for every given context.

Taking it one step further is the concept of simplification. Simplification is the act of taking a complex representation of a context and breaking it down further into the most critical variables that still contain the core of the information — through the Pareto principle, or compression, or some other method.

For example — take the fraction 39/111. This is a perfectly valid number and you can picture 39 objects divided from a sum total of 111 objects — but it would be easier to picture 13 objects out of 37 objects (13/37). This is simplification in its most basic form.

The importance of simplification is as follows:

It makes things actionable. A complex representation of a situation is hard to move on. Too many variables, too much noise — and you freeze. Simplification cuts it down to what actually matters right now, so you can make a decision and move. A doctor doesn't recite your entire medical history before treating a broken arm. They identify the most critical variable — the broken arm — and act.

It makes things communicable. You cannot hand someone a thousand variables and expect them to understand you. Simplification is what makes ideas transferable. Every great explanation, every good teacher, every useful diagram — they all work by stripping a thing down until the core is visible. If you can't simplify it, you probably don't understand it well enough yet.

It reduces cognitive load. Your brain has a finite amount of processing power at any given moment. The more variables you're actively tracking, the less bandwidth you have for actually thinking. Simplification is cognitive housekeeping — it clears the desk so you can work.

---

The risk of simplification

Here's where it gets interesting — and where a lot of people go wrong.

Remember: no variable is truly irrelevant. You're not discarding information because it doesn't matter — you're deprioritising it because it matters less right now, in this context. That's a crucial distinction. The moment you forget that, simplification stops being a tool and starts being a blindspot.

The classic example is a map. A map is a simplification of a territory — it strips out most of the detail to show you what's useful for navigation. But if you're a geologist, that same map is nearly useless. The variables the cartographer discarded — soil composition, rock strata, water tables — are exactly the ones you need. Same map, different context, completely different set of critical variables.

This is how simplification fails people:

It can fossilise into assumption. You simplify a situation once, it works, and then you stop re-evaluating. The model becomes fixed. But circumstances change, and a variable you previously ruled out as low-priority can become the most important thing in the room overnight. The person who over-simplifies stops asking "what am I not seeing?" — and that's when they get blindsided.

It can be weaponised. Simplified narratives are easier to sell than complex ones. Politics runs on this. Advertising runs on this. "The problem is X, the solution is Y" — clean, memorable, almost always incomplete. When someone offers you a very clean explanation for a very complicated thing, that's not clarity. That's a flag worth examining.

It can compound into ignorance. Each time you simplify, you make a judgement call about what to keep and what to shelve. Get that judgement wrong consistently, and the errors stack. You end up with a model of reality that feels coherent and familiar but is quietly, dangerously wrong — because you've been discarding the wrong variables for years.

---

The goal, then, isn't to simplify as much as possible. It's to simplify correctly — to find the minimum representation that still contains the core of the truth, without losing the variables that could matter. And to hold that simplified model loosely enough that you can expand it the moment the context demands it.