Posts Tagged ‘excerptedquote’

N.Y.C. Public Schools Will Not Fully Reopen in September – The New York Times

July 14, 2020

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City Hall does not yet know precisely how many parents are planning to keep their children home from school but will begin formally asking families next week. If the number of students who opt for full-time remote learning turns out to be much higher or lower than anticipated, the models could change again. Like many urban school districts, New York has moved away from neighborhood high schools to schools that admit students from all over the city — many of whom have long trips on public transportation.

A Department of Education survey of about 400,000 parents found that about 75 percent of families are tentatively willing to send their children back to school.

He said the proposal, with students attending school physically for a range of one to three days a week, does not allow his family to do much specific planning.
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….
https://www.nytimes.com/2020/07/08/nyregion/nyc-schools-reopening-plan.html

How Iceland Beat the Coronavirus | The New Yorker

June 29, 2020

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By sequencing the virus from every person infected, researchers at deCODE could also make inferences about how it had spread.

“We
saw what was going on in China,” she recalled. “We saw the pictures of people lying dead in emergency departments, even on the street. So it was obvious that something terrible was happening. And, of course, we didn’t know if it would spread to other countries. But we didn’t dare take the chance. So we started preparing.” For example, it was discovered that the country didn’t have enough protective gear for its health-care workers, so hospital officials immediately set about buying more.

Meanwhile, Möller began assembling a “backup” team. … When new cases started to be diagnosed in a great rush, the backup team, along with doctors whose offices had been shut by the pandemic, counselled people over the phone. “If you were seventy, if you had high blood pressure, you got called every day,” Möller told me. “But, if you were young and healthy, maybe twice a week. And I’m sure that this led to fewer hospital admittances and even to fewer intensive-care admittances.”

This, in turn, appears to have cut down on fatalities. Iceland’s death rate from covid-19 is one out of every one hundred and eighty confirmed cases, or just 0.56 per cent—one of the lowest in the world. …
Arnarson, who represents, among others, the Danish-Icelandic artist Olafur Eliasson, had been in New York, attending the Armory Show, at the beginning of March. After the show ended, he’d gone to a crowded party where finger food was served. “I’m not a news guy,” he told me. “But I knew what was going on here in Iceland, and I knew what was going on in Europe. And I was struck by how New Yorkers were so confident. They didn’t believe it was going to happen, or, if it was going to happen, somehow it was going to be O.K.”
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https://www.newyorker.com/magazine/2020/06/08/how-iceland-beat-the-coronavirus

iPad Notebook export for The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution

June 15, 2020

The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution Zuckerman, Gregory
Citation (APA): Zuckerman, G. (2019). The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution [Kindle iOS version]. Retrieved from Amazon.com

Some quick quotes from the book above are below.

