The book is intended to appeal to a broad audience, and I believe it succeeds in that regard, which means that from a personal standpoint, I would have preferred more math. Nonetheless, I believe that most people’s lives would be improved and the world would be a better place the more folks understood the lessons Silver is trying to impart. I recommend this book to a broad audience, although as is often the case, those to whom it seems most appealing are probably the people who benefit least from reading it.

What he found is that predicting terrorist attacks is a lot like predicting earthquakes that follow the power law distribution discussed earlier. If you live in an area and experience a couple of magnitude five earthquakes and then a year later a magnitude six, we know from earthquake forecasting that a serious earthquake is coming. There were many signs of a possible attack on Pearl Harbor beforehand, but Schelling explains that people most likely realized that the United States rarely ever gets attacked so therefore Hawaii was very unlikely to be attacked.

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Until now, he took aim mostly at sports pundits and political handicappers. But the book hints at his ambitions to take on weightier questions. There’s no better example of this than his chapter on climate change. In recent years, the most sophisticated global-warming skeptics have seized on errors in the forecasts of the United Nations’ International Panel on Climate Change (I.P.C.C.) in order to undermine efforts at greenhouse gas reduction. These skeptics note that global temperatures have increased at only about half the rate the I.P.C.C. predicted in 1990, and that they flatlined in the 2000s (albeit after rising sharply in the late ’90s). In 2002, while toiling away as a lowly consultant for the accounting firm KPMG, he hatched a revolutionary method for predicting the performance of baseball players, which the Web site Baseball Prospectus subsequently acquired. The following year, he took up poker in his spare time and quit his job after winning $15,000 in six months.

The initial segment is an examination of all the ways that predictions go wrong. The second part is about how applying Bayes Theorem can make expectations correct. Bayes’ theorem describes the probability of an event, based on prior knowledge of conditions that might be related to the event. Throughout the book, Silver makes many practical recommendations that reveal his practitioner’s perspective on forecasting. In fact, I found that there are entire disciplines in which our analysis has failed to produce much progress, at least as measured by our ability to make reliable predictions.

He states that it is important to take a fox-like approach to forecasting, especially when dealing with qualitative data. Here you can increase the accuracy of your prediction by taking into account the qualitative data such as the candidate’s demeanor, likeability, or reputation in the community. The main point is that you cannot always rely on quantitative data to make good predictions. The willingness to change your prediction will only result in more accurate predictions.

Readers will finish the book with a solid conceptual understanding of how these methods are used in various fields. What you won’t take away is detailed knowledge of how the actual prediction process works. One of the challenges in anyone’s life, especially in one’s job, is figuring out what’s really going on. In the final predictions for the 2016 presidential general election, Silver’s model showed roughly a 70 percent likelihood of Hillary Clinton winning the election with Donald Trump winning the other 30 percent.

Somewhat counterintuitively, modern-day macroeconomists know a great deal about math and rather less about the economy (it’s hard, see above). But humility ill-suits the desire to publish exciting papers and get ahead. So a high premium is placed on what amount to sophisticated data-mining techniques. You can build elaborate models showing that past recessions can be accounted for by “shocks” to technology or people’s desire to work hard.

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This is useful because it now allows seismologists to forecast the amount of large earthquakes based on the amount of smaller ones. “For every increase of one point in magnitude, an earthquake becomes about ten times less frequent” . The main flaw with this law is that it gives a large time frame for when the events will actually occur. An example of a forecast would be there is a 50% chance an earthquake will happen in Japan in the next 50 years. This law states that the frequency of earthquakes versus their magnitude follows the power law distribution, meaning that if the frequency decreases, the magnitude exponentially increases.

• He believes there should be markets where you can bet on economic forecasts.
• There were again many signs that an attack might be imminent, but an attack of that scale had never happened on US soil and was seen as nearly impossible.
• Silver also describes the competition between the public and private weather forecasting operations in order to determine which forecasting system is better.
• For earthquakes, computers aren’t very helpful because we don’t understand the laws that earthquakes abide by.
• Nate Silver shows that the people who are most sure are the ones that make the most exceedingly bad predictions.
• During the Great Moderation there were only two mild recessions, therefore the economic data from that time painted a bright picture.

