I had read most of this book with a fair degree of equanimity – finding some faults, but also a lot of good information in it. Then I’m jarred out of complacency by a sudden shot from nowhere, in which he says that David Hume, one of the greatest philosophers of the 18th century, is simply too ‘daft to understand’ probabilistic arguments. Without any introduction to the subject, he claims Hume is stuck in some ‘skeptical shell’ that prevents him from understanding the simple, elegant solutions of Bayes. Silver rejects much ideology taught with statistical method in colleges and universities today, specifically the “frequentist” approach of Ronald Fisher, originator of many classical statistical tests and methods. The problem Silver finds is a belief in perfect experimental, survey, or other designs, when data often comes from a variety of sources and idealized modeling assumptions rarely hold true. Often such models reduce complex questions to overly simple “hypothesis tests” using arbitrary “significance levels” to “accept or reject” a single parameter value. It sounds like science fiction to imagine being able to switch genes on and off using light but this already happens in nature.
Noise is anything that requires you to invest your time and attention, but yields a negative return on that investment. Anything where you walk out knowing that you just wasted a few grains in the finite hourglass of your life that did not contribute to you achieving your professional or personal ambitions.
Every morning, sunlight triggers cells to start making proteins that wake you from your sleep. Scientists looked at the sequences of these proteins and found the code for the switch. Researchers can insert the switch code next to new genes and use light to turn them on and off to study their functions. Genomics is predicted to become the biggest source of data in the world, producing more digital information than astronomy, particle physics or even YouTube. By 2025 around 1 billion people will have had their genomes sequenced helping researchers find out more about how genetic mutations are linked to health and disease.
I had hoped that the book would draw on the author’s experience and give an insight into how to apply this idea in the real world. There are lots of examples and stories (sometimes amusing; I liked the Chess story in Chapter 9), but the stories lead the reader to few insights. I’ll wind up with a brief mention of an aspect of Silver’s thinking that I found more interesting than anything else. That is his interest in, and application of, Bayesian reasoning or inference. Silver is quite obviously much taken with this, and he does a good job of explaining it. He doesn’t really introduce it until his chapter on gambling, where he shows how it can be used to make probabilistic forecasts using several interesting (non-gambling) examples. In almost every chapter following this he refers to the way that Bayesian reasoning can be used to strengthen forecasting and to overcome some of the difficulties of predicting in that area.
The Signal And The Noise : Why So Many Predictions
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. Coming to a better understanding of data and statistics is essential to help us navigate our lives. But as with most emerging technologies, the widespread benefits to science, industry, and human welfare will come only after the hype has died down. One of the centerpieces of Silver’s approach to prediction is based upon Bayesian reasoning, developed in the 18th century by mathematician and Reverend Thomas Bayes. He argued that any probability can be interpreted only in the context of a baseline that preceded the measurement.
His predictive powers were such that at one point the Obama campaign turned to him for guidance. The first thing to note about The Signal and the Noise is that it is modest – not lacking in confidence or pointlessly self-effacing, but calm and honest about the limits to what the author or anyone else can know about what is going to happen next. You’ll recognize the 2008 US election, the large earthquakes, especially in Japan, swine flu, both the one in the 70s and the more recent epidemic, economic meltdowns, 911, Pearl Harbour, stock market fluctuations, and much more. Throughout these stories, we learn about what the predictions were and why they failed or succeeded. Nate gives advice on how the predictions can be improved in these particular incidents, but gives the reader advice on how to create accurate predictions in similar situations.
Rather, it is a work in progress, always subject to further refinement and testing. The 1970s were the high point for “vast amounts of theory applied to extremely small amounts of data,” as Paul Krugman put it. We had begun to use computers to produce models of the world, but it took us some time to recognize how crude and assumption-laden they were, and that the precision that computers trading strategy were capable of was no substitute for predictive accuracy. Foxes sometimes have more trouble fitting into cultures like television, business, and politics. They are quicker to recognize how noisy the data can be, and they are less inclined to chase false signals. onary instincts sometimes lead us to see patterns when there are none there, trying to see a solution in random noise.
If A Genetic Code Needs To Be Improved, Who Would Make A Better Editor: A Human, A Machine, Or Would We Need A Hybrid?