QT:{{”
Part One: Money Isn’t Everything
Highlight(pink) – Chapter Six > Page 109 · Location 1754
Laufer also uncovered how the previous day’s trading often can predict the next day’s activity, something he termed the twenty-four-hour effect. The Medallion model began to buy late in the day on a Friday if a clear up-trend existed, for instance, and then sell early Monday, taking advantage of what they called the weekend effect.
Highlight(pink) – Chapter Seven > Page 131 · Location 2073
The Morgan Stanley traders became some of the first to embrace the strategy of statistical arbitrage, or stat arb. This generally means making lots of concurrent trades, most of which aren’t correlated to the overall market but are aimed at taking advantage of statistical anomalies or other market behavior. The team’s software ranked stocks by their gains or losses over the previous weeks, for example. APT would then sell short, or bet against, the top 10 percent of the winners within an industry while buying the bottom 10 percent of the losers on the expectation that these trading patterns would revert. Highlight(pink) – Chapter Seven > Page 132 · Location 2089
Frey proposed deconstructing the movements of various stocks by identifying the independent variables responsible for those moves. A surge in Exxon, for example, could be attributable to multiple factors, such as moves in oil prices, the value of the dollar, the momentum of the overall market, and more. A rise in Procter & Gamble might be most attributable to its healthy balance sheet and a growing demand for safe stocks, as investors soured on companies with lots of debt. If so, selling groups of stocks with robust balance sheets and buying those with heavy debt might be called for, if data showed the performance gap between the groups had moved beyond historic bounds. A handful of investors and academics were mulling factor investing around that same time, but Frey wondered if he could do a better job using computational statistics and other mathematical techniques to isolate the true factors moving shares.
Part Two: Money Changes Everything
Highlight(pink) – Chapter Twelve > Page 225 · Location 3435
Basket options are financial instruments whose values are pegged to the performance of a specific basket of stocks. While most options are valued based on an individual stock or financial instrument, basket options are linked to a group of shares. If these underlying stocks rise, the value of the option goes up—it’s like owning the shares without actually doing so. Indeed, the banks were legal owners of shares in the basket, but, for all intents and purposes, they were Medallion’s property. The fund’s computers told the banks which stocks to place in the basket and how they should be traded.
Highlight(pink) – Chapter Sixteen > Page 310 · Location 4705 Quant investors had emerged as the dominant players in the finance business. As of early 2019, they represented close to a third of all stock-market trades, a share that had more than doubled since 2013.6 Highlight(pink) – Chapter Sixteen > Page 311 · Location 4719 The rage among investors is for alternative data, which includes just about everything imaginable, including instant information from sensors and satellite images around the world. Creative investors test for money-making correlations and patterns by scrutinizing the tones of executives on conference calls, traffic in the parking lots of retail stores, records of auto-insurance applications, and
recommendations by social media influencers.
Highlight(pink) – Chapter Sixteen > Page 311 · Location 4727 To explore these new possibilities, hedge funds have begun to hire a new type of employee, what they call data analysts or data hunters, who focus on digging up new data sources, much like what Sandor Straus did for Renaissance in the mid-1980s.
Highlight(pink) – Chapter Sixteen > Page 312 · Location 4744 A quip by novelist Gary Shteyngart sums up the future path of the finance industry, and the direction of broader society: “When the shrinks for their kids are replaced by algorithms, that’ll be the end; there’ll be nothing left.”
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iPad Notebook export for Algorithms to Live By: The Computer Science of Human Decisions

June 5, 2020

Notebook Export
Algorithms to Live By: The Computer Science of Human Decisions Christian, Brian; Griffiths, Tom
Citation (APA): Christian, B., & Griffiths, T. (2016). Algorithms to Live By: The Computer Science of Human Decisions [Kindle iOS version].

