Why Do Our Minds Refuse to Pick Just One Rule of Reasoning?

Most of the time, we don’t notice how we decide. We just do it. Whether it’s a restaurant menu, a job offer, or a fork in the road we’re pondering, our choice feels immediate; it’s like instinct. But underneath, our minds run little games of logic and intuition, sometimes strict as a calculator, sometimes loose as a hunch.

Psychologists have given names to these games. One family of rules is all about speed and thrift. These are the fast-and-frugal heuristics: the shortcuts that tell us, “Don’t overthink this.” One version, the Priority Heuristic, says: check the worst-case scenario first—if it’s tolerable, move on. Another, Take The Best, says: look at the most important clue and stop right there. If the city’s a capital, it’s probably bigger. If the hotel has a terrible rating, don’t book it. These rules don’t bother with weighing every detail—they’re about escaping the swamp of indecision.

The other family of rules is slower, heavier, and more deliberate. Here we add and weigh and compensate: a weak score in one column can be rescued by strength in another. Economists call this Expected Utility Theory: multiply the odds and payoffs, and the math will lead you home. Psychologists gave it a softer sibling, the Weighted Additive model: tally all the pluses and minuses, each given its proper weight. This is the patient part of the mind, the part that doesn’t like to leave stones unturned.

So which is true of us mere mortals—are we shortcutters or accountants? The answer, like most human things, is messy. The research says we use some of both. We cut corners when gathering information, but once we’ve got it, we tend to use it all. We search like heuristics but decide like compensators.

I see this play out every time I’m eyeing cards for my card collection. Some are long-term keepers: rare slabs of players whose careers I believe will age well, cards that will still give me joy to hold decades from now. Those are compensatory decisions—I weigh expected value, liquidity, history, and aesthetics before committing. But sometimes, the choice is quicker: a card makes me smile, the price is fair, and I just grab it. That’s the heuristic side—take the best and stop there.

The same happens with books. Some I know I’ll keep forever, reading and re-reading until they’re part of my bloodstream. Others I buy with the clear intention of passing them on—read once, tuck into a Little Free Library, and let someone else take it from there. My brain seems to know when to pause and calculate, and when to just listen to the sharp tug of my instincts.

Machines, oddly enough, don’t do this. Large language models, the kind already people turn to for answers more often every day, behave more like pure compensators. They gather every shred of training data and integrate it into one big, smoothed-out response. Impressive, yes—but also bloated, sometimes absurdly confident, and rarely willing to admit, “I don’t know.”

What if our machines learned to reason the way we do? They need quick, sharp filters to catch the obvious—don’t book the 18-hour layover, don’t ignore the red-flag symptom, don’t buy the warped card at triple its worth. Then, once the noise is cleared, a slower weighing of the options that remain. Finally, we could build in a confession of confidence: I’m 90% sure this is the best choice, but here’s what would change my mind.

We could develop a travel app that admits when two flights are a close call. Future iterations of medical AI that can flag danger in a heartbeat but still respect the lab work before committing. Book recommenders could know when to suggest a lifelong companion and when to steer you toward a passing fling.

The art of choosing is less about pledging allegiance to one rule or the other and more about knowing when to switch, when to leap, and when to linger. We humans already live this way, toggling between instinct and calculation without noticing. If machines want to be less alien and more useful, they’ll have to learn our thought rhythms.

After all, wisdom has never been about speed or precision. It’s always been about balance.