Silver first gained public recognition for developing PECOTA, a system for forecasting the performance and career development of Major League Baseball players, which he sold to and then managed for Baseball Prospectus from 2003 to 2009. Nate Silver is great a explaining things and illustrating them with compelling stories. But what I was not expecting was the extent to which The Signal and the Noise embodies a philosophy of living. Silver is a proponent of thinking probabilistically, which means making predictions and decisions based on the most likely outcome, given the data you have. Sometimes probabilities are effectively 100% (will the sun rise again today?), but often they are not (will a hurricane hit? does my opponent have a better poker hand than me?). Reading Nate Silver is like exhaling after holding your breath for a really long time. And while you could find plenty of other people calling it for Romney or Obama, they are for the most part just talking heads that don’t actually care about reality.
I think this illustrates his discussion on the difference between likelihood and probability. Be careful what predictions you trust, most of them will be wrong a good portion of the time.
This is a book about competition, free markets, and the evolution of ideas. This is a book about the things that make us smarter than any computer, and a book about human error. This is a book about how we learn, one step at a time, to come to knowledge of the objective world, and why we sometimes take a step back. Predictions are the signal and the noise more likely successful when there is ample data on which to make them. Weather, poker, and political campaigns fall into this category. Earthquakes, while their frequency is reasonably predictable over a very long time frame, are extremely difficult to predict in a specific location and time frame relevant to preventing disaster.
In 1976, the Secretary of Health, Education, and Welfare, F. David Matthews, predicted that possibly a million Americans would die. President Ford authorized manufacture of 200 million doses of swine flu vaccine, and widespread vaccination began that next fall. In the interim, no other fatality from swine flu was seen in the United States. In Silver’s view, the accuracy of predictions is heavily dependent upon how willing the observer is to modify initial beliefs based upon newly emerging information.
Books By Nate Silver
It is worth a read to understand the limitations of predictions – that is areas where predictions can be made successfully and where it does not have much chance of succeeding. Such statements give odds on outcomes, including a 17% chance of Romney winning the electoral college. The shares of the popular vote similarly are ranges including outcomes in which Romney gets the most votes. What is highly probable is that the voting shares are in these ranges, but not whose share is highest; that’s another probability question with closer odds.
- If 2 out of 3 scenarios had Hillary winning, then 1 out of 3 scenarios had Trump winning.
- I watched the election closely (as well as 538′s forecasts) and they were quite correct.
- Not only naive people have biases, everybody as human beings have their biases too.
- The noise is the random, unwanted variation or fluctuation that interferes with the signal.
- In contrast, weather forecasters use multiple models, constantly update their data, and express their predictions in ranges of probability.
- With everything from the health of the global economy to our ability to fight terrorism dependent on the quality of our predictions, Nate Silver’s insights are an essential read.
He is a contributor to The New York Times Magazine and has appeared as a commentator on CNN and MSNBC. He has spoken at TED and SXSW, and was named one of TIME’s 100 Most Influential People in the world. In 2007, writing under the pseudonym “Poblano”, Silver began to publish analyses and predictions related to the 2008 United States presidential election.
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. This “explains” the observed fluctuations in the business cycle in a mathematical sense, but it should be obvious that it doesn’t actually explain anything. It’s no coincidence that such methods are completely useless in producing policy-relevant forecasts, even as their fans are quite adept at continually fitting new events into the model.
The self-aggrandizing by Silver of his own skill at Poker, political forecasting, sports betting etc, seems to belie his own understanding of Bayesian theory and eur at times reach nauseating levels. I don’t care to know his own personal income from limit poker or his player tracking system used by baseball prospectus.
There are so many interesting topics covered in this book such as the prediction of weather , prediction of earthquakes , the economics of poker sites , and hurricane landfall predictions . It also has the most intelligent discussion of global warming that I have encountered. His blog/podcast, ‘fivethirtyeight’, is quite popular, featuring talks about polls, forecasting, data, and predictions about sports, and politics, forex and was even carried by the NYT at one point. However, after reading this book, I think I will keep a closer eye on his website. The accuracy of weather predictions increases slowly but steadily, year by year. Ensembles of computer model runs are part of the story, but human judgment add value, and increases the accuracy. Weather forecasts issued by the National Weather Service are unbiased in a probabilistic sense.