Nate Silver Quotes

Distinguishing the signal from the noise requires both scientific knowledge and self-knowledge: the serenity to accept the things we cannot predict, the courage to predict the things we can, and the wisdom to know the difference.

The signal is the truth. The noise is what distracts us from the truth.

In America, everyone is entitled to an opinion, and it is certainly useful to have a few when a pollster shows up. But these are opinions of a quite different roder from eighteenth- or nineteenth-century opinions. It is probably more accurate to call them emotions rather than opinions, which would account for the fact that they change from week to week, as the pollsters tell us. What is happening here is that television is altering the meaning of 'being informed' by creating a species of information that might properly be called disinformation. I am using this world almost in the precise sense in which it is used by spies in the CIA or KGB. Disinformation does not mean false information. It means misleading information--misplace, irrelevant, fragmented or superficial information--information that creates the illusion of knowing something but which in fact leads one away from knowing. In saying this, I do not mean to imply that television news deliberately aims to deprive Americans of a coherent, contextual understanding of their world. I mean to say that when news is packaged as entertainment, that is the inevitable result. And in saying that the television news show entertains but does not inform, I am saying something far more serious than that we are being deprived of authentic information. I am saying we are losing our sense of what it means to be well informed. Ignorance is always correctable. But what shall we do if we take ignorance to be knowledge?

The irony is that by being less focused on your results, you may achieve better ones.

Nate handed it over the expert, and Waschbär examined it in turn, finally concluding with a low whistle. "No, not a diamond. It's a star." "A star?" the fairies chorused. "Yer cryin' th' stars from yer eyes." Nate's hands on the reins tightened as he added, "Fer Ariel.

We need to stop, and admit it: we have a prediction problem. We love to predict things—and we aren’t very good at it.

One of the pervasive risks that we face in the information age, as I wrote in the introduction, is that even if the amount of knowledge in the world is increasing, the gap between what we know and what we think we know may be widening.

Most of you will have heard the maxim "correlation does not imply causation." Just because two variables have a statistical relationship with each other does not mean that one is responsible for the other. For instance, ice cream sales and forest fires are correlated because both occur more often in the summer heat. But there is no causation; you don't light a patch of the Montana brush on fire when you buy a pint of Haagan-Dazs.

If political scientists couldn’t predict the downfall of the Soviet Union—perhaps the most important event in the latter half of the twentieth century—then what exactly were they good for?

Shakespeare's plays often turn on the idea of fate, as much drama does. What makes them so tragic is the gap between what his characters might like to accomplish and what fate provides them.

Voting, we might even say, is the next to last refuge of the politically impotent. The last refuge is, of course, giving your opinion to a pollster, who will get a version of it through a desiccated question, and then will submerge it in a Niagara of similar opinions, and convert them into--what else?--another piece of news. Thus we have here a great loop of impotence: The news elicits from you a variety of opinions about which you can do nothing except to offer them as more news, about which you can do nothing.

Wherever there is human judgment there is the potential for bias.

Data Is Useless Without Context

Money doesn't make you happy... but it sure doesn't make you said either!" - Nate Temple

The more interviews that an expert had done with the press, Tetlock found, the worse his predictions tended to be.

Under Bayes' theorem, no theory is perfect. Rather, it is a work in progress, always subject to further refinement and testing.

When catastrophe strikes, we look for a signal in the noise - anything that might explain the chaos that we see all around us and bring order to the world again.

The story the data tells us is often the one we’d like to hear, and we usually make sure that it has a happy ending.

Economy is not baseball, where the game is always played by the same rules.

As the statistician George E. P. Box wrote, "All models are wrong, but some models are useful." What he meant by that is that all models are simplifications of the universe, as they must necessarily be. As another mathematician said, "The best model of a cat is a cat." ... The key is in remembering that a model is a tool to help us understand the complexities of the universe, and never a substitute for the universe itself.

I spotted a shiny item on the ground, and like all girls, squirrels, and toddlers, I made a move to grab it.

It was a gift of mine. Annoying people to immeasurable levels of hatred.

Finding patterns is easy in any kind of data-rich environment; that’s what mediocre gamblers do. The key is in determining whether the patterns represent noise or signal.