If there are a large number of models that are all predicting a much different outcome than your model, there might be something wrong. There are always outliers, but for the most part a group of people is more accurate than an individual.

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It dropped to No. 20 in the second week, before rising to No. 13 in the third, and remaining on the non-fiction hardback top 15 list for the following thirteen weeks, with a highest weekly ranking of No. 4. The book’s already strong sales soared right after election night, November 6, jumping 800% and becoming the second best seller on Amazon.com. bama aside, the indubitable hero of the 2012 US presidential election was the statistician and political forecaster Nate Silver. His blog, FiveThirtyEight.com, syndicated by the New York Times since 2010, correctly predicted the results of the election in 50 out of 50 states. When the worldwide media was universally proclaiming the race too close to call and the pundits were deriding mathematical models, FiveThirtyEight.com steadily argued that the odds made clear that Obama would win. On election day, Silver’s final forecast was that Obama had a 90.9% chance of winning. The exercise is not so different in spirit from the way public intellectuals like John Kenneth Galbraith once shaped discussions of economic policy and public figures like Walter Cronkite helped sway opinion on the Vietnam War.

In a survey of climate scientists, 94% believed climate change was occurring and 84% believed it is a result of human activity. Silver also discusses the importance of recognizing what the consensus is. He explains that whenever he makes a prediction he looks at the consensus and the farther away from it his prediction is, the more evidence he needs to be comfortable with it. If you stray far away from the consensus, you need to have good reason to or else you’ll end up like the many noise traders. Instead, Silver believes that the market cannot be completely efficient but that it takes novice traders with less skill to create a marketplace where skilled individuals can prevail. His theory is that you need variance in the skill set of traders within the market in order for the most skilled ones to have a chance at beating the market.

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Japanese officials built the Fukushima nuclear reactor to withstand an earthquake of magnitude 8.6. They over fitted the data, showing that the chance of a magnitude 9 earthquake was once every 13,000 years. Following the Gutenberg-Richter law, the chance was actually once every 300 years. forex It is still a small chance, but Japanese officials might have built a stronger reactor had they employed this data set. Silver also talks about the problem of over fitting in earthquake prediction. Susan Hough describes the “Holy Grail” of seismology as a time-dependent forecast.

In addition he interviews many esteemed names in the world of forecasting in order to get their ideas on prediction. Other interesting points raised by this chapter are the ideas of judging predictions by accuracy, honesty and economic value. Honesty in this sense means “is this the best model I can make at this point? The interesting thing about weather forecasts in the US is that the National Weather Service makes the results of simulations available to all. Big national value-adders https://astroturfmats.com/the-research-driven-investor-by-hayes-timothy/ such as the Weather Channel produce a “wet bias” forecast which systematically overstates the possibility of rain for lower values of the chances of rain. This is because customers prefer to be told that there is, say, a 20% chance of rain when it actually turns out to be dry, than be told the actual chance of rain (say 5%) and for it to rain. There are several chapters on scientific prediction, looking at the predictions of earthquakes, the weather, climate and disease.

Within each sector, Silver explains how predictions are typically made and what the resulting pros and cons are. Lessons in Corporate Finance: A Case Studies Approach to Financial He will very often offer up his own interpretation because Silver himself claims to be a great forecaster.

The Signal And The Noise By Nate Silver

Medical experts believed that the chance of one million Americans dying was between 2% and 35%. Ford chose to ignore these low prediction numbers, which was catastrophic for him because this fiasco played a part in Ford losing his bid for re-election to Jimmy Carter.

Silver says, “You should make the best forecast possible today, regardless of what you said last week, last month, or last year” . In searching for why these so-called expert’s predictions were no better than a coin flip, Silver came across a paper by Philip Tetlock, a professor of political science and psychology.