some quick quotes from the book that I liked are listed below

1. Optimal Stopping: When to Stop Looking
Highlight(pink) – Page 15 · Location 258
Assuming that his search would run from ages eighteen to forty, the 37% Rule gave age 26.1 years as the point at which to switch from looking to leaping.
2. Explore/Exploit: The Latest vs. the Greatest
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In 1969, Marvin Zelen, a biostatistician who spent most of his career at Harvard, proposed conducting “adaptive” trials. One of the ideas he suggested was a randomized “play the winner” algorithm—a version of Win-Stay, Lose-Shift, in which the chance of using a given treatment is increased by each win and decreased by each loss.
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More generally, our intuitions about rationality are too often informed by exploitation rather than exploration. When we talk about decision-making, we usually focus just on the immediate payoff of a single decision—and if you treat every decision as if it were your last, then indeed only exploitation makes sense. But over a lifetime, you’re going to make a lot of decisions. And it’s actually rational to emphasize exploration—the new rather than the best, the exciting rather than the safe, the random rather than the considered—for many of those choices, particularly earlier in life. What we take to be the caprice of children may be wiser than we know.
3. Sorting: Making Order
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As long as the two stacks were themselves sorted, the procedure of merging them into a single sorted stack was incredibly straightforward and took linear time: simply compare the two top cards to each other, move the smaller of them to the new stack you’re creating, and repeat until finished. The program that John von Neumann wrote in 1945 to demonstrate the power of the stored-program computer took the idea of collating to its beautiful and ultimate conclusion. Sorting two cards is simple: just put the smaller one on top. And given a pair of two-card stacks, both of them sorted, you can easily collate them into an ordered stack of four. Repeating this trick a few times, you’d build bigger and bigger stacks, each one of them already sorted. Soon enough, you could collate yourself a perfectly sorted full deck—with a final climactic merge, like a riffle shuffle’s order-creating twin, producing the desired result. This approach is known today as Mergesort,
Highlight(pink) – Page 68 · Location 1233
If you’re still strategizing about that bookshelf, the Mergesort solution would be to order a pizza and invite over a few friends. Divide the books evenly, and have each person sort their own stack. Then pair people up and have them merge their stacks. Repeat this process until there are just two stacks left, and merge them one last time onto the shelf.
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In fact, March Madness is not a complete Mergesort—
Highlight(pink) – Page 78 · Location 1418
But in fact it isn’t Bubble Sort that emerges as the single best algorithm in the face of a noisy comparator. The winner of that particular honor is an algorithm called Comparison Counting Sort. In this algorithm, each item is compared to all the others, generating a tally of how many items it is bigger than. This number can then be used directly as the item’s rank. Since it compares all pairs, Comparison Counting Sort is a quadratic-time algorithm, like Bubble Sort.
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Comparison Counting Sort operates exactly like a Round-Robin tournament. In other words, it strongly resembles a sports team’s regular season—playing every other team in the division and building up a win-loss record by which they are ranked.
Highlight(pink) – Page 79 · Location 1426
The Mergesort postseason is chancy, but the Comparison Counting regular season is not; championship rings aren’t robust, but divisional standings are literally as robust as it gets.
5. Scheduling: First Things First
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The board depicted every machine in the shop, showing the task currently being carried out by that machine and all the tasks waiting for it. This practice would be built upon by Taylor’s colleague Henry Gantt, who in the 1910s developed the Gantt charts that would help organize many of the twentieth century’s most ambitious construction projects,
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Thus you can keep the total amount of time spent doing laundry to the absolute minimum. Johnson’s analysis had yielded scheduling’s first optimal algorithm: start with the lightest wash, end with the smallest hamper.
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The comedian Mitch Hedberg recounts a time when “I was at a casino, I was minding my own business, and this guy came up and said, ‘You’re gonna have to move, you’re blocking the fire exit.’ As though if there was a fire, I wasn’t gonna run.” The bouncer’s argument was priority inversion; Hedberg’s rebuttal was priority inheritance. Hedberg lounging casually in front of a fleeing mob puts his low-priority loitering ahead of their high-priority running for their lives—but not if he inherits their priority. And an onrushing mob has a way of making one inherit their priority rather quickly. As Hedberg explains, “If you’re flammable and have legs, you are never blocking a fire exit.”
6. Bayes’s Rule: Predicting the Future
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Normal distributions tend to have a single appropriate scale: a one-digit life span is considered tragic, a three-digit one
extraordinary.
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These are also known as “scale-free distributions” because they characterize quantities that can plausibly range over many scales: a town can have tens, hundreds, thousands, tens of thousands, hundreds of thousands, or millions of residents, so we can’t pin down a single value for how big a “normal” town should be.
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Examining the Copernican Principle, we saw that when Bayes’s Rule is given an uninformative prior, it always predicts that the total life span of an object will be exactly double its current age. In fact, the uninformative prior, with its wildly varying possible scales—the wall that might last for months or for millennia—is a power-law