As the political prediction machine Nate Silver of 538.com tweeted in 2012, “If a place has sidewalks, it votes Democratic.

Good innovators typically think very big and they think very small. New ideas are sometimes found in the most granular details of a problem where few others bother to look. And they are sometimes found when you are doing your most abstract and philosophical thinking, considering why the world is the way that it is and whether there might be an alternative to the dominant paradigm. Rarely can they be found in the temperate latitudes between they two spaces, where we spend 99 percent of our lives.

Who needs theory when you have so much information? But this is categorically the wrong attitude to take toward forecasting, especially in a field like economics where the data is so noisy.

For Popper, a hypothesis was not scientific unless it was falsifiable—meaning that it could be tested in the real world by means of a prediction.

If there is a mutual distrust between the weather forecaster and the public, the public may not listen when they need to most.

There are 1,013,900 words in the English language, and none of them accurately portray how badly I want to hit you over the head with a chair. A heavy Amish chair.” Gavin…

In science, progress is possible. In fact, if one believes in Bayes' theorem, scientific progress is inevitable as predictions are made and as beliefs are tested and refined.

When they play against each other, the game usually comes down to who can find his opponent’s blind spots first.

As John Maynard Keynes said, “The market can stay irrational longer than you can stay solvent.

Absolutely nothing useful is realized when one person who holds that there is a 0 percent probability of something argues against another person who holds that the probability is 100 percent.

When we advance more confident claims and they fail to come to fruition, this constitutes much more powerful evidence against our hypothesis. We can't really blame anyone for losing faith when this occurs

We face danger whenever information growth outpaces our understanding of how to process it. The last forty years of human history imply that it can still take a long time to translate information into useful knowledge, and that if we are not careful, we may take a step back in the meantime.

What makes him successful is the way that he analyzes information. He is not just hunting for patterns. Instead, Bob combines his knowledge of statistics with his knowledge of basketball in order to identify meaningful relationships in the data.

But forecasters often resist considering these out-of-sample problems. When we expand our sample to include events further apart from us in time and space, it often means that we will encounter cases in which the relationships we are studying did not hold up as well as we are accustomed to. The model will seem to be less powerful. It will look less impressive in a PowerPoint presentation (or a journal article or a blog post). We will be forced to acknowledge that we know less about the world than we thought we did. Our personal and professional incentives almost always discourage us from doing this.

It’s never good when you blindly follow some belief system or group of people without consciously deducing whether what they do is right or wrong. The number one test is to wonder what would happen if you openly, but respectfully, questioned your commander’s decision when you think it’s wrong. If the answer in any way resembles punishment, pain, or ridicule, rather than an explanation, you are probably not working for the good guys.” Gavin

We are undoubtedly living with many delusions that we do not even realize.

You stupid thundercunt. I’m a Beast Master. I own shifters. And you are going to pay very dearly for that oversight.

The litmus test for whether you are a competent forecaster is if more information makes your predictions better.

All models are wrong, but some models are useful.”90 What he meant by that is that all models are simplifications of the universe, as they must necessarily be.

Human beings have an extraordinary capacity to ignore risks that threaten their livelihood, as though this will make them go away.

A forecaster who says he doesn’t care about the science is like the cook who says he doesn’t care about food.

After adjusting for inflation, a $10,000 investment made in a home in 1896 would be worth just $10,600 in 1996.

The leap is into the Bayesian way of thinking about prediction and probability.

Nothing is written in stone.” “The Ten Commandments are. And I think we both know that neither of us would pull off an A on that grading scale.

How can we apply our judgment to the data—without succumbing to our biases?

What a well-designed forecasting system can do is sort out which statistics are relatively more susceptible to luck; batting average, for instance, is more erratic than home runs.

We may, without even realizing it, work backward to generate persuasive-sounding theories that rationalize them, and these will often fool our friends and colleagues as well as ourselves.

