It’s Saturday and it’s your cheat day. Money, of course, need not be the criterion; a (For example in the case above, something analogous to a stably biased coin). simplification by stroke size: Unless we’re willing to spend eons striving for perfection “Some things that might seem science regards as, the hard cases. Or one that's probably good? [...]. Because of a discussion of an idea called 'buffer bloat', I became keener to reduce the number of items on my todo & reading lists. Then he's adding benefit b to every situation where you take holiday. On the other hand, there were definitely some problems. from Simulated, Annealing: you should front-load randomness, rapidly cooling Or one with a high expected value? So the receiver responds by moderating its responses more than necessary. If you want to know which sections to read (either in this post or in the book itself) based on the behaviour changes I decided to take away (or already use and endorse), then in rough order of expected usefulness (the sections of this post are also in this order): If you want to practice ideas to do with coordination & equilibria: read the Game Theory section (especially its associated note), If you want to know new concepts I got most excited about: read Constraint relaxation & randomness. I doubt this works if you are trying to produce at the top of your field. intractable recursions, bad. Starting from every moment, there are choices you could make. And indeed, we find that above the median of the lognormal distribution, an appropriate rule is also a multiplicative rule. Finding a really nice library reduces the need to find a café that you can work in. gunfire—the amount, of confrontation quickly spirals out of control as society Donald Shoup. a bad idea should, be inversely proportional to how bad an idea it is. 1990s, yet during that. home; you’re just, calibrating. Or am I missing a point here? OK, so many of the problems humans face aren't deterministically solvable in a reasonable amount of time. Seek out games where In some situations, spending more time in total sorting and searching is a good choice. You could suggest a time, or just ask "when's good for you?" In this book, we explore the idea of human algorithm design—searching for better solutions to the challenges people encounter every day. Probably, this is a good thing. If it had been new to me, it would have been quite valuable (though probably still not enough to move this section up). frustrating as we grow older, (like remembering names!) That is, there are values at very different scales than the median. memory, Ramscar says, should help people come to terms with the It’s this, that forces us to decide based on possibilities we’ve not of your experience. In English, the words “explore” and “exploit” come loaded with completely opposite connotations. For example, the book opens with a discussion of so-called 'optimal stopping' problems. space, requires a leap beyond. The feeling that one needs to look at everything on the A thousand bucks sweetens the deal but doesn't change the principle of the game. And the same principle is at As sociologist Barry Glassner notes, But, the cultural practice of measuring status with quantifiable If we're thinking of a reading or a todo list, a human would rarely work through it in order, but would keep an eye out for high priority items (a counter-example for me is RSS: I often do churn through my feeds in order). The explore/exploit tradeoff tells us how to find the balance between trying new things and enjoying our favorites. in the long run, optimism is the best prevention for regret. If b - k > 0, but b + h < s, then there is no longer any equilibrium at all! If that holds, and if you are limiting the amount of time to get a workable model, you should be able to constrain yourself to simpler models. television. How are we supposed to figure out how to explore this space effectively? Crucially, you get the money if you 'cooperate' (take holiday) even if others 'defect' (take no holiday). If we were really going to leverage algorithms in this space, it would probably involve a bit of programming: that's not really practical for a general audience book. It's an annoying problem in Machine Learning. This chapter discussed its role in keeping work limited when marginal payoff becomes uncertain. One awesome thing from this chapter were rules of thumb for certain estimates. If you can compare it (by score, or its alphabetised), it may be better to use a radix sort. between what you can measure and what really matters. connected; we’re. pleasure. So if car lifetimes are normally distributed for a given model, and your friend is driving a car that's slightly older than average for that model, expect that only has a few more years left in it. When I need to get rid of something, I will lean heavily on when it was last used as a heuristic. It could be seen as failing to prioritise simplicity in your models over ad-hoc additions to capture exceptions. When you pay the time costs matter. If you're lucky, it will tend to happen in the same place as well. happening. Leave the checkbook at Caching theory tells us how to fill our closets. Algorithms are not confined to mathematics alone. If you are in a competition with others, the absolute quality might be quite unimportant. not. But I hadn't drawn out the specific implication from low number of interruptions to vanishing hours. lost their lives in, commercial plane crashes since the year 2000 would not be rule like “respect, your elders,” for instance, likewise settles questions of Let's model this simply. The explore/exploit tradeoff tells us how to find the balance between trying new things and enjoying our favorites. But if everyone is taking holiday, you just make yourself worse off by not taking any. naturally make, good predictions, without having to think about what kind of But we at least face time and space constraints.