Algorithms to Live By: The Computer Science of Human Decisions

Lagrangian Relaxation. The idea behind Lagrangian Relaxation is simple. An optimization problem has two parts: the rules and the scorekeeping. In Lagrangian Relaxation, we take some of the problem’s constraints and bake them into the scoring system instead. That

Regret is the result of comparing what we actually did with what would have been best in hindsight.

Likewise, if none of your email correspondents require you to respond in less than twenty-four hours, you can limit yourself to checking your messages once a day.

This is the first and most fundamental insight of sorting theory. Scale hurts.

First, from Hill Climbing: even if you’re in the habit of sometimes acting on bad ideas, you should always act on good ones. Second, from the Metropolis Algorithm: your likelihood of following a bad idea should be inversely proportional to how bad an idea it is. Third, from Simulated Annealing: you should front-load randomness, rapidly cooling out

No matter how good your organization scheme is, having to search through more things will inevitably take longer. It’s not that we’re forgetting; it’s that we’re remembering. We’re becoming archives.

Do the difficult things while they are easy and do the great things while they are small.

First, from Hill Climbing: even if you’re in the habit of sometimes acting on bad ideas, you should always act on good ones. Second, from the Metropolis Algorithm: your likelihood of following a bad idea should be inversely proportional to how bad an idea it is. Third, from Simulated Annealing: you should front-load randomness, rapidly cooling out

Religion seems like the kind of thing a computer scientist rarely talks about; in fact, it’s literally the subject of a book called Things a Computer Scientist Rarely Talks About. But by reducing the number of options that people have, behavioral constraints of the kind imposed by religion don’t just make certain kinds of decisions less computation