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Correct Isn't the Same as Right

You can be factually correct and completely wrong. Being right about the data doesn't make you right about the decision.
Correct Isn't the Same as Right

Here's a thought experiment that's been breaking philosophers for fifty years. Two boxes. One has a thousand dollars. The other has either a million or nothing — depending on what a near-perfect predictor thinks you'll do. Take both boxes, logic says you're always $1,000 ahead. But people who take both boxes consistently walk away with a thousand. People who "irrationally" take one walk away with a million.

The argument is airtight. The outcomes are damning.

Correct Isn't the Same as Right

Two competing frameworks — both internally sound, both endorsed by serious thinkers — point in opposite directions. Causal reasoning says the boxes are already set, take both. Evidential reasoning says look at the track record, take one. Neither side is crazy. Neither side has won.

This shows up outside thought experiments too. The pitch that made perfect sense on paper. The hire who checked every box. The strategy that was right by every metric available. The argument was clean. The result was not.

The Question Underneath

Newcomb's paradox has no consensus answer after fifty years. That's not an accident — it's a signal that the frameworks themselves are incomplete. That rationality, as most people practice it, is optimised for being justifiable, not for being right.

The real question isn't one box or two. It's: are you choosing a framework because it produces good outcomes, or because it lets you defend yourself afterwards?

Those aren't the same thing. Most people optimise for the second and call it discipline.

Stop using rigour as a shield. If correct decisions keep producing bad results, the problem isn't bad luck. Start there.