📖 Short Summary (1 takeaway)

• Applying some crucial algorithms developed for computer science and math can be applied to real life. These span use cases like decision making, sorting, determining the best, storing information, making guesses, optimize things, think heuristically, how modern technology works

🧐 Why I am reading this book

• Part of [[8. ☕️ Finer Things Book Club]]

🙊 Great quotes

Over the past decade or two, behavioral economics has told a very particular story about human beings: that we are irrational and error-prone, owing in large part to the buggy, idiosyncratic hardware of the brain. 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” The math shows that you should always keep playing. But if you follow this strategy, you will eventually lose everything. Some problems are better avoided than solved. Spend the afternoon. You can’t take it with you. When balancing favorite experiences and new ones, nothing matters as much as the interval over which we plan to enjoy them. The old adage tells us that “the grass is always greener on the other side of the fence,” but the math tells us why: the unknown has a chance of being better, even if we actually expect it to be no different, or if it’s just as likely to be worse. Following the advice of these algorithms, you should be excited to meet new people and try new things—to assume the best about them, in the absence of evidence to the contrary. Data scientist Jeff Hammerbacher, former manager of the Data group at Facebook, once told Bloomberg Businessweek that “the best minds of my generation are thinking about how to make people click ads.” In general, it seems that people tend to over-explore—to favor the new disproportionately over the best. Sorting something that you will never search is a complete waste; searching something you never sorted is merely inefficient. Ironically, in Single Elimination no tournament structure is actually necessary at all. Any 63 games will yield a single undefeated champion. For instance, you could simply have a single “king of the hill” team take on challengers one by one until it is dethroned, at which point whoever defeated it takes over its spot and continues. As Trick points out, sports leagues aren’t concerned with determining the rankings as quickly and expeditiously as possible. Instead, sports calendars are explicitly designed to maintain tension throughout the season, something that has rarely been a concern of sorting theory. In the practical use of our intellect, forgetting is as important a function as remembering. LRU consistently performed the closest to clairvoyance. “temporal locality”: if a program has called for a particular piece of information once, it’s likely to do so again in the near future. Unless we have good reason to think otherwise, it seems that our best guide to the future is a mirror image of the past. The nearest thing to clairvoyance is to assume that history repeats itself—backward. The key idea behind Anderson’s new account of human memory is that the problem might be not one of storage, but of organization. According to his theory, the mind has essentially infinite capacity for memories, but we have only a finite amount of time in which to search for them. “If you’re flammable and have legs, you are never blocking a fire exit.” “The TeX Tuneup of 2014,” in which he fixed all of the bugs that had been reported in his TeX typesetting software over the previous six years. His report ends with the cheery sign-off “Stay tuned for The TeX Tuneup of 2021!” Alert me only once every ten minutes, say; then tell me everything. Our days are full of “small data.” Perhaps we had already intuited as much, but Bayes’s logic offers us the ability to quantify that intuition. In fact, for any possible drawing of w winning tickets in n attempts, the expectation is simply the number of wins plus one, divided by the number of attempts plus two: (w+1)⁄(n+2). This incredibly simple scheme for estimating probabilities is known as Laplace’s Law, and it is easy to apply in any situation where you need to assess the chances of an event based on its history. And the beauty of Laplace’s Law is that it works equally well whether we have a single data point or millions of them. The mathematical formula that describes this relationship, tying together our previously held ideas and the evidence before our eyes, has come to be known—ironically, as the real heavy lifting was done by Laplace—as Bayes’s Rule. The Erlang distribution gives us a third kind of prediction rule, the Additive Rule: always predict that things will go on just a constant amount longer. These three very different patterns of optimal prediction—the Multiplicative, Average, and Additive Rules—all result directly from applying Bayes’s Rule to the power-law, normal, and Erlang distributions, respectively. In other words, the ability to resist temptation may be, at least in part, a matter of expectations rather than willpower. Failing the marshmallow test—and being less successful in later life—may not be about lacking willpower. It could be a result of believing that adults are not dependable: that they can’t be trusted to keep their word, that they disappear for intervals of arbitrary length. Learning self-control is important, but it’s equally important to grow up in an environment where adults are consistently present and trustworthy. you want to be a good intuitive Bayesian—if you want to naturally make good predictions, without having to think about what kind of prediction rule is appropriate—you need to protect your priors. The lesson is this: it