distribution. And for any power-law distribution, Bayes’s Rule indicates that the appropriate prediction strategy is a Multiplicative Rule: multiply the quantity observed so far by some constant factor. For an uninformative prior, that constant factor happens to be 2, hence the Copernican prediction; in other power-law cases, the multiplier will depend on the exact distribution you’re working with. For the grosses of movies, for instance, it happens to be about 1.4. Highlight(pink) – Page 140 · Location 2595
When we apply Bayes’s Rule with a normal distribution as a prior, on the other hand, we obtain a very different kind of guidance. Instead of a multiplicative rule, we get an Average Rule: use the
distribution’s “natural” average—its single, specific scale—as your guide. For instance, if somebody is younger than the average life span, then simply predict the average; as their age gets close to and then exceeds the average, predict that they’ll live a few years more. Highlight(pink) – Page 141 · Location 2608
Between those two extremes, there’s actually a third category of things in life: those that are neither more nor less likely to end just because they’ve gone on for a while. Sometimes things are simply … invariant. The Danish mathematician Agner Krarup Erlang, who studied such phenomena, formalized the spread of intervals between independent events into the function that now carries his name: the Erlang distribution.
Highlight(pink) – Page 141 · Location 2619
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. The familiar refrain of “Just five more minutes!… [five minutes later] Five more minutes!” that so often characterizes human claims regarding, say, one’s readiness to leave the house or office, or the time until the completion of some task, may seem indicative of some chronic failure to make realistic estimates. Well, in the cases where one’s up against an Erlang distribution, anyway, that refrain happens to be correct.
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When he was in graduate school, Tom, along with MIT’s Josh Tenenbaum, ran an experiment asking people to make predictions for a variety of everyday quantities—such as human life spans, the grosses of movies, and the time that US representatives would spend in office—based on just one piece of information in each case: current age, money earned so far, and years served to date. Then they compared the predictions people made to the predictions given by applying Bayes’s Rule to the actual real-world data across each of those domains. As it turned out, the predictions that people had made were extremely close to those produced by Bayes’s Rule. Intuitively, people made different types of predictions for quantities that followed different
distributions—power-law, normal, and Erlang—in the real world. In other words, while you might not know or consciously remember which situation calls for the Multiplicative, Average, or Additive Rule, the predictions you make every day tend to implicitly reflect the different cases where these distributions appear in everyday life, and the different ways they behave.
Highlight(pink) – Page 144 · Location 2672
Small data is big data in disguise. 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.
Highlight(pink) – Page 146 · Location 2708
Decades after the original marshmallow experiments, Walter Mischel and his colleagues went back and looked at how the participants were faring in life. Astonishingly, they found that children who had waited for two treats grew into young adults who were more successful than the others, even measured by quantitative metrics like their SAT scores. If the marshmallow test is about willpower, this is a powerful testament to the impact that learning self-control can have on one’s life.
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If 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. Counterintuitively, that might mean turning off the news.
7. Overfitting: When to Think Less
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Overfitting, for instance, explains the irony of our palates. How can it be that the foods that taste best to us are broadly considered to be bad for our health, when the entire function of taste buds, evolutionarily speaking, is to prevent us from eating things that are bad? The answer is that taste is our body’s proxy metric for health. Fat, sugar, and salt are important nutrients, and for a couple hundred thousand years, being drawn to foods containing them was a reasonable measure for a sustaining diet. But being able to modify the foods available to us broke that relationship.
Highlight(pink) – Page 157 · Location 2860
Beware: when you go to the gym to work off the extra weight from all that sugar, you can also risk overfitting fitness. Certain visible signs of fitness—low body fat and high muscle mass, for example—are easy to measure, and they are related to, say, minimizing the risk of heart disease and other ailments. But they, too, are an imperfect proxy measure.
Highlight(pink) – Page 157 · Location 2872
Perhaps nowhere, however, is overfitting as powerful and troublesome as in the world of business. “Incentive structures work,” as Steve Jobs put it. “So you have to be very careful of what you incent people to do, because various incentive structures create all sorts of consequences that you can’t anticipate.”
Highlight(pink) – Page 158 · Location 2892
But when overfitting creeps in, it can prove disastrous. There are stories of police officers who find themselves, for instance, taking time out during a gunfight to put their spent casings in their pockets—good etiquette on a firing range.
Highlight(pink) – Page 158 · Location 2898
Mistakes like these are known in law enforcement and the military as “training scars,” and they reflect the fact that it’s possible to overfit one’s own preparation.
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The same kind of process is also believed to play a role at the neural level. In computer science, software models based on the brain, known as “artificial neural networks,” can learn arbitrarily complex functions—they’re even more flexible than our nine-factor model above—but precisely because of this very flexibility they are notoriously vulnerable to overfitting. Actual, biological neural networks sidestep some of this problem because they need to trade off their performance against the costs of maintaining it. Neuroscientists have suggested, for instance, that brains try to minimize the number of neurons that are firing at any given moment—implementing the same kind of downward pressure on complexity as the Lasso.
