You’ve probably seen those hyper-realistic video game maps or GPS overlays that look almost identical to the real world. They’re impressive. But there’s a weird paradox here that Jorge Luis Borges, the famous Argentine writer, nailed in a tiny, one-paragraph story called On Exactitude in Science.
He imagined an empire where the art of cartography reached such perfection that they built a map of the empire that was the exact size of the empire itself. Every house, every tree, every blade of grass—one-to-one scale.
It was useless.
Think about it. If a map is as big as the territory, you can’t unfold it. You can’t look at it to find your way. It just covers the ground and blocks the sun. Eventually, according to the story, the next generations realized this "perfection" was total nonsense, and they left the map to rot in the desert. This isn't just a clever fable from 1946; it’s a fundamental warning for how we handle data, AI, and scientific modeling today.
The Trap of Data Obsession
We live in an era where we think more data always equals more truth.
It doesn't.
When people talk about on exactitude in science, they’re usually wrestling with the "Signal vs. Noise" problem. If you’re a data scientist trying to predict the stock market, you might think that tracking every single tweet, every weather pattern, and every shipping container movement will give you the "perfect" model.
But you’ll just end up with "overfitting."
Overfitting is basically the digital version of Borges' giant map. Your model becomes so specific to the past data—the "exact" territory—that it fails to predict anything about the future. It’s too rigid. It’s too heavy. Science requires abstraction. To understand the world, we have to simplify it. We have to ignore the "noise" to see the "signal."
Honestly, a "perfect" science would just be a duplicate of reality, and we already have reality. We don't need a second one that’s just as confusing as the first.
Why Models Must "Lie" to Be Useful
A map is useful specifically because it leaves things out.
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If you're using Google Maps to walk to a coffee shop, you don't need to know the chemical composition of the asphalt or the name of every person living in the apartments you pass. That information is "exact," but it’s irrelevant.
In the philosophy of science, this is often discussed through the lens of Alfred Korzybski, who famously said, "The map is not the territory." He was pointing out that our mental models, our words, and our scientific equations are just reductions.
The Bohr Model Example
Take the way we teach atoms in school. You probably remember the little solar system model—the Bohr model—with electrons orbiting a nucleus like planets.
- It’s factually wrong.
- Electrons don't move in neat circles.
- They exist in "probability clouds."
But we still teach the Bohr model because it’s a useful lie. It’s "exact" enough to help a high schooler understand chemical bonding without needing to dive into the nightmare of multi-dimensional wave functions. If we insisted on total exactitude from day one, nobody would ever pass chemistry.
The Problem with Digital Twins
In the 2020s, the concept of the "Digital Twin" became a massive trend in engineering and urban planning. Companies like Siemens and GE build digital replicas of wind turbines or entire factories to run simulations.
It’s the closest we’ve ever come to the Borges story.
But even here, engineers have to decide where the exactness ends. Do you simulate the molecular fatigue of every bolt? Probably not. You’d run out of computing power before the simulation even started. The genius of science isn't in being 100% accurate; it's in knowing exactly which 5% of the data you can safely ignore.
The Human Cost of Exactness
There's a dark side to this obsession with perfect measurement. When we apply the logic of on exactitude in science to human beings, things get messy fast.
Social sciences often try to mimic the "hard" sciences by turning people into numbers. We see this in "hyper-quantified" workplaces where every keystroke is tracked. Management thinks they’re getting an exact map of productivity.
What they’re actually getting is a distorted mess.
People change their behavior when they’re being measured—a version of the observer effect in physics. If you measure a coder by "lines of code written," they’ll write long, bloated, crappy code to hit their targets. The "exact" metric kills the actual goal (good software).
Borges’ ruined map in the desert is a metaphor for any system that values the metric more than the reality it’s supposed to represent.
Moving Beyond the "Perfect" Map
So, how do we actually use this?
If you’re a business owner, a student, or just someone trying to make sense of the news, you have to embrace "Good Enough" modeling.
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- Identify the Scale: Before you start a project, ask what "scale" you need. Are you looking at the forest or the trees? Don't collect "tree" data if you’re trying to move the "forest."
- Kill the Noise: If a piece of information doesn't change your next decision, it’s clutter. Toss it.
- Respect the Margin of Error: Every scientific "fact" has a boundary. Newton’s laws of motion are "exact" until you start moving near the speed of light, and then Einstein takes over. Knowing where your map stops being true is more important than the map itself.
The quest for absolute exactitude is a dead end. Science is a tool for navigation, not a replacement for the world. We need maps we can fold up and put in our pockets, not ones that cover the very ground we're trying to walk on.
Actionable Insights for Navigating Complex Data
- Audit your metrics: Look at your most used KPIs. If they are 100% "exact" but don't actually correlate to success, delete them.
- Embrace heuristic thinking: Sometimes a "rule of thumb" (a simplified map) is more effective than a complex algorithm because it allows for faster pivoting.
- Study the outliers: Exactness often hides the most interesting things. When the map doesn't match the ground, don't assume the ground is wrong. That "error" is usually where the next scientific discovery is hiding.
Stop trying to build a 1:1 replica of your life or your business. Focus on the highlights, the turns, and the destination. The rest is just paper in the wind.