is indeed true that including more factors in a model will always, by definition, make it a better fit for the data we have already. But a better fit for the available data does not necessarily mean a better prediction. Business plans get compressed to an elevator pitch; life advice becomes proverbial wisdom only if it is sufficiently concise and catchy. And anything that needs to be remembered has to pass through the inherent Lasso of memory. When we start designing something, we sketch out ideas with a big, thick Sharpie marker, instead of a ball-point pen. Why? Pen points are too fine. They’re too high-resolution. They encourage you to worry about things that you shouldn’t worry about yet, like perfecting the shading or whether to use a dotted or dashed line. The first, Constraint Relaxation, simply removes some constraints altogether and makes progress on a looser form of the problem before coming back to reality. The second, Continuous Relaxation, turns discrete or binary choices into continua: when deciding between iced tea and lemonade, first imagine a 50–50 “Arnold Palmer” blend and then round it up or down. The third, Lagrangian Relaxation, turns impossibilities into mere penalties, teaching the art of bending the rules Metropolis named this approach—replacing exhaustive probability calculations with sample simulations—the Monte Carlo Method, Aggregate statistics, on the other hand, are the reverse: comprehensive but thin. As Harvard’s Michael Mitzenmacher puts it, “What we’re going to do is come up with an answer which saves you in time and space and trades off this third dimension: error probability.” The river meanders because it can’t think. Protocol is how we get on the same page; in fact, the word is rooted in the Greek protokollon, “first glue,” which referred to the outer page attached to a book or manuscript. The technology that ate circuit switching’s lunch would become known as packet switching. In packet switching, on the other hand, the proliferation of paths in a growing network becomes a virtue: there are now that many more ways for data to flow, so the reliability of the network increases exponentially with its size. The way that ACKs work is both simple and clever. Behind the scenes of the triple handshake, each machine provides the other with a kind of serial number—and it’s understood that every packet sent after that will increment those serial numbers by one each time, like checks in a checkbook. Real-time voice communications, such as Skype, typically do not use TCP, if you lose a packet, you just say, ‘Say that again, I missed something.’” In one of the seminal results in game theory, the mathematician John Nash proved in 1951 that every two-player game has at least one equilibrium. In a game-theory context, knowing that an equilibrium exists doesn’t actually tell us what it is—or how to get there. In fact, this makes defection not merely the equilibrium strategy but what’s known as a dominant strategy. A dominant strategy avoids recursion altogether, by being the best response to all of your opponent’s possible strategies—so you don’t even need to trouble yourself getting inside their head at all. A dominant strategy is a powerful thing. The price of anarchy measures the gap between cooperation (a centrally designed or coordinated solution) and competition (where each participant is independently trying to maximize the outcome for themselves). Whenever you find yourself on the side of the majority, it is time to pause and reflect. Here game theory offers a sobering perspective: catastrophes like this can happen even when no one’s at fault. Dutch auctions are more prevalent than they might initially seem. A store marking down its unsold items, and landlords listing apartments at the highest price they think the market will bear, both share its basic quality: the seller is likely to begin optimistically and nudge the price down until a buyer is found. In an English auction, bidders alternate raising the price until all but one of them drop out. This seems to offer something closer to what we want: here, if you value an item at $25 and I value it at$10, you’ll win it for just over $10 without either having to go all the way to$25 or disappearing down the strategic rabbit hole. However, in a Vickrey auction, the winner ends up paying not the amount of their own bid, but that of the second-place bidder. That is to say, if you bid $25 and I bid$10, you win the item at my price: you only have to pay $10. In a Vickrey auction, on the other hand, honesty is the dominant strategy. This is the mechanism designer’s holy grail. You do not need to strategize or recurse. In a landmark finding called the “revelation principle,” Nobel laureate Roger Myerson proved that any game that requires strategically masking the truth can be transformed into a game that requires nothing but simple honesty. The 37% Rule, the Least Recently Used criterion for handling overflowing caches, and the Upper Confidence Bound as a guide to exploration are all examples of this. 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. Call it a kind of computational Stoicism. If you wind up stuck in an intractable scenario, remember that heuristics, approximations, and strategic use of randomness can help you find workable solutions. sometimes “good enough” really is good enough. What’s more, being aware of complexity can help us pick our problems: These aren’t the concessions we make when we can’t be rational. They’re what being rational means.

✅ Actionable item

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🗂 Detailed Summary