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Book cover for Algorithms to Live By - The Computer Science of Human Decisions

Algorithms to Live By - The Computer Science of Human Decisions

Author: Brian Christian and Tom Griffiths

Rating: ★★★★★

Genre: Psychology

Themes: Cognitive Psychology, Self-improvement, Computer Science

Format: Audio Book

Finished: November 20, 2016

Purchase link

The explanation of the mathematical problems underlying many of our daily tasks has been worth the read, but I wanted the author to leave us with more rules we can implement in our daily lives for overcoming these issues. Instead we get vague guidelines which aren’t directly applicable. I suppose that since we better understand the problems we can manage on our own, but I wanted more than a pop-sci explanation.

The audio book narration on the Audible version is pretty dry.

Practical takeaways

  • err on the side of messiness
  • make sure things are closest to the place where they’re typically used
  • complete the easiest task first
  • slow down to get things done
  • optimism is the best prevention for regret

Quotes

Here are quotes from the book shared with me by Bill Strohm to facilitate a discussion:

  • Tackling real-world task requires being comfortable with chance, trading off time with accuracy, and using approximations. (5)
  • The flow of time turns all decision-making into optimal stopping. (29)
  • Hesitation—inaction—is just as irrevocable as action. (30)
  • A sobering property of trying new things is that the value of exploration, of finding a new favorite, can only go down over time, as the remaining opportunities to savor it dwindle. The flip side is that the value of exploitation can only go up over time. (34-35)
  • The untested rookie is worth more (early in the season, anyway) than the veteran of seemingly equal ability, precisely because we know less about him. Exploration in itself has value, since trying new things increases our chances of finding the best. (41)
  • In the long run, optimism is the best prevention for regret. (45)
  • In general, it seems that people tend to over-explore—to favor the new disproportionately over the best. (52)
  • The deliberate honing of a social network down to the most meaningful relationships is the rational response to having less time to enjoy them. (57)
  • Life should get better over time. (58)
  • With sorting, size is a recipe for disaster. (62)
  • Err on the side of messiness. Sorting something that you will never search is a complete waste; searching something you never sorted is merely inefficient. (72)
  • Soccer’s low scores make game outcomes much closer to random than most fans would prefer to imagine. (77)
  • If your team is eliminated early in the postseason, it’s tough luck. But if your team fails to get to the post-season, it’s tough truth. (79)
  • Biologists tend to think of pecking orders as the violence that preempts violence. (80)
  • The dominant performance of the LRU algorithm in most tests that computer scientists have thrown at it leads to a simple suggestion: turn the library inside out. Put acquisitions int he back, for those who want to find them. And put the most recently returned items in the lobby, where they are ripe for the browsing. (91)
  • Make sure things are in whatever cache is closest to the place where they’re typically used. (95)
  • What we call “cognitive decline”—lags and retrieval errors—may not be about the search process slowing or deteriorating, but (at least partly) an unavoidable consequence of the amount of information we have to navigate getting bigger and bigger. (103)
  • Before you can have a plan, you must first choose a metric. (108)
  • Sometimes that which matters most cannot be done until that which matters least is finished. (115)
  • Most scheduling problems admit no ready solution. (117)
  • Every time you switch tasks, you pay a price. (120)
  • When you context switch you pretty much invalidate all caches. (122)
  • In a thrashing state, you’re making essentially no progress, so even doing tasks in the wrong order is better than doing nothing at all. (124)
  • The best strategy for getting things done might be, paradoxically, to slow down. (124)
  • 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. (126)
  • The reason we can often make good predictions from a small number of observations—or just a single one—is that our priors are so rich. (144)
  • There’s a crucial caveat here, however. In cases where we don’t have good priors, our predictions aren’t good. (145)
  • Learning self-control is important, but it’s equally important to grow up in an environment where adults are consistently present and trustworthy. (147)
  • The best way to make good predictions is to be accurately informed about the things you’re predicting. (147)
  • What we talk about isn’t what we experience—we speak chiefly of interesting things, and those tend to be things that are uncommon. (148)
  • There’s a wisdom to deliberately thinking less. (151)
  • It’s not always better to use a more complex model. (155)
  • It’s incredibly difficult to come up with incentives or measurements that do not have some kind of perverse effect. (157)
  • When it comes to portfolio management, it turns out that unless you’re highly confident in the information you have about the markets, you may actually be better off ignoring that information altogether. (162-162)
  • As a species, being constrained by the past makes us less perfectly adjusted to the present we know but helps keep us robust for the future we don’t. (165)
  • If the factors we come up with first are likely to be the most important ones, then beyond a certain point thinking more about a problem is not only going to be a waste of time and effort—it will lead us to worse solution. (166)
  • If you don’t have a clear read on how your work will be evaluated, and by whom, than 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. (167)
  • When an optimization problem’s constraints say “Do it, or else!,” Lagrangian Relaxation replies, “Or else what?” Once we can color outside the lines—even just a little bit, and even at a steep cost—problems become tractable that weren’t tractable before. (178)
  • Even when you don’t commit the infraction, simply imagining it can be illuminating. (181)
  • In a game like solitaire, reasoning your way through the space of possibilities gets almost instantly overwhelming. After trying some elaborate combinatorial calculations . . . and giving up, Ulam landed on a different approach, beautiful in its simplicity: just play the game. (184)
  • A statistic can only tell us part of the story, obscuring any underlying heterogeneity. And often we don’t even know which statistic we need. (191)
  • A close examination of random samples can be one of the most effective means of making sense of something too complex to be comprehended directly. (191-192)
  • If it looks like you’re stuck, mix things up a little. (196)
  • Sometimes it’s best not to get too attached to an initial direction that shows promise, and simply start over from scratch. (197)
  • Communication is one of those delightful things that work only in practice; in theory it’s impossible. (210)
  • In an unpredictable and changing environment, pushing things to the point of failure is indeed sometimes the best (or the only) way to use all the resources to their fullest. What matters is making sure that the response to failure is both sharp and resilient. (220)
  • A poor listener destroys the tale. (221)
  • One of the fundamental principles of buffers, be they for packets or patrons, is that they only work correctly when they are routinely zeroed out. (224)
  • At a crowded party we inevitably participate in less than 5% of the conversation, and cannot read up or catch up on the remainder. Photons that miss the retina aren’t queued for later viewing. In real life, packet loss is almost total. (225)
  • The problem isn’t that we’re always connected; we’re not. The problem is that we’re always buffered. The difference is enormous. (226)
  • Something very important happens once somebody decides to follow blindly his predecessors independently of his own information signal, and that is that his action becomes uninformative to all later decision makers. (250)
  • Be wary of cases where public information seems to exceed private information, where you know more about what people are doing than why they’re doing it, where you’re most concerned with your judgments fitting the consensus than fitting the facts. (251)
  • Actions are not beliefs; cascades get caused in part when we misinterpret what others think based on what they do. We should be especially hesitant to overrule our own doubts. (251)
  • Seek out games where honesty is the dominant strategy. Then just be yourself. (255)
  • If you follow the best possible process, then you’ve done all you can, and you shouldn’t blame yourself if things didn’t go your way. (257)
  • We can hope to be fortunate—but we should strive to be wise. (257)
  • Computation is bad: the underlying directive of any good algorithm is to minimize the labor of thought. (258)