On Rachel's show for November 7, 2012: Ohio really did go to President Obama last night. and he really did win. And he really was born in Hawaii. And he really is legitimately President of the United States, again. And the Bureau of Labor statistics did not make up a fake unemployment rate last month. And the congressional research service really can find no evidence that cutting taxes on rich people grows the economy. And the polls were not screwed to over-sample Democrats. And Nate Silver was not making up fake projections about the election to make conservatives feel bad; Nate Silver was doing math. And climate change is real. And rape really does cause pregnancy, sometimes. And evolution is a thing. And Benghazi was an attack on us, it was not a scandal by us. And nobody is taking away anyone's guns. And taxes have not gone up. And the deficit is dropping, actually. And Saddam Hussein did not have weapons of mass destruction. And the moon landing was real. And FEMA is not building concentration camps. And you and election observers are not taking over Texas. And moderate reforms of the regulations on the insurance industry and the financial services industry in this country are not the same thing as communism. Listen, last night was a good night for liberals and for democrats for very obvious reasons, but it was also, possibly, a good night for this country as a whole. Because in this country, we have a two-party system in government. And the idea is supposed to be that the two sides both come up with ways to confront and fix the real problems facing our country. They both propose possible solutions to our real problems. And we debate between those possible solutions. And by the process of debate, we pick the best idea. That competition between good ideas from both sides about real problems in the real country should result in our country having better choices, better options, than if only one side is really working on the hard stuff. And if the Republican Party and the conservative movement and the conservative media is stuck in a vacuum-sealed door-locked spin cycle of telling each other what makes them feel good and denying the factual, lived truth of the world, then we are all deprived as a nation of the constructive debate about competing feasible ideas about real problems. Last night the Republicans got shellacked, and they had no idea it was coming. And we saw them in real time, in real humiliating time, not believe it, even as it was happening to them. And unless they are going to secede, they are going to have to pop the factual bubble they have been so happy living inside if they do not want to get shellacked again, and that will be a painful process for them, but it will be good for the whole country, left, right, and center. You guys, we're counting on you. Wake up. There are real problems in the world. There are real, knowable facts in the world. Let's accept those and talk about how we might approach our problems differently. Let's move on from there. If the Republican Party and the conservative movement and conservative media are forced to do that by the humiliation they were dealt last night, we will all be better off as a nation. And in that spirit, congratulations, everyone!

If you compare the number of children who are diagnosed as autistic64 to the frequency with which the term autism has been used in American newspapers,65 you’ll find that there is an almost perfect one-to-one correspondence (figure 7-4), with both having increased markedly in recent years.

The key to making a good forecast, as we observed in chapter 2, is not in limiting yourself to quantitative information. Rather, it’s having a good process for weighing the information appropriately.

Paperback and buckram books decorated the floor, torn open to leave loose pages lying about like the useless guts of an eviscerated animal. Fury smoldered deep down inside my stomach, as if I were looking at heaps of dead children lying about the room instead. Books were my children. It was sacrilegious. Hundreds

War is wild, but one must know when to be wild, and when to keep calm. Too cold, and you become calloused, brittle, and breakable. Too hot, and your emotions rule you, stealing your control. But to be strategically ruthless? Analytically wild? In a General, these qualities are priceless. For everything else, there’s soldiers,

A forecaster should almost never ignore data, especially when she is studying rare events like recessions or presidential elections, about which there isn’t very much data to begin with. Ignoring data is often a tip-off that the forecaster is overconfident, or is overfitting her model—that she is interested in showing off rather than trying to be accurate.

Making predictions based on our beliefs is the best (and perhaps even the only) way to test ourselves. If objectivity is the concern for a greater truth beyond our personal circumstances, and prediction is the best way to examine how closely aligned our personal perceptions are with that greater truth, the most objective among us are those who make the most accurate predictions.

The alchemy that the ratings agencies performed was to spin uncertainty into what looked and felt like risk. They took highly novel securities, subject to an enormous amount of systemic uncertainty, and claimed the ability to quantify just how risky they were. Not only that, but of all possible conclusions, they came to the astounding one that these investments were almost risk-free.

Quite a lot of evidence suggests that aggregate or group forecasts are more accurate than individual ones, often somewhere between 15 and 20 percent more accurate depending on the discipline. That doesn’t necessarily mean the group forecasts are good. (We’ll

The most calamitous failures of prediction usually have a lot in common. We focus on those signals that tell a story about the world as we would like it to be, not how it really is. We ignore the risks that are hardest to measure, even when they pose the greatest threats to our well-being. We make approximations and assumptions about the world that are much cruder than we realize. We abhor uncertainty, even when it is an irreducible part of the problem we are trying to solve.

