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February 9 - February 17, 2025
Optimal stopping tells us when to look and when to leap. The explore/exploit tradeoff tells us how to find the balance between trying new things and enjoying our favorites. Sorting theory tells us how (and whether) to arrange our offices. Caching theory tells us how to fill our closets. Scheduling theory tells us how to fill our time.
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.
When balancing favorite experiences and new ones, nothing matters as much as the interval over which we plan to enjoy them.
In other words, something you have no experience with whatsoever is more attractive than a machine that you know pays out 70% of the time!
Exploration in itself has value, since trying new things increases our chances of finding the best. So taking the future into account, rather than focusing just on the present, drives us toward novelty.
Sorting something that you will never search is a complete waste; searching something you never sorted is merely inefficient.
Displacement happens when an animal uses its knowledge of the hierarchy to determine that a particular confrontation simply isn’t worth it.
If you have only a single machine, and you’re going to do all of your tasks, then any ordering of the tasks will take you the same amount of time.
make your goals explicit.
Do the difficult things while they are easy and do the great things while they are small.
Minimizing the sum of completion times leads to a very simple optimal algorithm called Shortest Processing Time: always do the quickest task you can.
Live by the metric, die by the metric.
The moral here is that a love of getting things done isn’t enough to avoid scheduling pitfalls, and neither is a love of getting important things done.
“Things which matter most must never be at the mercy of things which matter least,”
If you’re working by Earliest Due Date and the new task is due even sooner than the current one, switch gears; otherwise stay the course.
Likewise, if you’re working by Shortest Processing Time, and the new task can be finished faster than the current one, pause to take care of it first; otherwise, continue with what you were doing.
When the future is foggy, it turns out you don’t need a calendar—just a to-do list.
Instead of answering the most important emails first—which requires an assessment of the whole picture that may take longer than the work itself—maybe you should sidestep that quadratic-time quicksand by just answering the emails in random order, or in whatever order they happen to appear on-screen.
The moral is that you should try to stay on a single task as long as possible without decreasing your responsiveness below the minimum acceptable limit. Decide how responsive you need to be—and then, if you want to get things done, be no more responsive than that.
Occam’s razor principle, which suggests that, all things being equal, the simplest possible hypothesis is probably the correct one.
Jump toward the bandwagon, by all means—but not necessarily on
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.
If you can’t solve the problem in front of you, solve an easier version of it—and then see if that solution offers you a starting point, or a beacon, in the full-blown problem. Maybe it does.
Sometimes the best solution to a problem is to turn to chance rather than trying to fully reason out an answer.
“The optimist proclaims that we live in the best of all possible worlds; and the pessimist fears this is true.”
Don’t hate the player, hate the game.
This sounds like a reasonable approach to getting more employees to take vacation, but from a game-theoretic perspective it’s actually misguided. Increasing the cash on the table in the prisoner’s dilemma, for instance, misses the point: the change doesn’t do anything to alter the bad equilibrium.
The problem isn’t that vacations aren’t attractive; the problem is that everyone wants to take slightly less vacation than their peers, producing a game whose only equilibrium is no vacation at
And if you’re the kind of person who always does what you think is right, no matter how crazy others think it is, take heart. The bad news is that you will be wrong more often than the herd followers. The good news is that sticking to your convictions creates a positive externality, letting people make accurate inferences from your behavior. There may come a time when you will save the entire herd from disaster.
As Keynes observed, popularity is complicated, intractable, a recursive hall of mirrors; but beauty, in the eye of the beholder, is not.
Seek out games where honesty is the dominant strategy. Then just be yourself.
Even the best strategy sometimes yields bad results—which is why computer scientists take care to distinguish between “process” and “outcome.” If you followed the best possible process, then you’ve done all you can, and you shouldn’t blame yourself if things didn’t go your way.
If we can be kinder to others, we can also be kinder to ourselves. Not just computationally kinder—all the algorithms and ideas we have discussed will help with that. But also more forgiving.
