Algorithms to Live By
An exploration of computer algorithms and how their principles can be applied to everyday decision-making and life problems.
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 of a totally random state, using ever less and less randomness as time goes on, lingering longest as you approach freezing.
there’s reason to think of Tail Drop not as the lamentable consequence of limited memory space but as a purposeful strategy in its own right.
“Once you got somewhere you were happy,” he told the Guardian, “you’d be stupid to shake it up any further.”
“In contrast to the widely held view that less processing reduces accuracy,” they write, “the study of heuristics shows that less information, computation, and time can in fact improve accuracy.” A heuristic that favors simpler answers—with fewer factors, or less computation—offers precisely these “less is more” effects.
Our interviewees were on average more likely to be available when we requested a meeting, say, “next Tuesday between 1:00 and 2:00 p.m. PST” than “at a convenient time this coming week.”
Outcomes make news headlines—indeed, they make the world we live in—so it’s easy to become fixated on them. But processes are what we have control over.
If you don’t have a clear read on how your work will be evaluated, and by whom, then it’s not worth the extra time to make it perfect with respect to your own (or anyone else’s) idiosyncratic guess at what perfection might be.
Being randomly jittered, thrown out of the frame and focused on a larger scale, provides a way to leave what might be locally good and get back to the pursuit of what might be globally optimal.
Early Stopping provides the foundation for a reasoned argument against reasoning, the thinking person’s case against thought.
We can hope to be fortunate—but we should strive to be wise. Call it a kind of computational Stoicism.