Algorithms to Live By: The Computer Science of Human Decisions

Look-Then-Leap Rule: You set a predetermined amount of time for “looking”—that is, exploring your options, gathering data—in which you categorically don’t choose anyone, no matter how impressive. After that point, you enter the “leap” phase, prepared to instantly commit to anyone who outshines the best applicant you saw in the look phase.

The math shows that when there are a lot of applicants left in the pool, you should pass up even a very good applicant in the hopes of finding someone still better than that—but as your options dwindle, you should be prepared to hire anyone who’s simply better than average. It’s a familiar, if not exactly inspiring, message: in the face of slim pickings, lower your standards. It also makes clear the converse: with more fish in the sea, raise them. In both cases, crucially, the math tells you exactly by how much.

Merrill Flood. Though he is largely unheard of outside mathematics, Flood’s influence on computer science is almost impossible to avoid. He’s credited with popularizing the traveling salesman problem (which we discuss in more detail in chapter 8), devising the prisoner’s dilemma (which we discuss in chapter 11), and even with possibly coining the term “software.”

In any optimal stopping problem, the crucial dilemma is not which option to pick, but how many options to even consider.

The earliest known mathematical algorithms, however, predate even al-Khwārizmī’s work: a four-thousand-year-old Sumerian clay tablet found near Baghdad describes a scheme for long division.

multi-armed bandit problem.

how to best approach problems whose optimal answers are out of reach. How to relax.
Just Relax

The verdict is clear: ordering your bookshelf will take more time and energy than scanning through it ever will.

As a consequence, considering more and more factors and expending more effort to model them can lead us into the error of optimizing for the wrong thing

Upper Confidence Bound algorithm