If you’ve ever tried to follow a GPS that insisted you were driving through a lake when you were clearly on a paved road, you’ve felt the friction between data and reality. It’s annoying. But it also points to a massive philosophical trap that scientists and engineers have been falling into for centuries. Jorge Luis Borges, the Argentine writer, nailed this feeling in a one-paragraph short story called On Exactitude in Science. He imagined an empire where the art of cartography reached such a fever pitch of "perfection" that they built a map of the empire that was the same size as the empire itself.
Imagine that for a second.
A map that covers every square inch of the ground it’s supposed to represent. It’s useless. It’s a 1:1 scale disaster. If you want to see where the bakery is, you have to unroll a mile of paper over the actual bakery. Eventually, the story goes, the next generations realized this was "useless" and left the map to rot in the desert.
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This isn't just a clever fable. It is the single most important warning for how we handle Big Data, climate modeling, and Artificial Intelligence today. We are obsessed with precision. We want more pixels, more data points, more "exactness." But there is a point where a model stops being a tool and starts being a burden.
The Precision Trap in Modern Modeling
We live in a world obsessed with digital twins.
Whether it’s a virtual replica of a jet engine or a city-wide simulation for traffic flow, the goal is always higher fidelity. But On Exactitude in Science teaches us that fidelity isn't the same as utility. In fact, if your model is as complex as the thing it's modeling, you haven't solved the problem; you've just moved it.
Take climate science.
Researchers like those at the National Center for Atmospheric Research (NCAR) deal with this constantly. If you try to simulate every single molecule in the atmosphere, your computer will melt before you get a three-day forecast. You have to lose information to gain insight. You have to "smear" the reality. Scientists call this "parameterization." You take a messy, chaotic process—like how a single cloud forms—and you turn it into a simplified mathematical average.
Is it "exact"? No. Is it useful? Absolutely.
If we aimed for total exactitude, we’d be like Borges’ cartographers, standing in the middle of a desert with a decaying map that’s too big to read.
When Data Becomes Noise
There’s a weird thing that happens in statistics called overfitting. Honestly, it’s the modern version of the 1:1 map.
You’ve got a bunch of data points. You want to find a pattern. If you make your mathematical formula too "exact," it will hit every single point on the graph perfectly. It looks beautiful. But the second you give it new data, it fails. Why? Because it didn't learn the pattern; it just memorized the noise.
It’s like trying to predict what your friend will order for dinner by tracking every single meal they’ve eaten since 2012. You’ve got "exactitude." You know they ate a ham sandwich on a rainy Tuesday in 2015. But that doesn't help you realize they just really like tacos. The noise of the ham sandwich obscures the signal of the taco preference.
Why we crave the 1:1 map
- Fear of uncertainty: We think if we measure everything, nothing can surprise us.
- Computational Hubris: We have the storage space now, so we figure we might as well use it.
- The "More is Better" Fallacy: We confuse the volume of information with the quality of the insight.
Alfred Korzybski, a scholar who lived through the early 20th century, famously said, "The map is not the territory." It sounds simple. Kinda obvious, right? But look at how we treat social media. We look at a filtered, curated feed—a map of someone’s life—and we get depressed because our "territory" (our messy, actual life) doesn't look like their map. We’ve forgotten that the map must leave things out to be a map.
The Science of Being "Wrong Enough"
In engineering, there is a concept called "Effective Theory."
Physicists don't use quantum mechanics to calculate the trajectory of a baseball. They could, technically. They could try to track every subatomic particle in the cowhide and the stitches. But it would be a nightmare. Instead, they use Newtonian physics—which we know is technically "wrong" at the atomic level—because it’s "right enough" for the scale of a baseball.
This is where the genius of On Exactitude in Science really hits home. The value of science isn't in its ability to replicate reality. Reality is already there. We have reality. We don't need a second one.
The value of science is in its ability to abstract.
Abstraction is the act of throwing away the junk so the truth can stand out. If you want to understand how a virus spreads, you don't need to know the favorite color of every person in the city. You need to know their contact rate. By "ignoring" the favorite colors, you make the map readable.
The AI Mirror
We are currently building the largest map in human history: LLMs (Large Language Models).
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These models are trained on basically the entire internet. They are trying to reach a level of On Exactitude in Science where they can predict the next word in any possible sentence. But we’re seeing the "rotting in the desert" phase already. When AI is trained on AI-generated content, the "map" begins to degrade. It’s called "Model Collapse."
Without the "territory" of human experience to ground it, the exactness becomes a feedback loop of nonsense.
It’s a reminder that no matter how many petabytes we throw at a problem, the goal should never be a 1:1 replica. We need the gaps. We need the simplifications.
How to Apply "Less is More" to Your Data
If you’re a developer, a scientist, or just someone trying to make sense of your own life’s data, you need to stop aiming for total exactness.
Start by asking what you can throw away.
If you're building a budget, don't track every 50-cent gum purchase if it makes you quit the whole process in a week. That’s a 1:1 map. It’s too heavy to carry. Track the big stuff. Be "exactly" 80% right rather than "exhaustingly" 99% right.
Actionable Steps for Better Modeling:
- Define your "Scale": Before you start collecting data, decide what level of detail actually changes your decision. If a 5% difference in data won't change your final move, don't waste resources measuring it.
- Audit for "Noise": Look at your metrics. Are you tracking things because they are important, or just because they are easy to measure?
- Embrace the Heuristic: Use rules of thumb. They are the "useful maps" of daily life. "Don't eat things with ingredients you can't pronounce" is a map. It’s not scientifically "exact," but it’s a lot more useful than reading a 400-page chemistry textbook before every snack.
- Test for Overfitting: If your plan or model works perfectly on past events but fails the moment something "weird" happens, your map is too specific. Broaden your strokes.
The map in Borges' story ended up as tattered ruins in the desert, inhabited only by animals and beggars. It’s a haunting image. It’s what happens when we value the representation more than the thing itself.
Science is a tool for navigation, not a replacement for the world. Keep your maps small enough to fit in your pocket. That’s the only way they’ll actually help you get where you’re going.
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Focus on the signal. Let the rest be noise.
Next Steps for Implementation:
Evaluate your current primary project—whether it's a business spreadsheet, a fitness tracker, or a coding project. Identify three data points you are tracking that have never actually influenced a decision you've made. Delete them. Notice how the clarity of the remaining "map" improves when you stop trying to achieve total exactitude.