Why One Map Is Never Enough: Navigating Reality with Multiple Models

 If you’ve ever tried to navigate New York City with an outdated subway map, you’ll know how quickly a “helpful guide” can turn into a recipe for getting lost. That’s because a map is not the territory. It’s just a compressed, simplified way to describe reality. And here’s the kicker: there isn’t just one “correct” map—there are multiple. A street map, a restaurant guide, a real-estate zoning plan—each describes the same city but with a completely different lens. Sometimes, the map even changes the territory itself. Just think of NYC’s city planning: decisions drawn in ink eventually get poured in concrete.

The same is true for how we understand the world. Our mental models are just approximations of reality. None of them are perfect. Each comes with blind spots, biases, and assumptions. Why do we cling so hard to different perspectives? Often it’s because of incentives, baked-in human biases, and the gravitational pull of group identity. "Us vs. them" isn’t just a tribal instinct—it’s a shortcut for making sense of complexity. But shortcuts, like single maps, can leave us walking in circles.

That’s why we need multiple mental models. One lens won’t cut it. If you only have a hammer, you’ll start looking for nails everywhere—and probably smash a few things that weren’t nails to begin with. But when you collect a toolkit of maps and models—economic, psychological, historical, biological—you begin to triangulate reality. Each angle corrects the distortion of another. Like overlaying several maps, you get closer to a territory you can actually navigate.

But having multiple maps isn’t enough—you also need to keep them updated. Maps age. Roads get blocked by construction, parades reroute traffic, new subway lines open. If you cling to yesterday’s map, you’ll walk into a dead end. The same goes for mental models: the world changes, and so should your assumptions.

This is where Bayesian thinking offers a useful analogy. In Bayesian inference, you start with a “prior” (your best guess so far), then update it with each new piece of evidence. See fresh data? Adjust your belief. Spot an anomaly? Reweight your assumptions. Over time, your model evolves closer to reality. In daily life, that might look like updating your career map after a market shift, revising your health habits after new research, or rethinking group assumptions after a surprising conversation.

So, the practice becomes: collect multiple maps, and keep them current. Be willing to toss out the parts that no longer serve you, redraw the areas that have shifted, and annotate the edges with observations from lived experience.

Because the richest journeys happen not when you walk with one map, but when you learn to switch between many—and continuously update them along the way.