Love is precious, and shouldn’t be wasted on every passing whim, or it will mean nothing by the time you truly wish to share it with someone who matters.” I said softly. Silvers, Shayne (2012-10-08). Obsidian Son: A Novel In The Nate Temple Supernatural Thriller Series (The Temple Chronicles Book 1) (p. 194). Argento Publishing. Kindle Edition.

Where our enemies will strike us is predictable: it's where we least expect them to.

For the duration of another Big Bang. For the single flutter of a Hummingbird’s wings. For no time at all. Because

By playing games you can artificially speed up your learning curve to develop the right kind of thought processes

The market can stay irrational longer than you can stay solvent.

In 2011, he said that Donald Trump would run for the Republican nomination—and had a “damn good” chance of winning it.19

The alchemy that the ratings agencies performed was to spin uncertainty into what looked and felt like risk.

In complex systems, however, mistakes are not measured in degrees but in whole orders of magnitude.

sometimes the only solution when the data is very noisy—is to focus more on process than on results.

It’s hard enough to know where the economy is going. But it’s much, much harder if you don’t know where it is to begin with.

If you hold there is a 100 percent probability that God exists, or a 0 percent probability, then under Bayes’s theorem, no amount of evidence could persuade you otherwise.

RANDOM-WALK AND ACTUAL STOCK-MARKET CHARTS

An economic model conditioned on the notion that nothing major will change is a useless one.

Things like Google search traffic patterns, for instance, can serve as leading indicators for economic data series like unemployment.

We should neither worship at the altar of technology nor be frightened by it.

it is not really “artificial” intelligence if a human designed the artifice.

in the 1990s, economists predicted only 2 of the 60 recessions around the world a year ahead of time.

There was “nothing new under the sun,” as the beautiful Bible verses in Ecclesiastes put it—not so much because everything had been discovered but because everything would be forgotten.

John P. Ioannidis published a controversial paper titled “Why Most Published Research Findings Are False.”39 The paper studied positive findings documented in peer-reviewed journals: descriptions of successful predictions of medical hypotheses carried out in laboratory experiments. It concluded that most of these findings were likely to fail when applied in the real world. Bayer Laboratories recently confirmed Ioannidis’s hypothesis. They could not replicate about two-thirds of the positive findings claimed in medical journals when they attempted the experiments themselves.40

It turns out that the economic forecasts produced by the White House, for instance, have historically been among the least accurate of all,69 regardless of whether it’s a Democrat or a Republican in charge.)

Meanwhile, exposure to so many new ideas was producing mass confusion. The amount of information was increasing much more rapidly than our understanding of what to do with it, or our ability to differentiate the useful information from the mistruths. Paradoxically, the result of having so much more shared knowledge was increasing isolation along national and religious lines. The instinctual shortcut that we take when we have “too much information” is to engage with it selectively, picking out the parts we like and ignoring the remainder, making allies with those who have made the same choices and enemies of the rest.