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If you happen to know the expected mean and expected variance of a set of investments, then use mean-variance portfolio optimization—the optimal algorithm is optimal for a reason. But when the odds of estimating them all correctly are low, and the weight that the model puts on those untrustworthy quantities is high, then an alarm should be going off in the decision-making process: it’s time to regularize. Inspired by examples like Markowitz’s retirement savings,
psychologists Gerd Gigerenzer and Henry Brighton have argued that the decision-making shortcuts people use in the real world are in many cases exactly the kind of thinking that makes for good decisions. “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.” Highlight(pink) – Page 167 · Location 3060
As entrepreneurs Jason Fried and David Heinemeier Hansson explain, the further ahead they need to brainstorm, the thicker the pen they use—a clever form of simplification by stroke size:
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The upshot of Early Stopping is that sometimes it’s not a matter of choosing between being rational and going with our first instinct. Going with our first instinct can be the rational solution. The more complex, unstable, and uncertain the decision, the more rational an approach that is.
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Darwin made up his mind exactly when his notes reached the bottom of the diary sheet. He was regularizing to the page. This is reminiscent of both Early Stopping and the Lasso: anything that doesn’t make the page doesn’t make the decision.
8. Relaxation: Let It Slide
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One of the simplest forms of relaxation in computer science is known as Constraint Relaxation. In this technique, researchers remove some of the problem’s constraints and set about solving the problem they wish they had.
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For instance, you can relax the traveling salesman problem by letting the salesman visit the same town more than once, and letting him retrace his steps for free. Finding the shortest route under these looser rules produces what’s called the “minimum spanning tree.” Highlight(pink) – Page 180 · Location 3292
Occasionally it takes a bit of diplomatic finesse, but a Lagrangian Relaxation—where some impossibilities are downgraded to penalties, the inconceivable to the undesirable—enables progress to be made. Highlight(pink) – Page 180 · Location 3304
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 (or breaking them and accepting the consequences).
9. Randomness: When to Leave It to Chance
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Consider the lobster stuck in the lobster trap: poor beast, he doesn’t realize that exiting the cage means backtracking to the cage’s center, that he needs to go deeper into the cage to make it out. A lobster trap is nothing other than a local maximum made of wire—a local maximum that kills.
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One approach is to augment Hill Climbing with what’s known as “jitter”: if it looks like you’re stuck, mix things up a little. Make a few random small changes (even if they are for the worse), then go back to Hill Climbing; see if you end up at a higher peak.
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But there’s also a third approach: instead of turning to full-bore randomness when you’re stuck, use a little bit of randomness every time you make a decision. This technique, developed by the same Los Alamos team that came up with the Monte Carlo Method, is called the Metropolis Algorithm. The Metropolis Algorithm is like Hill Climbing, trying out different small-scale tweaks on a solution, but with one important difference: at any given point, it will potentially accept bad tweaks as well as good ones.
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How much randomness should you use? And when? And—given that strategies such as the Metropolis Algorithm can permute our itinerary pretty much ad infinitum—how do you ever know that you’re done? Highlight(pink) – Page 199 · Location 3644
Taking the ten-city vacation problem from above, we could start at a “high temperature” by picking our starting itinerary entirely at random, plucking one out of the whole space of possible solutions regardless of price. Then we can start to slowly “cool down” our search by rolling a die whenever we are considering a tweak to the city sequence. Taking a superior variation always makes sense, but we would only take inferior ones when the die shows, say, a 2 or more. After a while, we’d cool it further by only taking a higher-price change if the die shows a 3 or greater—then 4, then 5. Eventually we’d be mostly hill climbing, making the inferior move just occasionally when the die shows a 6. Finally we’d start going only uphill, and stop when we reached the next local max. This approach, called Simulated Annealing, seemed like an intriguing way to map physics onto problem solving.
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Luria realized that if he bred several generations of different lineages of bacteria, then exposed the last generation to a virus, one of two radically different things would happen. If resistance was a response to the virus, he’d expect roughly the same amount of resistant bacteria to appear in every one of his bacterial cultures, regardless of their lineage. On the other hand, if resistance emerged from chance mutations, he’d expect to see something a lot more uneven—just like a slot machine’s payouts. That is, bacteria from most lineages would show no resistance at all; some lineages would have a single “grandchild” culture that had mutated to become resistant; and on rare occasions, if the proper mutation had happened several generations up the “family tree,” there would be a jackpot: all the “grandchildren” in the lineage would be resistant.
10. Networking: How We Connect
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The cell phone began with a boast—Motorola’s Martin Cooper walking down Sixth Avenue on April 3, 1973, as Manhattan pedestrians gawked, calling his rival Joel Engel at AT& T:
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The technology that ate circuit switching’s lunch would become known as packet switching.
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Deciding once and for all that she’d finally had enough and giving up entirely on the relationship seemed arbitrary and severe, but continuing to persist in perpetual rescheduling seemed naïve, liable to lead to an endless amount of disappointment and wasted time. Solution: Exponential Backoff on the invitation rate. Try to reschedule in a week, then two, then four, then eight. The rate of “retransmission” goes toward zero—yet you never have to completely give up.
