What if algorithms optimized for regret minimization?

This is a placeholder post demonstrating the Imagine format. Replace with actual content.

One idea. Probably nonsense.

What if recommendation systems didn’t optimize for engagement or even satisfaction, but for minimizing long-term regret?

The metric would be: “Six months from now, will you wish you hadn’t spent that time?”

Most feeds optimize for the immediate hit—the dopamine spike of the next scroll. But what if we flipped it? What if the algorithm asked: “Will this person look back and feel good about having consumed this content?”

The challenge: regret is personal, delayed, and hard to measure. You’d need to survey users months later, or infer regret from behavioral signals like unfollows, account deletions, or “I need to touch grass” posts.

Still, the exercise is useful. It forces us to ask: what are we actually optimizing for, and is that what we should be optimizing for?