Do I need to check up on you guys later? You know the rules.No sleeping in opposite-sex rooms." My face flames,and St. Clair's cheeks grow blotchy. It's true.It's a rule. One that my brain-my rule-loving, rule-abiding brain-conveniently blocked last night. It's also one notoriously ignored by the staff. "No,Nate," we say. He shakes his shaved head and goes back in his apartment. But the door opens quickly again,and a handful of something is thrown at us before it's slammed back shut. Condoms.Oh my God, how humiliating. St. Clair's entire face is now bright red as he picks the tiny silver squares off the floor and stuffs them into his coat pockets. We don't speak,don't even look at each other,as we climb the stairs to my floor. My pulse quickens with each step.Will he follow me to my room,or has Nate ruined any chance of that? We reach the landing,and St. Clair scratches his head. "Er..." "So..." "I'm going to get dressed for bed. Is that all right?" His voice is serious,and he watches my reaction carefully. "Yeah.Me too.I'm going to...get ready for bed,too." "See you in a minute?" I swell with relief. "Up there or down here?" "Trust me,you don't want to sleep in my bed." He laughs,and I have to turn my face away,because I do,holy crap do I ever. But I know what he means.It's true my bed is cleaner. I hurry to my room and throw on the strawberry pajamas and an Atlanta Film Festival shirt. It's not like I plan on seducing him. Like I'd even know how. St. Clair knocks a few minutes later, and he's wearing his white bottoms with the blue stripes again and a black T-shirt with a logo I recognize as the French band he was listening to earlier. I'm having trouble breathing. "Room service," he says. My mind goes...blank. "Ha ha," I say weakly. He smiles and turns off the light. We climb into bed,and it's absolutely positively completely awkward. As usual. I roll over to my edge of the bed. Both of us are stiff and straight, careful not to touch the other person. I must be a masochist to keep putting myself in these situations. I need help. I need to see a shrink or be locked in a padded cell or straitjacketed or something. After what feels like an eternity,St. Clair exhales loudly and shifts. His leg bumps into mine, and I flinch. "Sorry," he says. "It's okay." "..." "..." "Anna?" "Yeah?" "Thanks for letting me sleep here again. Last night..." The pressure inside my chest is torturous. What? What what what? "I haven't slept that well in ages." The room is silent.After a moment, I roll back over. I slowly, slowly stretch out my leg until my foot brushes his ankle. His intake of breath is sharp. And then I smile,because I know he can't see my expression through the darkness.

Some of you may be uncomfortable with a premise that I have been hinting at and will now state explicitly: we can never make perfectly objective predictions. They will always be tainted by our subjective point of view.

It is much easier after the event to sort the relevant from the irrelevant signals. After the event, of course, a signal is always crystal clear; we can now see what disaster it was signaling, since the disaster has occurred. But before the event it is obscure and pregnant with conflicting meanings. It comes to the observer embedded in an atmosphere of “noise,” i.e., in the company of all sorts of information that is useless and irrelevant for predicting the particular disaster.

Successful gamblers—and successful forecasters of any kind—do not think of the future in terms of no-lose bets, unimpeachable theories, and infinitely precise measurements. These are the illusions of the sucker, the sirens of his overconfidence.

Political experts had difficulty anticipating the USSR’s collapse, Tetlock found, because a prediction that not only forecast the regime’s demise but also understood the reasons for it required different strands of argument to be woven together. There was nothing inherently contradictory about these ideas, but they tended to emanate from people on different sides of the political spectrum,11 and scholars firmly entrenched in one ideological camp were unlikely to have embraced them both.

There is a “water level” established by the competition and your profit will be like the tip of an iceberg: a small sliver of competitive advantage floating just above the surface, but concealing a vast bulwark of effort that went in to support it.

Risk, as first articulated by the economist Frank H. Knight in 1921,45 is something that you can put a price on. Say that you’ll win a poker hand unless your opponent draws to an inside straight: the chances of that happening are exactly 1 chance in 11.46 This is risk. It is not pleasant when you take a “bad beat” in poker, but at least you know the odds of it and can account for it ahead of time. In the long run, you’ll make a profit from your opponents making desperate draws with insufficient odds. Uncertainty, on the other hand, is risk that is hard to measure. You might have some vague awareness of the demons lurking out there. You might even be acutely concerned about them. But you have no real idea how many of them there are or when they might strike. Your back-of-the-envelope estimate might be off by a factor of 100 or by a factor of 1,000; there is no good way to know. This is uncertainty. Risk greases the wheels of a free-market economy; uncertainty grinds them to a halt.

Johannes Gutenberg’s invention in 1440 made information available to the masses, and the explosion of ideas it produced had unintended consequences and unpredictable effects. It was a spark for the Industrial Revolution in 1775,1 a tipping point in which civilization suddenly went from having made almost no scientific or economic progress for most of its existence to the exponential rates of growth and change that are familiar to us today. It set in motion the events that would produce the European Enlightenment and the founding of the American Republic. But the printing press would first produce something else: hundreds of years of holy war. As mankind came to believe it could predict its fate and choose its destiny, the bloodiest epoch in human history followed.2

for-profit weather forecasters rarely predict exactly a 50 percent chance of rain, which might seem wishy-washy and indecisive to consumers.41 Instead, they’ll flip a coin and round up to 60, or down to 40, even though this makes the forecasts both less accurate and

expected during the Long Boom years. In the year after the stimulus package was passed in 2009, for instance, GDP was growing fast enough to create about two million jobs according to Okun’s law.41 Instead, an additional 3.5 million jobs were lost during the period.