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Fundamentally, buffers use delay—known in networking as “latency”—in order to maximize throughput. That is, they cause packets (or customers) to wait, to take advantage of later periods when things are slow. But a buffer that’s operating permanently full gives you the worst of both worlds: all the latency and none of the give.
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The most prevalent critique of modern communications is that we are “always connected.” But the problem isn’t that we’re always connected; we’re not. The problem is that we’re always buffered. The difference is enormous.
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Vacation email autoresponders explicitly tell senders to expect latency; a better one might instead tell senders to expect Tail Drop. 11. Game Theory: The Minds of Others
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For a share of stock to be sold at, say, $ 60, the buyer must believe he can sell it later for $ 70—to someone who believes he can sell it for $ 80 to someone who believes he can sell it for $ 90 to someone who believes he can sell it for $ 100 to someone else. In this way, the value of a stock isn’t what people think it’s worth but what people think people think it’s worth.
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As Alan Turing proved in 1936, a computer program can never tell you for sure whether another program might end up calculating forever without end—except by simulating the operation of that program and thus potentially going off the deep end itself.
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“In poker, you never play your hand,” James Bond says in Casino Royale; “you play the man across from you.” In fact, what you really play is a theoretically infinite recursion. There’s your own hand and the hand you believe your opponent to have; then the hand you believe your opponent believes you have, and the hand you believe your opponent believes you to believe he has … and on it goes.
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(Luring an opponent into fruitless recursion can be an effective strategy in other games, too. One of the most colorful, bizarre, and fascinating episodes in the history of man-vs.-machine chess came in a 2008 blitz showdown between American grandmaster Hikaru Nakamura and leading computer chess program Rybka.
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In rock-paper-scissors, for example, the equilibrium tells us, perhaps unexcitingly, to choose one of the eponymous hand gestures completely at random, each roughly a third of the time. What makes this equilibrium stable is that, once both players adopt this 1⁄3-1⁄3-1⁄3 strategy, there is nothing better for either to do than stick with it. (If we tried playing, say, more rock, our opponent would quickly notice and start playing more paper, which would make us play more scissors, and so forth until we both settled into the 1⁄3-1⁄3-1⁄3 equilibrium again.) 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.
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The counterintuitive and powerful thing here is we can worsen every outcome—death on the one hand, taxes on the other—yet make everyone’s lives better by shifting the equilibrium.
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On the other hand, a change to the game’s payoffs that doesn’t change the equilibrium will typically have a much smaller effect than desired. The CEO of the software firm Evernote, Phil Libin, made headlines with a policy of offering Evernote employees a thousand dollars cash for taking a vacation. 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. If a million-dollar heist ends up with both thieves in jail, so does a ten-million-dollar heist. 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 all. A thousand bucks sweetens the deal but doesn’t change the principle of the game—which is to take as much vacation as possible while still being perceived as slightly more loyal than the next guy or gal, therefore getting a raise or promotion over them that’s worth many thousands of dollars.
Conclusion: Computational Kindness
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* * * There’s a certain paradox the two of us observed when it came to scheduling the interviews that went into this book. 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.” At first this seems absurd, like the celebrated studies where people on average donate more money to save the life of one penguin than eight thousand penguins, or report being more worried about dying in an act of terrorism than about dying from any cause, terrorism included. In the case of interviews, it seems that people preferred receiving a constrained problem, even if the constraints were plucked out of thin air, than a wide-open one. It was seemingly less difficult for them to accommodate our preferences and constraints than to compute a better option based on their own. Highlight(pink) – Page 256 · Location 4752
One of the implicit principles of computer science, as odd as it may sound, is that computation is bad: the underlying directive of any good algorithm is to minimize the labor of thought. When we interact with other people, we present them with computational problems—not just explicit requests and demands, but implicit challenges such as interpreting our intentions, our beliefs, and our preferences. It stands to reason, therefore, that a computational understanding of such problems casts light on the nature of human interaction. We can be “computationally kind” to others by framing issues in terms that make the underlying computational problem easier. This matters because many problems—especially social ones, as we’ve seen—are intrinsically and inextricably hard. Consider this all-too-common scenario. A group of friends are standing around, trying to figure out where to go for dinner. Each of them clearly has some preferences, albeit potentially weak ones. But none of them wants to state those preferences explicitly, so they politely navigate the social hazards with guesses and half-hints instead.
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In such situations, computational kindness and conventional etiquette diverge. Politely withholding your preferences puts the computational problem of inferring them on the rest of the group. In contrast, politely asserting your preferences (“ Personally, I’m inclined toward x. What do you think?”) helps shoulder the cognitive load of moving the group toward resolution.

iPad Notebook export for Algorithms to Live By: The Computer Science of Human Decisions

May 31, 2020

Notebook Export
Algorithms to Live By: The Computer Science of Human Decisions Christian, Brian; Griffiths, Tom
Citation (APA): Christian, B., & Griffiths, T. (2016). Algorithms to Live By: The Computer Science of Human Decisions [Kindle iOS version]. Retrieved from Amazon.com

iPhone Notebook export for Human Errors: A Panorama of Our Glitches, from Pointless Bones to Broken Genes

May 8, 2020

Human Errors: A Panorama of Our Glitches, from Pointless Bones to Broken Genes Lents, Nathan H.
Citation (APA): Lents, N. H. (2018). Human Errors: A Panorama of Our Glitches, from Pointless Bones to Broken Genes

Low-density parity-check code – Wikipedia

February 17, 2020

https://en.wikipedia.org/wiki/Low-density_parity-check_code

turbo codes

QT:{{”
2G cell phones used “soft decoding” (i.e., probabilities) but not belief propagation. 3G cell phones used Berrou’s turbo codes, and 4G phones used Gallager’s turbo-like codes.
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[from book!]

Human brain samples yield a genomic trove | Science

December 15, 2018

The papers are out!
Using the tag pecrollout for this.
http://science.sciencemag.org/content/362/6420/1227

QT: {{”
The project’s namesake, ENCODE (Encyclopedia of DNA Elements), was a broader quest to map noncoding regions of the human genome. Its initial results, unveiled in 2012, stirred controversy. Scientists disputed the team’s claim that most of the genome was functional and questioned whether the project’s insights would be worth NIH’s $185 million investment (Science, 21 March 2014, p. 1306).
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Human brain samples yield a genomic trove | Science

December 14, 2018

The papers are out!
Using the tag pecrollout for this.
http://science.sciencemag.org/content/362/6420/1227

QT: {{”
The project’s namesake, ENCODE (Encyclopedia of DNA Elements), was a broader quest to map noncoding regions of the human genome. Its initial results, unveiled in 2012, stirred controversy. Scientists disputed the team’s claim that most of the genome was functional and questioned whether the project’s insights would be worth NIH’s $185 million investment (Science, 21 March 2014, p. 1306).
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Why “Many-Model Thinkers” Make Better Decisions

November 23, 2018

Why “Many-Model Thinkers” Make Better Decisions
https://HBR.org/2018/11/why-many-model-thinkers-make-better-decisions Intuitive description of #MachineLearning concepts. Focuses on practical business contexts (eg hiring) & explains how #ensemble models & boosting can make better choices

QT:{{”
“The agent based model is not necessarily better. It’s value comes from focusing attention where the standard model does not.

The second guideline borrows the concept of boosting, …Rather than look for trees that predict with high accuracy in isolation, boosting looks for trees that perform well when the forest of current trees does not.

A boosting approach would take data from all past decisions and see where the first model failed. …The idea of boosting is to go searching for models that do best specifically when your other models fail.

To give a second example, several firms I have visited have hired computer scientists to apply techniques from artificial intelligence to identify past hiring mistakes. This is boosting in its purest form. Rather than try to use AI to simply beat their current hiring model, they use AI to build a second model that complements their current hiring model. They look for where their current model fails and build new models to complement it.”
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