Most statistical models are built on the notion that there are independent variables and dependent variables, inputs and outputs, and they can be kept pretty much separate from one another.39 When it comes to the economy, they are all lumped together in one hot mess.

Nowadays, in a fast-paced world awash in numbers and statistics, those same tendencies can get us into trouble: when presented with a series of random numbers, we see patterns where there aren’t any. (Advertisers and politicians, possessed of modern guile, often prey on the primordial parts of our brain.)

I was losing—a lot: about $75,000 during the last few months of 2006, most of it in one horrible evening. I played through the first several months of 2007 and continued to lose—another $60,000 or so. At that point, no longer confident that I could beat the games, I cashed out the rest of my money and quit.

An admonition like "The more complex you make the model, the worse the forecast gets." is equivalent to saying "Never add too much salt to the recipe." How much complexity-how much salt-did you begin with? If you want to get good at forecasting, you'll need to immerse yourself in the craft and trust your own taste buds.

Wait!” I called out. “Did she just say Fook and Yu?” I asked incredulously. Achilles was now sobbing with laughter. I pointed at the blue sapphire dragons. “No, I think this is really important, guys. Their names are Fook and Yu? I really need to know the answer to this. I’m not concerned about the necklace. This is way more important.

An old Cherokee man was teaching his grandson about life. “A fight is going on inside me,” he told the boy. “It is a terrible fight between two wolves. One is evil — he is anger, envy, sorrow, regret, greed, arrogance, self-pity, guilt, resentment, inferiority, lies, false pride, superiority, and ego.” After a few moments to make sure he had the boy’s undivided attention, he continued. “The other wolf is good — he is joy, peace, love, hope, serenity, humility, kindness, benevolence, empathy, generosity, truth, compassion, and faith. The same fight is going on inside of you, boy, and inside of every other person, too.” The grandson thought about this for a few minutes before replying. “Which wolf will win?” The old Cherokee man simply said, “The one you feed, boy. The one you feed…

The Weather Service was initially organized under the Department of War by President Ulysses S. Grant, who authorized it in 1870. This was partly because President Grant was convinced that only a culture of military discipline could produce the requisite accuracy in forecasting25 and partly because the whole enterprise was so hopeless that it was only worth bothering with during wartime when you would try almost anything to get an edge.

An American home has not, historically speaking, been a lucrative investment. In fact, according to an index developed by Robert Shiller and his colleague Karl Case, the market price of an American home has barely increased at all over the long run. After adjusting for inflation, a $10,000 investment made in a home in 1896 would be worth just $10,600 in 1996. The rate of return had been less in a century than the stock market typically produces in a single year.

Although Israel is targeted by terrorists much more frequently than the United States, Israelis do not live in fear of terrorism. A 2012 survey of Israeli Jews found that only 16 percent described terrorism as their greatest fear81—no more than the number who said they were worried about Israel’s education system. No Israeli politician would say outright that he tolerates small-scale terrorism, but that’s essentially what the country does. It tolerates it because the alternative—having everyone be paralyzed by fear—is incapacitating and in line with the terrorists’ goals. A key element in the country’s strategy is making life as normal as possible for people after an attack occurs. For instance, police typically try to clear the scene of an attack within four hours of a bomb going off,82 letting everyone get back to work, errands, or even leisure. Small-scale terrorism is treated more like crime than an existential threat. What Israel certainly does not tolerate is the potential for large-scale terrorism (as might be made more likely, for instance, by one of their neighbors acquiring weapons of mass destruction). There is some evidence that their approach is successful: Israel is the one country that has been able to bend Clauset’s curve. If we plot the fatality tolls from terrorist incidents in Israel using the power-law method (figure 13-8), we find that there have been significantly fewer large-scale terror attacks than the power-law would predict; no incident since 1979 has killed more than two hundred people. The fact that Israel’s power-law graph looks so distinct is evidence that our strategic choices do make some difference.